Advances in Swarm Intelligence: 12th International Conference, ICSI 2021, Qingdao, China, July 17–21, 2021, Proceedings, Part I (Lecture Notes in Computer Science, 12689) [1st ed. 2021] 3030787427, 9783030787424
his two-volume set LNCS 12689-12690 constitutes the refereed proceedings of the 12th International Conference on Advance
468 63 35MB
English Pages 606 [589] Year 2021
Preface
Organization
Contents – Part I
Contents – Part II
Swarm Intelligence and Nature-Inspired Computing
Swarm Unit Digital Control System Simulation
1 Introduction
2 Features of Von Neumann Computer Control
3 Semi-Markov Model of DC Operation
4 Example of Control System Analysis
5 Conclusion
References
Natural Emergence of Heterogeneous Strategies in Artificially Intelligent Competitive Teams
1 Introduction
2 Related Work
2.1 Multi-agent Reinforcement Learning
2.2 Multi-agent Environments
3 Method
3.1 Modeling Observation of Opponents
3.2 Modeling Interactions with Teammates
3.3 Policy
3.4 Scalability and Real World Applicability
3.5 Training
4 Environment
4.1 Observation Space
4.2 Action Space
4.3 Reward Structure
5 Results
5.1 Evolution of Strategies
5.2 Being Attentive
5.3 Ensemble Strategies
6 Conclusions
References
Analysis of Security Problems in Groups of Intelligent Sensors
1 Introduction
2 Analysis of Information Security Problems of the Internet of Things as a Group with Swarm Intelligence
3 Exploitation Vulnerabilities
4 Internet of Things and Lightweight Cryptography
5 The Tiny Encryption Algorithm Cipher Description
6 Speck Cipher Description
7 Experimental Research of Encryption Algorithms
8 Conclusion
References
Optimization of a High-Lift Mechanism Motion Generation Synthesis Using MHS
1 Introduction
2 High-Lift Mechanism
3 Optimization Problem and Constraint Handling
4 The Design Results
5 Conclusion and Discussion
References
Liminal Tones: Swarm Aesthetics and Materiality in Sound Art
1 Introduction
2 Methods
2.1 Concept
2.2 Model
3 Results
4 Discussion and Future Works
4.1 Order and Chaos – Sound as Emergence
4.2 Future Works
References
Study on the Random Factor of Firefly Algorithm
1 Introduction
2 Review of FA
2.1 Algorithm Idea
2.2 Model Analysis of FA
3 Research on the Random Factor Control Method of FA
3.1 Problem Analysis
3.2 New Control Method for the Random Factor
4 Experimental Results
4.1 Experiment Design
4.2 Results Analysis
4.3 Comparison with Other Decreasing Control Methods
4.4 Performance Comparison with Other Swarm Intelligence Algorithms
5 Conclusion
References
Metaheuristic Optimization on Tensor-Type Solution via Swarm Intelligence and Its Application in the Profit Optimization in Designing Selling Scheme
1 Introduction
2 A Brief Review of PSO and SIB
3 Method and Implementation
4 Applications
5 Conclusion
References
An Improved Dragonfly Algorithm Based on Angle Modulation Mechanism for Solving 0–1 Knapsack Problems
1 Introduction
2 Dragonfly Algorithm
3 Improved Angle Modulated Dragonfly Algorithm (IAMDA)
3.1 AMDA
3.2 IAMDA
4 Experimental Results and Discussion
5 Conclusions
References
A Novel Physarum-Based Optimization Algorithm for Shortest Path
1 Introduction
2 Classical Physarum Algorithm
2.1 Basic Notations
2.2 Physarum Algorithm
3 Novel Physarum-Based Algorithm
3.1 Method 1: Quickly Removing Redundant Edges
3.2 Method 2: Merging Redundant Nodes
4 Computational Experiments
5 Conclusions
References
Traveling Salesman Problem via Swarm Intelligence
1 Introduction
2 The Swarm Intelligence Based Method for Solving TSP
2.1 Initialization Step
2.2 Iteration Step
3 Performance of SIB for TSP
3.1 Implementations
3.2 Results
3.3 Discussion
4 Conclusion
References
Swarm-Based Computing Algorithms for Optimization
Lion Swarm Optimization by Reinforcement Pattern Search
1 Introduction
2 Modified Lion Swarm Optimization
2.1 Lion Swarm Optimization
2.2 Problems with LSO
2.3 Modifications to LSO
3 Reinforcement Pattern Search
3.1 Pattern Search
3.2 Q-learning
3.3 Reinforcement Pattern Search Algorithm
4 Lion Swarm Optimization by Reinforcement Pattern Search
5 Experiment and Analysis
5.1 Experimental Setup
5.2 Comparison of Experimental Results
6 Conclusion
References
Fuzzy Clustering Algorithm Based on Improved Lion Swarm Optimization Algorithm
1 Introduction
2 The Basic Theory
2.1 Fuzzy C-Mean Clustering
2.2 Lion Swarm Optimization Algorithm
2.3 Sin Cos Algorithm
2.4 Elite Opposition-Based Learning
3 The Improved Lion Swarm Optimization Algorithm
3.1 The Improved Lion Swarm Optimization Algorithm
3.2 Algorithm Process
3.3 Performance Comparison and Analysis
4 Experimental Results and Analysis
4.1 FCM Algorithm Based on ILSO Algorithm
4.2 Database
4.3 Experimental Results and Analysis
5 Conclusion
References
Sparrow Search Algorithm for Solving Flexible Jobshop Scheduling Problem
1 Introduction
2 Problem Formulation
2.1 T-FJSP and P-FJSP
2.2 The Relationship Between JSP and FJSP
2.3 Symbol Definition and Description
2.4 The Mathematical Model of FJSP
3 Sparrow Search Algorithm
3.1 Biological Basis
3.2 Algorithm Description of SSA
4 Validation and Comparison
5 Conclusion
References
Performance Analysis of Evolutionary Computation Based on Tianchi Service Scheduling Problem
