Decomposition-based Evolutionary Optimization in Complex Environments

Multi-objective optimization problems (MOPs) and uncertain optimization problems (UOPs) which widely exist in real life are challengeable problems in the fields of decision making, system designing, and scheduling, amongst others. Decomposition exploits the ideas of ÔÇÿmaking things simpleÔÇÖ and ÔÇÿdivide and conquerÔÇÖ to transform a complex problem into a series of simple ones with the aim of reducing the computational complexity. In order to tackle the abovementioned two types of complicated optimization problems, this book introduces the decomposition strategy and conducts a systematic study to perfect the usage of decomposition in the field of multi-objective optimization, and extend the usage of decomposition in the field of uncertain optimization.Contents: IntroductionDecomposition-based Multi-objective Evolutionary Algorithm with the ε-Constraint FrameworkDecomposition-based Many-objective Evolutionary Algorithm with the ε-Constraint FrameworkAn A Posteriori Decision-making Framework and Subproblems Co-solving Evolutionary Algorithm for Uncertain OptimizationNoise-Tolerant Techniques for Decomposition-based Multi-objective Evolutionary AlgorithmsThe Bi-objective Critical Node Detection Problem with Minimum Pairwise Connectivity and Cost: Theory and AlgorithmsSolving Bi-objective Uncertain Stochastic Resource Allocation Problems by the CVaR-based Risk Measure and Decomposition-based Multi-objective Evolutionary Algorithms
Readership: Researchers and professionals in computer science that specialise or deal with multi-objective optimization and uncertain optimization in decision making, system designing, and scheduling.Algorithm Design;Application of Multi-Objective Uncertain Optimization Approaches;Multi-Objective Optimization;Uncertain Optimization;Combinatorial Optimization;Intelligent/Evolutionary Algorithms0Key Features:Algorithm designApplication of multi-objective uncertain optimization approaches