A Novel Decomposition-Based Multi-Objective Symbiotic Organism Search Optimization Algorithm

Narayanan Ganesh, Rajendran Shankar, Kanak Kalita (), Pradeep Jangir, Diego Oliva and Marco Pérez-Cisneros ()
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Narayanan Ganesh: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
Rajendran Shankar: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India
Kanak Kalita: Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi 600062, India
Pradeep Jangir: Rajasthan Rajya Vidyut Prasaran Nigam, Losal, Jaipur 302006, India
Diego Oliva: Departamento de Innovación Basada en la Información y el Conocimiento, Universidad de Guadalajara, CUCEI, Guadalajara 44100, Mexico
Marco Pérez-Cisneros: Departamento de Innovación Basada en la Información y el Conocimiento, Universidad de Guadalajara, CUCEI, Guadalajara 44100, Mexico

Mathematics, 2023, vol. 11, issue 8, 1-25

Abstract: In this research, the effectiveness of a novel optimizer dubbed as decomposition-based multi-objective symbiotic organism search (MOSOS/D) for multi-objective problems was explored. The proposed optimizer was based on the symbiotic organisms’ search (SOS), which is a star-rising metaheuristic inspired by the natural phenomenon of symbioses among living organisms. A decomposition framework was incorporated in SOS for stagnation prevention and its deep performance analysis in real-world applications. The investigation included both qualitative and quantitative analyses of the MOSOS/D metaheuristic. For quantitative analysis, the MOSOS/D was statistically examined by using it to solve the unconstrained DTLZ test suite for real-parameter continuous optimizations. Next, two constrained structural benchmarks for real-world optimization scenario were also tackled. The qualitative analysis was performed based on the characteristics of the Pareto fronts, boxplots, and dimension curves. To check the robustness of the proposed optimizer, comparative analysis was carried out with four state-of-the-art optimizers, viz., MOEA/D, NSGA-II, MOMPA and MOEO, grounded on six widely accepted performance measures. The feasibility test and Friedman’s rank test demonstrates the dominance of MOSOS/D over other compared techniques and exhibited its effectiveness in solving large complex multi-objective problems.

Keywords: multi-objective problems; Pareto front; decomposition; metaheuristics; constraints problems; truss optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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