Intro
Preface
Organization
Contents
Part II
Contents
Part I
Multi-objective Optimization
A Multi-objective Evolutionary Algorithm Based on Second-Order Differential Operator
1 Introduction
2 MOP Problem
3 Second-Order Differential Evolution
4 MOEA/D-SODE Algorithm
4.1 General Framework of MOEA/D-SODE
4.2 A SODE-Best Second-Order Differential Operator
4.3 The Flow Chart of MOEA/D-SODE
5 Experimental Results and Analysis
5.1 Experimental Environment and Parameter Setting
5.2 Performance Metrics
5.3 Analysis of Experimental Results

6 Conclusion
References
An Improved Evolutionary Multi-objective Optimization Algorithm Based on Multi-population and Dynamic Neighborhood
1 Introduction
2 Problem Statement and Related Methods
2.1 Problem Statement
2.2 Related Methods
3 Proposed Method
3.1 Framework of the Proposed Method
3.2 Multi-population Strategy
3.3 Dynamic Neighborhood
4 Experiment and Analysis
4.1 Settings
4.2 Results and Analysis
5 Conclusion
References
A Multiobjective Memetic Algorithm for Multiobjective Unconstrained Binary Quadratic Programming Problem

1 Introduction
2 Background
2.1 Multiobjective Optimization
2.2 Formulation of mUBQP
3 Proposed Algorithm: MOMA
3.1 Framework of MOMA
3.2 Population Initialization and Stopping Criterion
3.3 Uniform Generation
3.4 Crossover Operator and Tabu Search
3.5 Archive and Weight Vector Updating
4 Computational Experiments
4.1 Experimental Settings and Performance Measures
4.2 Competitors
4.3 Comparing MOMA with the Competitors
5 Conclusion and Future Work
References

A Hybrid Algorithm for Multi-objective Permutation Flow Shop Scheduling Problem with Setup Times
1 Introduction
2 Problem Description
3 Proposed Hybrid Algorithm for Multi-objective PFSP with Setup Times
3.1 Encoding and Decoding of Chromosome
3.2 Initial Population Generation
3.3 Pareto Sorting
3.4 Selection and Crossover Operator
3.5 Mutation Operator
3.6 Neighborhood Structure Design
3.7 Flowchart of Proposed MOHGA
4 Experimental Results and Analysis
5 Conclusion and Future Work
References

Dynamic Multi-objective Optimization via Sliding Time Window and Parallel Computing
1 Introduction
2 Background
2.1 Dynamic Multi-objective Optimization
2.2 Performance Metric
2.3 Sliding Time Window
3 Related Work
4 Sliding Time Window Based on Parallel Computing
5 Experimental Results and Analyses
5.1 Test the STW-PC Using MIGD
5.2 Experimental Analysis
6 Conclusions and Future Work
References
A New Evolutionary Approach to Multiparty Multiobjective Optimization
1 Introduction
2 Related Work