University of Qom Journal of Engineering Management and Soft Computing 3116-0158 12 2 2026 04 01 Solving Multi-objective Optimization Problems Using the Society Deciling Process Algorithm 75 94 4296 10.22091/jemsc.2026.13697.1297 EN Aylar Poorahad Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran, Email: aylar.p.2000@gmail.com 0009-0007-5366-9030 Einollah Pira Corresponding author, Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran, Email: pira@azaruniv.ac.ir 0000-0001-9010-6113 Mohammad Khodizadeh-Nahari Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran, Email: m.khodizadeh@azaruniv.ac.ir 0009-0007-7416-3100 Sajad Esfandyari Department of Computer Engineering, Malayer University, Malayer, Iran. Email: sajad.a1367@gmail.com 0000-0002-3971-7215 Journal Article 2025 08 22 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. https://jemsc.qom.ac.ir/article_4296_d1d17d37e9d635ff0087e5a05325e4a4.pdf