Solving Multi-objective Optimization Problems Using the Society Deciling Process Algorithm

Document Type : Original Article

Authors

1 Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran, Email: aylar.p.2000@gmail.com

2 Corresponding author, Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran, Email: pira@azaruniv.ac.ir

3 Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran, Email: m.khodizadeh@azaruniv.ac.ir

4 Department of Computer Engineering, Malayer University, Malayer, Iran. Email: sajad.a1367@gmail.com

10.22091/jemsc.2026.13697.1297

Abstract

Advancements in technology and the emergence of multi-objective optimization problems across various scientific domains have spurred research and development of novel metaheuristic algorithms to address these challenges. Although these methods have largely succeeded in approaching the Pareto-optimal front, the optimization process has not been fully realized. This paper introduces a multi-objective version of the Social Division Process (SDP) algorithm, termed MOSDP, aimed at improving the quality of Pareto front solutions. The MOSDP algorithm employs a memory structure as an archive to store non-dominated solutions. Additionally, it utilizes a non-dominated sorting mechanism based on crowding distance to establish a hierarchical social division structure and guide the evolutionary process in the multi-objective problem space. The performance of MOSDP is evaluated using 18 well-known multi-objective test functions, UF, and IMOP, and compared with the Multi-Objective City Councils Evolution (MOCCE), Multi-Objective Ant Lion Optimization (MOALO), Multi-Objective Slime Mould Algorithm (MOSMA), and Multi-Objective Artificial Hummingbird Algorithm (MOAHA). The results of the Friedman average rank test demonstrate the superiority of MOSDP over the aforementioned algorithms in terms of Inverted Generational Distance (IGD), Generational Distance (GD), and Maximum Spread (MS) metrics.

Keywords

Main Subjects

References

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