A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm for Solving Engineering Problems

Nour Elhouda Chalabi, Abdelouahab Attia, Khalid Abdulaziz Alnowibet, Hossam M. Zawbaa, Hatem Masri and Ali Wagdy Mohamed ()
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Nour Elhouda Chalabi: Computer Science Department, University Mohamed Boudiaf of Msila, Msila 28000, Algeria
Abdelouahab Attia: LMSE Laboratory, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, Bordj Bou Arreridj 34000, Algeria
Khalid Abdulaziz Alnowibet: Statistics and Operations Research Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Hossam M. Zawbaa: CeADAR Ireland’s Center for Applied AI, Technological University Dublin, D7 EWV4 Dublin, Ireland
Hatem Masri: Applied Science University, Sakhir 32038, Bahrain
Ali Wagdy Mohamed: Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt

Mathematics, 2023, vol. 11, issue 14, 1-37

Abstract: Metaheuristics in recent years has proven its effectiveness; however, robust algorithms that can solve real-world problems are always needed. In this paper, we suggest the first extended version of the recently introduced gaining–sharing knowledge optimization (GSK) algorithm, named multiobjective gaining–sharing knowledge optimization (MOGSK), to deal with multiobjective optimization problems (MOPs). MOGSK employs an external archive population to store the nondominated solutions generated thus far, with the aim of guiding the solutions during the exploration process. Furthermore, fast nondominated sorting with crowding distance was incorporated to sustain the diversity of the solutions and ensure the convergence towards the Pareto optimal set, while the ϵ -dominance relation was used to update the archive population solutions. ϵ -dominance helps provide a good boost to diversity, coverage, and convergence overall. The validation of the proposed MOGSK was conducted using five biobjective (ZDT) and seven three-objective test functions (DTLZ) problems, along with the recently introduced CEC 2021, with fifty-five test problems in total, including power electronics, process design and synthesis, mechanical design, chemical engineering, and power system optimization. The proposed MOGSK was compared with seven existing optimization algorithms, including MOEAD, eMOEA, MOPSO, NSGAII, SPEA2, KnEA, and GrEA. The experimental findings show the good behavior of our proposed MOGSK against the comparative algorithms in particular real-world optimization problems.

Keywords: multiobjective optimization; gaining–sharing knowledge optimization; crowding distance; Pareto optimal set; ? dominance relation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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