Push and Pull Search Embedded in an M2M Framework for Solving Constrained Multi-objective Optimization Problems
Zhun Fan, Zhaojun Wang, Wenji Li, Yutong Yuan, Yugen You, Zhi Yang,, Fuzan Sun, Jie Ruan, Zhaocheng Li
TL;DR
This paper introduces a novel approach combining push and pull search strategies within an M2M framework to effectively solve constrained multi-objective optimization problems, enhancing convergence and diversity.
Contribution
It proposes an innovative integration of push and pull search methods embedded in an M2M framework specifically designed for CMOPs, addressing key challenges in MOEAs.
Findings
Improved convergence rates on benchmark CMOPs
Enhanced diversity maintenance in solutions
Effective handling of constraints in multi-objective problems
Abstract
In dealing with constrained multi-objective optimization problems (CMOPs), a key issue of multi-objective evolutionary algorithms (MOEAs) is to balance the convergence and diversity of working populations.
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Metaheuristic Optimization Algorithms Research · Scheduling and Optimization Algorithms