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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Narayanan Ganesh & Rajendran Shankar & Kanak Kalita & Pradeep Jangir & Diego Oliva & Marco Pérez-Cisneros, 2023. "A Novel Decomposition-Based Multi-Objective Symbiotic Organism Search Optimization Algorithm," Mathematics, MDPI, vol. 11(8), pages 1-25, April.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1898-:d:1125502
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    Cited by:

    1. Viswanath Jagadeesan & Thilagavathi Rajamanickam & Vladimira Schindlerova & Sreelakshmi Subbarayan & Robert Cep, 2023. "A Study on Two-Warehouse Inventory Systems with Integrated Multi-Purpose Production Unit and Partitioned Rental Warehouse," Mathematics, MDPI, vol. 11(18), pages 1-24, September.

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