A Simple Evolutionary Algorithm for Multi-modal Multi-objective Optimization
Tapabrata Ray, Mohammad Mohiuddin Mamun, Hemant Kumar Singh

TL;DR
This paper presents a simple, parameter-free steady-state evolutionary algorithm for multi-modal, multi-objective optimization that outperforms existing complex algorithms with fewer function evaluations.
Contribution
The authors introduce a straightforward, effective evolutionary algorithm for MMOPs that requires no additional parameter tuning and demonstrates superior performance on benchmark problems.
Findings
Outperforms six state-of-the-art algorithms on benchmark tests.
Achieves better diversity and convergence with only 1000 function evaluations.
Simplifies MMOP solving without complex mechanisms or parameters.
Abstract
In solving multi-modal, multi-objective optimization problems (MMOPs), the objective is not only to find a good representation of the Pareto-optimal front (PF) in the objective space but also to find all equivalent Pareto-optimal subsets (PSS) in the variable space. Such problems are practically relevant when a decision maker (DM) is interested in identifying alternative designs with similar performance. There has been significant research interest in recent years to develop efficient algorithms to deal with MMOPs. However, the existing algorithms still require prohibitive number of function evaluations (often in several thousands) to deal with problems involving as low as two objectives and two variables. The algorithms are typically embedded with sophisticated, customized mechanisms that require additional parameters to manage the diversity and convergence in the variable and the…
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