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Constrained Multi-objective Evolutionary Algorithm

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Part of the Studies in Computational Intelligence book series (SCI, volume 779)

Abstract

Multi-objective optimization problems are common in practice. In practical problems, constraints are also inevitable. The population approach and implicit parallel search ability of evolutionary algorithms have made them popular and useful in finding multiple trade-off Pareto-optimal solutions in multi-objective optimization problems since the past two decades. In this chapter, we discuss evolutionary multi-objective optimization (EMO) algorithms that are specifically designed for handling constraints. Numerical test problems involving constraints and some constrained engineering design problems which are often used in the EMO literature are discussed next. The chapter is concluded with a number of future directions in constrained multi-objective optimization area.

Keywords

Multi-objective optimization Constrained optimization Evolutionary algorithms Pareto-optimal solution 

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computational Optimization and Innovation (COIN) Laboratory, Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingUSA

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