Intro
Preface
Organization
Contents - Part I
Contents - Part II
Evolutionary Computation and Swarm Intelligence
An Optimization Task Scheduling Model for Multi-robot Systems in Intelligent Warehouses
1 Introduction
2 Related Work
3 Intelligent Warehouses
3.1 Problem Statement
3.2 BHM
4 Improved Intelligent Warehouses
4.1 New Model
4.2 New Model
5 Simulated Experiments
5.1 Performance Metrics
5.2 Parameter Settings
5.3 Analysis of Results
6 Conclusion
References
A Multi-objective Optimization Algorithm for Wireless Sensor Network Energy Balance Problem in Internet of Things
Abstract
1 Introduction
2 WSN Energy Balance Problem
2.1 Problem Analysis
2.2 Problem Model
3 Algorithm Principle
3.1 NSGA-II
3.2 Clustering Mechanism
3.3 Framework of CMNSGA-II
4 Experimental Simulation
4.1 Parameter Setting
4.2 Result Analysis
5 Conclusion
References
Improved AODV Routing Protocol Based on Multi-objective Simulated Annealing Algorithm
Abstract
1 Introduction
2 Ad Hoc Network and AODV Protocol
3 Multi-objective Simulated Annealing
3.1 Single-objective Simulated Annealing
3.2 Multi-objective Simulated Annealing
3.3 Main Process of Multi-target Annealing
4 Multi-objective Optimization AODV Routing Protocol
4.1 Network Model
4.2 Fitness Function
4.3 Perturbation Function
4.4 Multi-objective Simulated Annealing Optimization Algorithm
5 Simulation and Analysis
5.1 Simulation Scenario Description
5.2 Simulation Parameter Setting
5.3 Simulation Experiment Results and Comparative Analysis
6 Conclusion
Acknowledgement
References
Solving Satellite Range Scheduling Problem with Learning-Based Artificial Bee Colony Algorithm
Abstract
1 Introduction
2 Problem Description.

3 Learning-Based Artificial Bee Colony Algorithm
3.1 Traditional Artificial Bee Colony Algorithm
3.2 Algorithm Overall Framework
3.3 Position-Based Learning Strategy
3.4 Error-Based Learning Strategy
3.5 Parameter Analysis
4 Experiment Analysis
4.1 Analysis of Algorithm
4.2 Comparison to State-Of-The-Art Algorithms
5 Conclusion
Acknowledgement
References
Black Widow Spider Algorithm Based on Differential Evolution and Random Disturbance
Abstract
1 Introduction
2 Black Widow Spider Algorithm
3 Improved Black Widow Spider Algorithm
3.1 Population Reproduction for Global Optimization
3.2 Population Mutation Based on Differential Evolution Algorithm
3.3 Random Disturbance Strategy
4 Algorithm Implementation
5 Simulation Experiment
5.1 Test Function
5.2 Design Problems of Tension Spring
6 Summary
Acknowledgments
References
Attribute Selection Method Based on Artificial Bee Colony Algorithm and Neighborhood Discrimination Matrix Optimization
1 Introduction
2 Related Knowledge
2.1 Basic Knowledge of Neighborhood Rough Set
2.2 Artificial Bee Colony and Its Improved Algorithm
3 Improved Attribute Selection Algorithm for Artificial Bee Colony and Neighborhood Discrimination Matrix
3.1 Definition of Attribute Importance of Neighborhood Discernibility Matrix
3.2 Fitness Function Construction
3.3 Neighborhood Discernibility Matrix Importance and Artificial Bee Colony Feature Selection Algorithm
4 Experiment Analysis
4.1 Selection of
4.2 Algorithm Comparison Results
4.3 Algorithm Performance Comparison
5 Concluding Remarks
References
A Cuckoo Quantum Evolutionary Algorithm for the Graph Coloring Problem
Abstract
1 Introduction
2 Problem Description
3 The Cuckoo Quantum Evolutionary Algorithm for GCP.

3.1 Representation of the Solution to the Graph Coloring Problem
3.2 Quantum Matrix in the Cuckoo Quantum Evolutionary Algorithm
3.3 Framework of the Cuckoo Quantum Evolutionary Algorithm
3.4 Initialization of the CQEA
3.5 Local Search in the Solution Space
3.6 The Cuckoo Search
3.7 The Perturbance Strategy
4 Experimental Results
5 Conclusion
References
Feature Selection Algorithm Based on Discernibility Matrix and Fruit Fly Optimization
1 Introduction
2 Related Knowledge
2.1 Rough Set Theory
2.2 Fruit Fly Optimization Algorithm
3 Feature Selection Algorithm Based on Discernibility Matrix and Fruit Fly Optimization
3.1 Method of Defining Attribute Importance Based on Discernibility Matrix
3.2 Rough Set Fitness Function
3.3 Feature Selection Algorithm Based on Discernibility Matrix and Improved Fruit Fly Optimization
3.4 Algorithm Time Complexity
4 Experimental Data Analysis
4.1 Instance Verification
4.2 UCI Data Sets Experimental Data Analysis
5 Concluding Remarks
References
Feature Selection Method Based on Ant Colony Optimization Algorithm and Improved Neighborhood Discernibility Matrix
1 Introduction
2 Related Knowledge
2.1 Neighborhood Rough Set Theory
2.2 Ant Colony Algorithm and Its Optimized Feature Selection Method
3 Feature Selection Method Based on ACO and Improved Neighborhood Discernibility Matrix
3.1 The Definition of Attribute Importance of Neighborhood Discernibility Matrix
3.2 Description of Algorithm Steps
3.3 Analysis of Algorithm Time Complexity
4 Analysis of Experimental Data
4.1 Case Analysis
4.2 Analysis of UCI Data Set
5 Concluding Remarks
References
Implementation and Application of NSGA-III Improved Algorithm in Multi-objective Environment
Abstract
1 Introduction.