1 Introduction
2 Tianchi Service Scheduling Problem
3 Proposed Solution
4 Experimental Evaluation
5 Discussions
6 Conclusion
References
An Intelligent Algorithm for AGV Scheduling in Intelligent Warehouses
1 Introduction
2 Related Work
3 Problem Description
4 A Hybrid Intelligent Algorithm for the Problem
4.1 Tabu Search for AGV Routing
4.2 WWO for Allocating Tasks to AGVs
5 Computational Experiments
6 Conclusion
References
Success-History Based Position Adaptation in Gaining-Sharing Knowledge Based Algorithm
1 Introduction
2 Gaining-Sharing Knowledge Based Algorithm
3 Proposed Adaptation
4 Experimental Results
4.1 Benchmark Functions and Experimental Setup
4.2 Numerical Results
5 Conclusions
References
Particle Swarm Optimization
Multi-guide Particle Swarm Optimisation Control Parameter Importance in High Dimensional Spaces
1 Introduction
2 Background
2.1 Multi-objective Optimization
2.2 Multi-guide Particle Swarm Optimization
2.3 Functional Analysis of Variance
3 Experimental Procedure
4 Results
4.1 Control Parameter Importance for Higher Dimensional Problems
4.2 Control Parameter Importance over Time
5 Conclusions
References
Research on the Latest Development of Particle Swarm Optimization Algorithm for Satellite Constellation
1 Introduction
2 Particle Swarm Optimization (PSO)
3 Binary Particle Swarm Optimization (BPSO)
4 Modified Binary Particle Swarm Optimization (MBPSO)
5 Hybrid-Resampling Particle Swarm Optimization (HRPSO)
6 Conclusions and Future Work
References
Polynomial Approximation Using Set-Based Particle Swarm Optimization
1 Introduction
2 Background
2.1 Polynomial Regression
2.2 Particle Swarm Optimization
2.3 Adaptive Coordinate Descent
3 Set-Based Particle Swarm Optimization Polynomial Regression
4 Empirical Process
4.1 Benchmark Problems
4.2 Tuning Algorithm Configurations
4.3 Performance Measures
5 Results
5.1 SBPSO and BPSO Results
5.2 Hybrid and PSO Results
6 Conclusions and Future Work
References
Optimizing Artificial Neural Network for Functions Approximation Using Particle Swarm Optimization
1 Introduction
2 Methodology
2.1 Building a Feed Forward ANN
2.2 Implementing the PSO Algorithm
3 Results and Analysis
3.1 Experimental Environment and Parameter Settings
3.2 Experimental Results
3.3 Limitations
4 Conclusion
References
Two Modified NichePSO Algorithms for Multimodal Optimization
1 Introduction
2 The NichePSO Algorithm
2.1 Cognitive Velocity Update
2.2 Social Velocity Update
2.3 Initialization
2.4 Partitioning Criteria
2.5 Merging Subswarms
3 The NichePSO-R Algorithm
4 The NichePSO-S Algorithm
5 Experimental Results
5.1 Results
6 Conclusions
References
VaCSO: A Multi-objective Collaborative Competition Particle Swarm Algorithm Based on Vector Angles
1 Introduction
2 The Proposed VaCSO
2.1 General Framework
2.2 Clustering Based on Indicators
2.3 Competitive Learning Based on Elite Archive Sets
2.4 Co-Evolution Based on the ``Three-Particle Competition'' Mechanism
3 Experimental Studies
3.1 Experimental Parameter Settings
3.2 Experimental Results and Analysis
4 Conclusion and Remark
References
The Experimental Analysis on Transfer Function of Binary Particle Swarm Optimization
1 Introduction
2 The Transfer Function of the BPSO
2.1 The Conventional BPSO
2.2 The Transfer Function of BPSO
2.3 The Analysis on the Velocity of BPSO
3 The Discussion of Transfer Function on BPSO
4 Experiment Test with 0/1 Knapsack Problem
4.1 The Description of MKP
4.2 Experiment and Analysis
5 Conclusion
References
Multi-stage COVID-19 Epidemic Modeling Based on PSO and SEIR
1 Introduction
2 SEIR Modeling of COVID-19
2.1 Data Sources
2.2 Modified SEIR Model of COVID-19
3 Parameter Estimation of the Model
3.1 The PSO Algorithm
3.2 Modified SEIR Model Optimized by PSO Algorithm
3.3 Parameter Estimation and Analysis
4 Conclusion
References
Particle Swarms Reformulated Towards a Unified and Flexible Framework
1 Introduction
1.1 Trajectory Difference Equation
1.2 Neighbourhood Topology
1.3 Other Features
2 Reformulated Particle Swarm Optimisation
3 Global Features
3.1 Initialisation
3.2 Termination Conditions
4 Individual Behaviour Features
4.1 Deterministic Features
4.2 Stochastic Features
5 Social Behaviour Features
5.1 Local Sociometry
5.2 Current Information Update
5.3 Memorised Information Update
6 Conclusions
References
Ant Colony Optimization
On One Bicriterion Discrete Optimization Problem and a Hybrid Ant Colony Algorithm for Its Approximate Solution
1 Introduction
2 Substantial Problem Statement
3 Formal Problem Statement
4 Hybrid Algorithm
5 Example Problem ``data-j8-p2-r4"
6 Test Results
7 Conclusions
References
Initializing Ant Colony Algorithms by Learning from the Difficult Problem’s Global Features
1 Introduction
2 Learning from the Global Features
2.1 The Global Features of Solution Space
2.2 Edge-Based Relative Frequency
3 Initializing GFL-ACO
4 Experimental Tests
5 Conclusion
References
An Ant Colony Optimization Based Approach for Binary Search
1 Introduction
2 Model of the System
3 Ant Colony Optimization based Binary Search
4 Case Study
4.1 Existence of a Search Space in Which the Key Value May Be Found
