Multi-Objective Evolutionary Algorithms

Abstract

Evolutionary algorithms (EA s) have amply shown their promise in solving various search and optimization problems for the past three decades. One of the hallmarks and niches of EAs is their ability to handle multi-objective optimization problems in their totality, which their classical counterparts lack. Suggested in the beginning of the 1990s, evolutionary multi-objective optimization (EMO ) algorithms are now routinely used in solving problems with multiple conflicting objectives in various branches of engineering, science, and commerce. In this chapter, we provide an overview of EMO methodologies by first presenting principles of EMO through an illustration of one specific algorithm and its application to an interesting real-world bi-objective optimization problem. Thereafter, we provide a list of recent research and application developments of EMO to provide a picture of some salient advancements in EMO research. The development and application of EMO to multi-objective optimization problems and their continued extensions to solve other related problems has elevated EMO research to a level which may now undoubtedly be termed as an active field of research with a wide range of theoretical and practical research and application opportunities.