Multi-objective Cat Swarm Optimization Algorithm based on a Grid System

TL;DR

This paper introduces GMOCSO, a multi-objective optimization algorithm that enhances the original Cat Swarm Optimization with grid and archive strategies, demonstrating improved robustness on benchmark and real-world problems.

Contribution

The paper proposes GMOCSO, integrating grid and archive strategies into CSO, replacing roulette selection, to improve convergence and diversity in multi-objective optimization.

Findings

GMOCSO outperforms other algorithms on benchmark functions.

The algorithm shows robustness in real-world pressure vessel design.

Statistical analysis confirms the effectiveness of GMOCSO.

Abstract

This paper presents a multi-objective version of the Cat Swarm Optimization Algorithm called the Grid-based Multi-objective Cat Swarm Optimization Algorithm (GMOCSO). Convergence and diversity preservation are the two main goals pursued by modern multi-objective algorithms to yield robust results. To achieve these goals, we first replace the roulette wheel method of the original CSO algorithm with a greedy method. Then, two key concepts from Pareto Archived Evolution Strategy Algorithm (PAES) are adopted: the grid system and double archive strategy. Several test functions and a real-world scenario called the Pressure vessel design problem are used to evaluate the proposed algorithm's performance. In the experiment, the proposed algorithm is compared with other well-known algorithms using different metrics such as Reversed Generational Distance, Spacing metric, and Spread metric. The optimization results show the robustness of the proposed algorithm, and the results are further confirmed using statistical methods and graphs. Finally, conclusions and future directions were presented..