An Inverse Modeling Constrained Multi-Objective Evolutionary Algorithm Based on Decomposition

TL;DR

This paper presents IM-C-MOEA/D, a novel inverse modeling constrained multi-objective evolutionary algorithm based on decomposition, which effectively solves constrained real-world optimization problems with superior performance and robustness.

Contribution

It introduces a new inverse modeling constrained MOEA/D approach that bridges gaps in applying inverse models to constrained problems, demonstrating improved results.

Findings

Outperforms state-of-the-art constrained MOEAs on diverse real-world problems

Shows robustness and applicability in constrained optimization scenarios

Effective in solving complex real-world constrained problems

Abstract

This paper introduces the inverse modeling constrained multi-objective evolutionary algorithm based on decomposition (IM-C-MOEA/D) for addressing constrained real-world optimization problems. Our research builds upon the advancements made in evolutionary computing-based inverse modeling, and it strategically bridges the gaps in applying inverse models based on decomposition to problem domains with constraints. The proposed approach is experimentally evaluated on diverse real-world problems (RWMOP1-35), showing superior performance to state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs). The experimental results highlight the robustness of the algorithm and its applicability in real-world constrained optimization scenarios.