Dialectical Optimization: A Metaheuristic Inspired by Human Argumentation for Multi-objective Problems

Shah, Ravikumar; Bhatt, Tanvi; parmar, Jay; Barbhaya, Mayur

doi:10.51094/jxiv.1950

##article.authors##

Ravikumar Shah Research and Development Department, VeBuIn, Japan
Tanvi Bhatt Research and Development Department, VeBuIn, Japan
parmar, Jay Research and Development Department, VeBuIn, Japan
Mayur Barbhaya Research and Development Department, VeBuIn, Japan

DOI:

https://doi.org/10.51094/jxiv.1950

キーワード:

Multi-objective Optimization、 Metaheuristics、 Swarm Intelligence、 Human Behavior、 Argumentation、 Dialectical Optimization、 Parameter-Free Algorithm

抄録

Multi-objective optimization problems, characterized by multiple conflicting objectives, are prevalent in science and engineering. While numerous metaheuristics have been proposed, most draw inspiration from simplified biological or physical processes. This paper introduces a fundamentally new class of algorithm, Dialectical Optimization for Multi-objective Problems, which is inspired by the human cognitive and social process of argumentation and consensus-building. We present the final, enhanced version of the algorithm, Unified DOMO with Argumentative Leap (U-DOMO+), a parameter-free metaheuristic designed for black-box MOOPs. The core of U-DOMO+ is a novel Dialectical Operator where solutions, termed ’Arguments’, refine their positions based on persuasion from elite arguments and skeptical exploration. To ensure robust performance, U-DOMO+ integrates a state of-the-art selection mechanism based on non-dominated sorting and crowding distance, and an Argumentative Leap mutation operator to maintain diversity. We demonstrate the algorithm’s effectiveness on the challenging ZDT benchmark problems, showing that U-DOMO+ successfully converges to the true Pareto front with excellent diversity, establishing it as a promising and novel contribution to the field of multi-objective optimization.

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The authors declare that they have no conflicts of interest.

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