Evolutionary Multi-objective Optimization - Methods and Metrics

Abstract

Evolutionary Multi-objective Optimization (EMO) is a powerful technique for solving complex optimization problems with multiple conflicting objectives. This paper provides a comprehensive review of methods and metrics used in EMO, focusing on their principles, advantages, and applications. The paper begins by introducing the concept of multi-objective optimization and the challenges it poses. It then explores various EMO algorithms, including genetic algorithms, particle swarm optimization, and differential evolution, highlighting their strengths and weaknesses. Additionally, the paper discusses the importance of performance metrics in evaluating EMO algorithms, such as hypervolume, inverted generational distance, and epsilon indicator. The insights provided in this paper aim to enhance understanding and promote further research in the field of EMO.