4.2 Non-existence of a Search Space
5 Experimental Results
6 Comparison Between ACOBS and Search Techniques
6.1 Comparison Between ACOBS and Binary Search
6.2 Comparison Between ACOBS and Sequential Search
6.3 Time Complexity of ACOBS and Other Search Techniques
7 Mathematical Model of the System
8 Computational Complexity
8.1 Successful Search Space
8.2 Unsuccessful Search Space
9 Conclusion
References
A Slime Mold Fractional-Order Ant Colony Optimization Algorithm for Travelling Salesman Problems
1 Introduction
2 Related Work
2.1 Ant Colony Optimization Algorithm
2.2 Fractional-Order Calculus
2.3 Slime Mold Model
3 Algorithm Description
3.1 Modified Slime Mold Model
3.2 SMFACO State Transition Probability
3.3 SMFACO Pheromone Updating Rule
4 Experiments
4.1 Experimental Settings
4.2 Experimental Results and Analysis
5 Conclusion
References
Ant Colony Optimization for K-Independent Average Traveling Salesman Problem
1 Introduction
2 Problem Description
2.1 K-Independent Average TSP
2.2 K-Independent Total TSP
3 K-Independent Average ACO
3.1 Overview
3.2 Simultaneous Movement of Ants
3.3 Heuristic with Degree of Possible Options
3.4 2-best-opt
3.5 Pheromone Update
4 Empirical Study
4.1 Parameters and Settings
4.2 Performance Evaluation of Heuristics
4.3 Performance Evaluation of KI-Average-ACO
5 Conclusions and Future Work
References
Differential Evolution
Inferring Small-Scale Maximum-Entropy Genetic Regulatory Networks by Using DE Algorithm
1 Introduction
2 Maximum-Entropy GRNs
3 Method
3.1 Problem Formulation
3.2 Strategy Selection: Probability Matching
3.3 Parameter Adaptation
3.4 Optimization Work Flow
4 Experimental Results
4.1 Simulation Studies
4.2 Real Data Analysis
5 Conclusion
References
Variable Fragments Evolution in Differential Evolution
1 Introduction
2 Differential Evolution
2.1 Generation of Initial Population
2.2 Mutation Operator
2.3 Crossover Operator
2.4 Selection Operator
3 Analysis of Crossover Operator in DE
4 Variable Fragments Evolution in DE
4.1 Variable Fragments Evolution
4.2 DE with Variable Fragment Evolution
5 Experimental Study
5.1 Experimental Setup
5.2 Results on Benchmark Functions
6 Conclusions
References
The Efficiency of Interactive Differential Evolution on Creation of ASMR Sounds
1 Introduction
2 Differential Evolution and Interactive Differential Evolution
2.1 Differential Evolution (DE)
2.2 Interactive Differential Evolution (IDE)
3 IDE Creating ASMR Sounds
4 Experimental Method
4.1 Search Experiment
4.2 Evaluation Experiment
5 Experimental Results
5.1 Result of Search Experiment
5.2 Result of Evaluation Experiment
6 Discussion
7 Conclusion
References
Genetic Algorithm and Evolutionary Computation
Genetic Algorithm Fitness Function Formulation for Test Data Generation with Maximum Statement Coverage
1 Introduction
2 Theoretical Background
2.1 Genetic Algorithm for Test Data Generation
2.2 Formulation of the Fitness Function for Maximum Statement Coverage
3 Research
4 Conclusion
References
A Genetic Algorithm-Based Ensemble Convolutional Neural Networks for Defect Recognition with Small-Scale Samples
1 Introduction
2 Proposed GA-Based Ensemble CNNs
2.1 Basic CNN Model
2.2 GA for Ensemble Weight Optimization
2.3 Application
3 Experimental Results
3.1 Experimental Setting
3.2 Experimental Results
4 Discussion
5 Conclusion and Future Work
References
Biased Random-Key Genetic Algorithm for Structure Learning
1 Introduction
2 Background
3 Bayesian Network Structure Learning
3.1 Problem Definition
3.2 Implementation Process
4 Experiment
4.1 Experimental Parameters
4.2 Experimental Results
4.3 Analysis
5 Conclusion
References
Fireworks Algorithms
Performance Analysis of the Fireworks Algorithm Versions
1 Introduction
2 Fireworks Algorithm
3 Deep Statistical Comparison
4 Fireworks Algorithms Evaluation
5 Conclusion
References
Using Population Migration and Mutation to Improve Loser-Out Tournament-Based Fireworks Algorithm
1 Introduction
2 Related Work
2.1 Explosion Operation
2.2 Selection Operation
2.3 Loser-Out Tournament-Based Fireworks Algorithm
3 The Proposed Algorithm
3.1 Biogeography-Based Optimization
3.2 ILoTFWA
4 Experiments
4.1 Experimental Settings
4.2 Experimental Results
5 Conclusion
References
Region Selection with Discrete Fireworks Algorithm for Person Re-identification
1 Introduction
2 Related Work
3 Method
3.1 A Subsection Sample
3.2 Region Selecting with Discrete Fireworks Algorithm
4 Experiment
5 Conclusion
References
Fireworks Harris Hawk Algorithm Based on Dynamic Competition Mechanism for Numerical Optimization
1 Introduction
2 Related Work
2.1 Harris Hawk Algorithm
2.2 The Explosive Operation of Fireworks Algorithm
3 Proposed Algorithm: DCFW-HHO
3.1 Dynamic Competition Mechanism
3.2 Fireworks Harris Hawk Algorithm based on Dynamic Competition Mechanism (DCFW-HHO)
4 Experiment and Result
4.1 Benchmark Functions and Parameter Setting
4.2 Numerical Experiment
4.3 Converging Curves of the Average Best Fitness
5 Conclusion
References
Enhancing Fireworks Algorithm in Local Adaptation and Global Collaboration
1 Introduction
2 Backgrounds
2.1 Problem Definition
