Tackling complex optimization problems with multiple conflicting objectives? This review provides a comprehensive analysis of multiobjective evolutionary algorithms (MOEAs), assessing their theoretical developments, classification schemes, and contemporary applications across science and engineering. The discussion rigorously defines multiobjective optimization problems, presents an MOEA classification scheme, and evaluates various contemporary MOEAs. Key topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. The review focuses on key analytical insights based on critical MOEA evaluation of current research and applications. Recommended MOEA designs are presented, along with conclusions and recommendations for future work. This analysis serves as a valuable resource for researchers and practitioners in optimization, offering guidance on MOEA selection and design to effectively solve complex, multiobjective problems in diverse fields.
Published in Evolutionary Computation, this paper aligns with the journal's focus on evolutionary algorithms and their applications. By providing a rigorous analysis and classification of MOEAs, the study contributes to the theoretical and practical advancements in solving complex optimization problems, a key area of interest for the journal's audience.