2 Many-Objective Problems and EMO Methodologies
2.1 Potential Difficulties in Dealing with Multi-objective Problems
2.2 Two Strategies to Face These Difficulties
3 NSGA-III Algorithm and Its Improvement
3.1 Determines the Reference Point on the Hyperplane
3.2 Normalization of Population Members
3.3 Association Operation
3.4 Inheritance of Population Offspring
3.5 Evaluation of Service Performance
4 Experiment
4.1 Experimental Environment Setting
4.2 Analysis of Experimental Results
5 Conclusion
References
A Differential Evolution Algorithm for Multi-objective Mixed-Variable Optimization Problems
Abstract
1 Introduction
2 Related Work
3 Overview
3.1 Review of NSGA-II
3.2 Review of MCDEmv
4 The Proposed MO-MCDEmv
4.1 Selection of the Optimal Individual
4.2 The Modifications of Statistics-Based Local Search
4.3 The Framework of MO-MCDEmv
5 Numerical Experiment
5.1 Set Relevant Parameters
5.2 The Results of Two Practical MO-MVOPs
6 Conclusion
Acknowledgments
References
An Effective Data Balancing Strategy Based on Swarm Intelligence Algorithm for Malicious Code Detection and Classification
1 Introduction
2 Swarm Intelligence Optimization Model
3 Dynamic Sampling Strategy Based on Swarm Intelligence Algorithm
3.1 Dynamic Sampling Model
3.2 Dynamic Sampling Based on Swarm Intelligence Algorithm
4 Experimental Evaluation
4.1 Experimental Setup
4.2 Type of Unbalanced Data
4.3 Data Sets and Models
4.4 Result Analysis
5 Conclusion
References
Adaptive Multi-strategy Learning Particle Swarm Optimization with Evolutionary State Estimation
1 Introduction
2 Related Works
2.1 Canonical PSO
2.2 Variants of PSO
3 Proposed AMSLPSO Algorithm
3.1 Evolutionary State Estimation.

3.2 Random Elite and Mainstream Learning Exemplars
3.3 Choose Learning Strategy
3.4 Framework of AMSLPSO
4 Experimental Results and Analysis
4.1 Benchmark Functions and Comparison Algorithms
4.2 Experimental Results
5 Conclusion
References
Water Wave Optimization with Distributed-Learning Refraction
1 Introduction
2 The Basic WWO Algorithm
2.1 Propagation
2.2 Refraction
2.3 Breaking
2.4 The Algorithmic Framework of WWO
3 The Proposed DLWWO Algorithm
3.1 Nonlinear Dimension Reduction
3.2 Distributed-Learning Refraction
3.3 The Framework of DLWWO
3.4 Runtime Complexity of DLWWO
4 Numerical Experiments
4.1 Experimental Parameter Setting
4.2 Comparative Experiments
5 Conclusion
References
Adaptive Differential Privacy Budget Allocation Algorithm Based on Random Forest
Abstract
1 Introduction
2 Related Work
3 Algorithm-Related Definitions
3.1 Solving for Feature Weights and Decision Tree Weights.
3.2 Filtering of Feature Sets
3.3 Adaptive Allocation of Privacy Protection Budgets
4 Algorithm Implementation and Analysis
4.1 Algorithm Flow
4.2 Algorithm Description
5 Experimental Results and Analysis
5.1 Experimental Design
6 Experimental Results and Analysis
7 Conclusion and Discussion
References
A Node Influence Based Memetic Algorithm for Community Detection in Complex Networks
Abstract
1 Introduction
2 Background Knowledge
2.1 Modularity
2.2 Transition Probability Matrix
3 Proposed Algorithm
3.1 Representation
3.2 Framework
3.3 Initialization
3.4 Selection
3.5 Crossover
3.6 Mutation
3.7 Multi-level Greedy Search
4 Experimental Study and Results Analysis
4.1 Experimental Settings
4.2 Experimental Results and Analysis
5 Conclusions
Acknowledgements
References.