Constraint-Handling in Evolutionary Optimization

by Efrén Mezura-Montes (Editor)

Overview

An efficient and adequate constraint-handling technique is a key element in the design of competitive evolutionary algorithms to solve complex optimization problems. This edited book presents a collection of recent advances in nature-inspired techniques for constrained numerical optimization. The book covers six main topics: swarm-intelligence-based approaches, studies in differential evolution, evolutionary multiobjective constrained optimization, hybrid approaches, real-world applications and the recent use of the artificial immune system in constrained optimization. Within the chapters, the reader will find different studies about specialized subjects, such as: special mechanisms to focus the search on the boundaries of the feasible region, the relevance of infeasible solutions in the search process, parameter control in constrained optimization, the combination of mathematical programming techniques and evolutionary algorithms in constrained search spaces and the adaptation of novel nature-inspired algorithms for numerical optimization with constraints.

"Constraint-Handling in Evolutionary Optimization" is an important reference for researchers, practitioners and students in disciplines such as optimization, natural computing, operations research, engineering and computer science.

Product Details

ISBN-13:	9783642101557
Publisher:	Springer Berlin Heidelberg
Publication date:	12/21/2010
Series:	Studies in Computational Intelligence , #198
Edition description:	Softcover reprint of hardcover 1st ed. 2009
Pages:	264
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

Continuous Constrained Optimization with Dynamic Tolerance Using the COPSO Algorithm.- Boundary Search for Constrained Numerical Optimization Problems.- Solving Difficult Constrained Optimization Problems by the— Constrained Differential Evolution with Gradient-Based Mutation.- Constrained Real-Parameter Optimization with—Self-Adaptive Differential Evolution.- Self-adaptive and Deterministic Parameter Control in Differential Evolution for Constrained Optimization.- An Adaptive Penalty Function for Handling Constraint in Multi-objective Evolutionary Optimization.- Infeasibility Driven Evolutionary Algorithm for Constrained Optimization.- On GA-AIS Hybrids for Constrained Optimization Problems in Engineering.- Constrained Optimization Based on Quadratic Approximations in Genetic Algorithms.- Constraint-Handling in Evolutionary Aerodynamic Design.- Handling Constraints in Global Optimization Using Artificial Immune Systems: A Survey.