2.2 Fireworks Algorithms
2.3 Related Works
3 Proposed Strategies
3.1 Adaptation
3.2 Restart
3.3 Collaboration
3.4 Experiments
4 Discussions
5 Conclusion
References
Brain Storm Optimization Algorithm
Multi-objective Brainstorming Optimization Algorithm Based on Adaptive Mutation Strategy
1 Introduction
2 The Basic Problem Description
2.1 Multi-objective Optimization Problem
2.2 Brainstorming Optimization Algorithm
3 Multi-objective Brainstorming Optimization Algorithm Based on Adaptive Mutation Strategy
3.1 Population Mutation
3.2 Adaptation of Learning Factors
4 Experiment
4.1 Testing Proplems
4.2 Parameter Setting
4.3 Experimental Results and Analysis
5 Conclusion
References
Brain Storm Optimization Algorithm Based on Formal Concept Analysis
1 Introduction
2 Related Work
2.1 Original Brain Storm Optimization Algorithm
2.2 Formal Concept Analysis
3 The Proposed Method
3.1 Framework of HBSO
3.2 Individual Similarity Analysis
3.3 Adaptively Determine the Number of Clusters
4 Experiments and Results
4.1 Parameter Settings and Test Functions
4.2 Result and Discussion
5 Conclusion
References
An Improved Brain Storm Optimization Algorithm Based on Maximum Likelihood Estimation
1 Introduction
2 Double Grouping
2.1 Interaction Structure of the Decision Variables
2.2 Double Grouping Strategy
3 Creating Strategy Based on Maximum Likelihood Estimation
4 Experimental Results and Analysis
4.1 Complex Offset Test Functions and Parameter Settings
4.2 Experimental Comparison
5 Conclusion
References
Bacterial Foraging Optimization Algorithm
Reorganized Bacterial Foraging Optimization Algorithm for Aircraft Maintenance Technician Scheduling Problem
1 Introduction
2 Model of Aircraft Maintenance Technician Scheduling Problem
2.1 Problem Description
2.2 Objective Function and Constraints
3 Reorganized Bacterial Foraging Optimization Algorithm
3.1 Structural Recombination
3.2 Information Transmission Mechanism
4 Experiments and Results
4.1 Encoding Scheme
4.2 Parameter Settings
4.3 Experimental Results
5 Conclusions and Future Directions
References
A Bacterial Foraging Optimization Algorithm Based on Normal Distribution for Crowdfunding Project Outcome Prediction
1 Introduction
2 Methodology
2.1 Light Gradient Boosting Machine
2.2 Bacterial Foraging Optimization
2.3 Enhanced LightGBM Framework with Improved BFO
3 Experiments and Results
3.1 Dataset
3.2 Experiment Settings
3.3 Experiment Results
4 Conclusion and Future Work
References
Bacterial Foraging Optimization with Leader Selection Strategy for Bi-objective Optimization
1 Introduction
2 Related Background
2.1 Multi-objective Optimization
2.2 Pareto Optimality
2.3 Bacterial Foraging Optimization
3 Bi-objective Bacterial Foraging Optimization
3.1 Leader Selection Strategy
3.2 Swarm Strategy
3.3 External Archive Control
4 Experiments and Results
4.1 Problems and Algorithm Settings
4.2 Results and Analysis
5 Conclusions and Feature Work
References
DNA Computing Methods
Stability and Hopf Bifurcation Analysis of DNA Molecular Oscillator System Based on DNA Strand Displacement
1 Introduction
2 Model Establishment
3 Positive Boundedness of Solution
4 Dynamic Analysis of Systems Without Time Delay
5 Dynamic Analysis of Systems with Time Delay
6 Hopf Bifurcation Direction
7 Numerical Simulation
8 Conclusion
References
Dynamic Behavior Analysis of DNA Subtraction Gate with Stochastic Disturbance and Time Delay
1 Introduction
2 Preparation
3 Existence and Uniqueness of the Positive Solution
4 Stationary Distribution and Ergodicity
5 Numerical Simulations
6 Conclusions
References
Modeling and Analysis of Nonlinear Dynamic System with Lévy Jump Based on Cargo Sorting DNA Robot
1 Introduction
2 Modeling of Nonlinear Biochemical Reaction System with Lévy Jump Based on Cargo Sorting DNA Robot
2.1 Modeling of Nonlinear DNA Robot Reaction System
2.2 Modeling of Nonlinear DNA Robot Reaction System with Lévy Jump
3 The Existence and Uniqueness of Positive Solutions
4 Sufficient Conditions for the End and Continuation of the Reaction
4.1 Sufficient Conditions for the End of the Reaction
4.2 Sufficient Conditions for Continuous Response
5 Positive Recursion of Reaction
6 Numerical Simulation
7 Conclusions
References
Stability and Hopf Bifurcation Analysis of Complex DNA Catalytic Reaction Network with Double Time Delays
1 Introduction
2 Modelling and Analysis of Complex DNA Catalytic Reaction Network System
2.1 System Modelling of DNA Catalytic Reaction Network Using Toehold
2.2 Double Time Delays System Modelling of DNA Catalytic Reaction Network
2.3 Model Simplification
3 Stability Analysis and Hopf Bifurcation of Complex DNA Catalytic Reaction Network System
4 Numerical Simulation
5 Conclusion
References
Author Index
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LNCS 12689
Ying Tan Yuhui Shi (Eds.)
Advances in Swarm Intelligence 12th International Conference, ICSI 2021 Qingdao, China, July 17–21, 2021 Proceedings, Part I
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
12689
More information about this subseries at http://www.springer.com/series/7407
Ying Tan Yuhui Shi (Eds.) •
