Open Access

REVIEW

A Review of the Evolution of Multi-Objective Evolutionary Algorithms

Thomas Hanne^1,*, Mohammad Jahani Moghaddam²

1 Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Olten, 4600, Switzerland
2 Department of Electrical Engineering, Lan.C., Islamic Azad University, Langarud, 4471311127, Iran

* Corresponding Author: Thomas Hanne. Email:

(This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)

Computers, Materials & Continua 2025, 85(3), 4203-4236. https://doi.org/10.32604/cmc.2025.068087

Received 20 May 2025; Accepted 01 September 2025; Issue published 23 October 2025

Abstract

Multi-Objective Evolutionary Algorithms (MOEAs) have significantly advanced the domain of Multi-Objective Optimization (MOO), facilitating solutions for complex problems with multiple conflicting objectives. This review explores the historical development of MOEAs, beginning with foundational concepts in multi-objective optimization, basic types of MOEAs, and the evolution of Pareto-based selection and niching methods. Further advancements, including decom-position-based approaches and hybrid algorithms, are discussed. Applications are analyzed in established domains such as engineering and economics, as well as in emerging fields like advanced analytics and machine learning. The significance of MOEAs in addressing real-world problems is emphasized, highlighting their role in facilitating informed decision-making. Finally, the development trajectory of MOEAs is compared with evolutionary processes, offering insights into their progress and future potential.

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Keywords

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