Advances in Swarm Intelligence 12th International Conference, ICSI 2021 Qingdao, China, July 17–21, 2021 Proceedings, Part I
123
Editors Ying Tan Peking University Beijing, China
Yuhui Shi Southern University of Science and Technology Shenzhen, China
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-78742-4 ISBN 978-3-030-78743-1 (eBook) https://doi.org/10.1007/978-3-030-78743-1 LNCS Sublibrary: SL1 – Theoretical Computer Science and General Issues © Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
This book and its companion volume, comprising LNCS volumes 12689 and 12690, constitute the proceedings of The Twelfth International International Conference on Swarm Intelligence (ICSI 2021) held during July 17–21, 2021, in Qingdao, China, both on-site and online. The theme of ICSI 2021 was “Serving Life with Swarm Intelligence.” The conference provided an excellent opportunity for academics and practitioners to present and discuss the latest scientific results and methods, innovative ideas, and advantages in theories, technologies, and applications in swarm intelligence. The technical program covered a number of aspects of swarm intelligence and its related areas. ICSI 2021 was the twelfth international gathering for academics and researchers working on most aspects of swarm intelligence, following successful events in Serbia (ICSI 2020, virtually), Chiang Mai (ICSI 2019), Shanghai (ICSI 2018), Fukuoka (ICSI 2017), Bali (ICSI 2016), Beijing (ICSI-CCI 2015), Hefei (ICSI 2014), Harbin (ICSI 2013), Shenzhen (ICSI 2012), Chongqing (ICSI 2011), and Beijing (ICSI 2010), which provided a high-level academic forum for participants to disseminate their new research findings and discuss emerging areas of research. ICSI 2021 also created a stimulating environment for participants to interact and exchange information on future challenges and opportunities in the field of swarm intelligence research. Due to the ongoing COVID-19 pandemic, ICSI 2021 provided opportunities for both online and offline presentations. On the one hand, ICSI 2021 was held normally in Qingdao, China, but on the other hand, the ICSI 2021 technical team provided the ability for authors who were subject to restrictions on overseas travel to present their work through an interactive online platform or video replay. The presentations by accepted authors were made available to all registered attendees on-site and online. The host city of ICSI 2021, Qingdao (also spelled Tsingtao), is a major sub-provincial city in the eastern Shandong province, China. Located on the western shore of the Yellow Sea, Qingdao is a major nodal city on the 21st Century Maritime Silk Road arm of the Belt and Road Initiative that connects East Asia with Europe, and has the highest GDP of any city in the province. It had jurisdiction over seven districts and three county-level cities till 2019, and as of 2014 had a population of 9,046,200 with an urban population of 6,188,100. Lying across the Shandong Peninsula and looking out to the Yellow Sea to its south, Qingdao borders the prefectural cities of Yantai to the northeast, Weifang to the west, and Rizhao to the southwest. ICSI 2021 received 177 submissions and invited submissions from about 392 authors in 32 countries and regions (Algeria, Australia, Bangladesh, Belgium, Brazil, Bulgaria, Canada, China, Colombia, India, Italy, Japan, Jordan, Mexico, Nigeria, Peru, Portugal, Romania, Russia, Saudi Arabia, Serbia, Slovakia, South Africa, Spain, Sweden, Taiwan (China), Thailand, Turkey, United Arab Emirates, UK, USA, and Vietnam) across 6 continents (Asia, Europe, North America, South America, Africa, and Oceania). Each submission was reviewed by at least 2 reviewers, and had on
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Preface
average 2.5 reviewers. Based on rigorous reviews by the Program Committee members and additional reviewers, 104 high-quality papers were selected for publication in this proceedings, an acceptance rate of 58.76%. The papers are organized into 16 cohesive sections covering major topics of swarm intelligence research and its development and applications. On behalf of the Organizing Committee of ICSI 2021, we would like to express our sincere thanks to the International Association of Swarm and Evolutionary Intelligence (IASEI), which is the premier international scholarly society devoted to advancing the theories, algorithms, real-world applications, and developments of swarm intelligence and evolutionary intelligence. We would also like to thank Peking University, Southern University of Science and Technology, and Ocean University of China for their co-sponsorships, and the Computational Intelligence Laboratory of Peking University and IEEE Beijing Chapter for their technical co-sponsorships, as well as our supporters including the International Neural Network Society, World Federation on Soft Computing, International Journal of Intelligence Systems, MDPI’s journals Electronics and Mathematics, Beijing Xinghui Hi-Tech Co., and Springer Nature. We would also like to thank the members of the Advisory Committee for their guidance, the members of the Program Committee and additional reviewers for reviewing the papers, and the members of the Publication Committee for checking the accepted papers in a short period of time. We are particularly grateful to Springer for publishing the proceedings in the prestigious series of Lecture Notes in Computer Science. Moreover, we wish to express our heartfelt appreciation to the plenary speakers, session chairs, and student helpers. In addition, there are still many more colleagues, associates, friends, and supporters who helped us in immeasurable ways; we express our sincere gratitude to them all. Last but not the least, we would like to thank all the speakers, authors, and participants for their great contributions that made ICSI 2021 successful and all the hard work worthwhile. May 2021
Ying Tan Yuhui Shi
Organization
General Co-chairs Ying Tan Russell C. Eberhart
Peking University, China IUPUI, USA
Program Committee Chair Yuhui Shi
Southern University of Science and Technology, China
Advisory Committee Chairs Xingui He Gary G. Yen
Peking University, China Oklahoma State University, USA
Technical Committee Co-chairs Haibo He Kay Chen Tan Nikola Kasabov Ponnuthurai Nagaratnam Suganthan Xiaodong Li Hideyuki Takagi M. Middendorf Mengjie Zhang Qirong Tang Milan Tuba
University of Rhode Island, USA City University of Hong Kong, China Auckland University of Technology, New Zealand Nanyang Technological University, Singapore RMIT University, Australia Kyushu University, Japan University of Leipzig, Germany Victoria University of Wellington, New Zealand Tongji University, China Singidunum University, Serbia
Plenary Session Co-chairs Andreas Engelbrecht Chaoming Luo
University of Pretoria, South Africa University of Mississippi, USA
Invited Session Co-chairs Andres Iglesias Haibin Duan
University of Cantabria, Spain Beihang University, China
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Organization
Special Sessions Chairs Ben Niu Yan Pei
Shenzhen University, China University of Aizu, Japan
Tutorial Co-chairs Junqi Zhang Shi Cheng
Tongji University, China Shanxi Normal University, China
Publications Co-chairs Swagatam Das Radu-Emil Precup
Indian Statistical Institute, India Politehnica University of Timisoara, Romania
Publicity Co-chairs Yew-Soon Ong Carlos Coello Yaochu Jin Rossi Kamal Dongbin Zhao
Nanyang Technological University, Singapore CINVESTAV-IPN, Mexico University of Surrey, UK GERIOT, Bangladesh Institute of Automation, China
Finance and Registration Chairs Andreas Janecek Suicheng Gu
University of Vienna, Austria Google, USA
Local Arrangement Chair Gai-Ge Wang
Ocean University of China, China
Conference Secretariat Renlong Chen
Peking University, China
Program Committee Ashik Ahmed Rafael Alcala Abdelmalek Amine Sabri Arik Carmelo J. A. Bastos Filho Sandeep Bhongade Sujin Bureerat Bin Cao
Islamic University of Technology, Bangladesh University of Granada, Spain Tahar Moulay University of Saida, Algeria Istanbul University, Turkey University of Pernambuco, Brazil G.S. Institute of Technology, India Khon Kaen University, Thailand Tsinghua University, China
Organization
Abdelghani Chahmi Junfeng Chen Walter Chen Hui Cheng Shi Cheng Prithviraj Dasgupta Khaldoon Dhou Bei Dong Haibin Duan Hongyuan Gao Shangce Gao Ying Gao Weian Guo Changan Jiang Mingyan Jiang Liangjun Ke Germano Lambert-Torres Xiujuan Lei Bin Li Zhetao Li Jing Liu Ju Liu Qunfeng Liu Wenlian Lu Chaomin Luo Wenjian Luo Chengying Mao Bernd Meyer Efrén Mezura-Montes Daniel Molina Cabrera Sreeja N. K. Linqiang Pan Quan-Ke Pan Endre Pap Mario Pavone Yan Pei Mukesh Prasad Radu-Emil Precup Robert Reynolds Yuji Sato Carlos Segura Kevin Seppi
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Universite des Sciences et Technologie d’Oran, Algeria Hohai University, China National Taipei University of Technology, Taiwan, China Liverpool John Moores University, UK Shaanxi Normal University, China U. S. Naval Research Laboratory, USA Texas A&M University, USA Shaanxi Nomal University, China Beijing University of Aeronautics and Astronautics, China Harbin Engineering University, China University of Toyama, Japan Guangzhou University, China Tongji University, China Osaka Institute of Technology, Japan Shandong University, China Xian Jiaotong University, China PS Solutions, USA Shaanxi Normal University, China University of Science and Technology of China, China Xiangtan University, China Xidian University, China Shandong University, China Dongguan University of Technology, China Fudan University, China Mississippi State University, USA Harbin Institute of Technology, China Jiangxi University of Finance and Economics, China Monash University, Australia University of Veracruz, Mexico University of Granada, Spain PSG College of Technology, India Huazhong University of Science and Technology, China Huazhong University of Science and Technology, China Singidunum University, Serbia University of Catania, Spain University of Aizu, Japan University of Technology Sydney, Australia Politehnica University of Timisoara, Romania Wayne State University, USA Hosei University, Japan Centro de Investigación en Matemáticas, Mexico Brigham Young University, USA
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Organization
Zhongzhi Shi Joao Soares Xiaoyan Sun Yifei Sun Ying Tan Qirong Tang Mladen Veinović Cong Wang Dujuan Wang Guoyin Wang Rui Wang Yong Wang Yuping Wang Ka-Chun Wong Shunren Xia Ning Xiong Benlian Xu Peng-Yeng Yin Jun Yu Saúl Zapotecas Martínez Jie Zhang Junqi Zhang Tao Zhang Xingyi Zhang Xinchao Zhao Yujun Zheng Zexuan Zhu Miodrag Zivkovic Xingquan Zuo
Institute of Computing Technology, China GECAD, Portugal China University of Mining and Technology, China Shaanxi Normal University, China Peking University, China Tongji University, China Singidunum University, Serbia Northeastern University, China Sichuan University, China Chongqing University of Posts and Telecommunications, China National University of Defense Technology, China Central South University, China Xidian University, China City University of Hong Kong, China Zhejiang University, China Mälardalen University, Sweden Changsu Institute of Technology, China National Chi Nan University, Taiwan, China Niigata University, Japan UAM Cuajimalpa, Mexico Newcastle University, UK Tongji University, China Tianjin University, China Huazhong University of Science and Technology, China Beijing University of Posts and Telecommunications, China Zhejiang University of Technology, China Shenzhen University, China Singidunum University, Serbia Beijing University of Posts and Telecommunications, China
Additional Reviewers Bao, Lin Chen, Yang Cortez, Ricardo Gu, Lingchen Han, Yanyang Hu, Yao Lezama, Fernando Liu, Xiaoxi
Márquez Grajales, Aldo Ramos, Sérgio Rivera Lopez, Rafael Rodríguez de la Cruz, Juan Antonio Vargas Hakim, Gustavo Adolfo Yu, Luyue Zhou, Tianwei
Contents – Part I
Swarm Intelligence and Nature-Inspired Computing Swarm Unit Digital Control System Simulation . . . . . . . . . . . . . . . . . . . . . Eugene Larkin, Aleksandr Privalov, and Tatiana Akimenko Natural Emergence of Heterogeneous Strategies in Artificially Intelligent Competitive Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ankur Deka and Katia Sycara Analysis of Security Problems in Groups of Intelligent Sensors . . . . . . . . . . Karen Grigoryan, Evgeniya Olefirenko, Elena Basan, Maria Lapina, and Massimo Mecella Optimization of a High-Lift Mechanism Motion Generation Synthesis Using MHS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Suwin Sleesongsom and Sujin Bureerat
3
13 26
38
Liminal Tones: Swarm Aesthetics and Materiality in Sound Art . . . . . . . . . . Mahsoo Salimi and Philippe Pasquier
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Study on the Random Factor of Firefly Algorithm. . . . . . . . . . . . . . . . . . . . Yanping Qiao, Feng Li, Cong Zhang, Xiaofeng Li, and Zhigang Zhou
58
Metaheuristic Optimization on Tensor-Type Solution via Swarm Intelligence and Its Application in the Profit Optimization in Designing Selling Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Frederick Kin Hing Phoa, Hsin-Ping Liu, Yun-Heh (Jessica) Chen-Burger, and Shau-Ping Lin
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An Improved Dragonfly Algorithm Based on Angle Modulation Mechanism for Solving 0–1 Knapsack Problems . . . . . . . . . . . . . . . . . . . . . Lin Wang, Ronghua Shi, Wenyu Li, Xia Yuan, and Jian Dong
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A Novel Physarum-Based Optimization Algorithm for Shortest Path . . . . . . . Dan Wang and Zili Zhang
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Traveling Salesman Problem via Swarm Intelligence . . . . . . . . . . . . . . . . . . Pei-Chen Yen and Frederick Kin Hing Phoa
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Swarm-Based Computing Algorithms for Optimization Lion Swarm Optimization by Reinforcement Pattern Search . . . . . . . . . . . . . Falei Ji and Mingyan Jiang
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Contents – Part I
Fuzzy Clustering Algorithm Based on Improved Lion Swarm Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haiyan Yu, Mingyan Jiang, Dongfeng Yuan, and Miaomiao Xin
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Sparrow Search Algorithm for Solving Flexible Jobshop Scheduling Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingliang Wu, Dongsheng Yang, Zhile Yang, and Yuanjun Guo
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Performance Analysis of Evolutionary Computation Based on Tianchi Service Scheduling Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jun Yu, Yuhao Li, Tianwei Zhou, Churong Zhang, Guanghui Yue, and Yunjiao Ge An Intelligent Algorithm for AGV Scheduling in Intelligent Warehouses . . . . Xue Wu, Min-Xia Zhang, and Yu-Jun Zheng Success-History Based Position Adaptation in Gaining-Sharing Knowledge Based Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shakhnaz Akhmedova and Vladimir Stanovov
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Particle Swarm Optimization Multi-guide Particle Swarm Optimisation Control Parameter Importance in High Dimensional Spaces. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Timothy G. Carolus and Andries P. Engelbrecht
185
Research on the Latest Development of Particle Swarm Optimization Algorithm for Satellite Constellation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jia-xu Zhang and Xiao-peng Yan
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Polynomial Approximation Using Set-Based Particle Swarm Optimization . . . Jean-Pierre van Zyl and Andries P. Engelbrecht Optimizing Artificial Neural Network for Functions Approximation Using Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lina Zaghloul, Rawan Zaghloul, and Mohammad Hamdan Two Modified NichePSO Algorithms for Multimodal Optimization . . . . . . . . Tyler Crane, Andries Engelbrecht, and Beatrice Ombuki-Berman
210
223 232
VaCSO: A Multi-objective Collaborative Competition Particle Swarm Algorithm Based on Vector Angles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Libao Deng, Le Song, Sibo Hou, and Gaoji Sun
244
The Experimental Analysis on Transfer Function of Binary Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yixuan Luo, Jianhua Liu, Xingsi Xue, Renyuan Hu, and Zihang Wang
254
Contents – Part I
Multi-stage COVID-19 Epidemic Modeling Based on PSO and SEIR . . . . . . Haiyun Qiu, Jinsong Chen, and Ben Niu Particle Swarms Reformulated Towards a Unified and Flexible Framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mauro Sebastián Innocente
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Ant Colony Optimization On One Bicriterion Discrete Optimization Problem and a Hybrid Ant Colony Algorithm for Its Approximate Solution . . . . . . . . . . . . . . . . . . . . . Yurii A. Mezentsev and Nikita Y. Chubko
289
Initializing Ant Colony Algorithms by Learning from the Difficult Problem’s Global Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiangyang Deng, Limin Zhang, and Ziqiang Zhu
301
An Ant Colony Optimization Based Approach for Binary Search . . . . . . . . . N. K. Sreelaja and N. K. Sreeja
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A Slime Mold Fractional-Order Ant Colony Optimization Algorithm for Travelling Salesman Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ziheng Rong, Xiaoling Gong, Xiangyu Wang, Wei Lv, and Jian Wang
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Ant Colony Optimization for K-Independent Average Traveling Salesman Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu Iwasaki and Koji Hasebe
333
Differential Evolution Inferring Small-Scale Maximum-Entropy Genetic Regulatory Networks by Using DE Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fu Yin, Jiarui Zhou, Zexuan Zhu, Xiaoliang Ma, and Weixin Xie Variable Fragments Evolution in Differential Evolution . . . . . . . . . . . . . . . . Changshou Deng, Xiaogang Dong, Yucheng Tan, and Hu Peng The Efficiency of Interactive Differential Evolution on Creation of ASMR Sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Makoto Fukumoto
347 358
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Genetic Algorithm and Evolutionary Computation Genetic Algorithm Fitness Function Formulation for Test Data Generation with Maximum Statement Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tatiana Avdeenko and Konstantin Serdyukov
379
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Contents – Part I
A Genetic Algorithm-Based Ensemble Convolutional Neural Networks for Defect Recognition with Small-Scale Samples . . . . . . . . . . . . . . . . . . . . Yiping Gao, Liang Gao, Xinyu Li, and Cuiyu Wang Biased Random-Key Genetic Algorithm for Structure Learning. . . . . . . . . . . Baodan Sun and Yun Zhou
390 399
Fireworks Algorithms Performance Analysis of the Fireworks Algorithm Versions . . . . . . . . . . . . . Ira Tuba, Ivana Strumberger, Eva Tuba, Nebojsa Bacanin, and Milan Tuba
415
Using Population Migration and Mutation to Improve Loser-Out Tournament-Based Fireworks Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . PengCheng Hong and JunQi Zhang
423
Region Selection with Discrete Fireworks Algorithm for Person Re-identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xuan Li, Tao Zhang, Xin Zhao, and Shuang Li
433
Fireworks Harris Hawk Algorithm Based on Dynamic Competition Mechanism for Numerical Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenyu Li, Ronghua Shi, Heng Zou, and Jian Dong
441
Enhancing Fireworks Algorithm in Local Adaptation and Global Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yifeng Li and Ying Tan
451
Brain Storm Optimization Algorithm Multi-objective Brainstorming Optimization Algorithm Based on Adaptive Mutation Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yali Wu, Yulong Wang, and Xiaoxiao Quan Brain Storm Optimization Algorithm Based on Formal Concept Analysis. . . . Fengrong Chang, Lianbo Ma, Yan Song, and Aoshuang Dong An Improved Brain Storm Optimization Algorithm Based on Maximum Likelihood Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junfeng Chen, Xingsi Xue, and Bulgan Ninjerdene
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Bacterial Foraging Optimization Algorithm Reorganized Bacterial Foraging Optimization Algorithm for Aircraft Maintenance Technician S