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MOPGO: a new physics-based multi-objective plasma generation optimizer for solving structural optimization problems

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Kumar, S and Jangir, P and Tejani, GG and Premkumar, M and Alhelou, HH, MOPGO: a new physics-based multi-objective plasma generation optimizer for solving structural optimization problems, IEEE Access, 9 pp. 84982-85016. ISSN 2169-3536 (2021) [Refereed Article]


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This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1109/ACCESS.2021.3087739

Abstract

This paper proposes a new Multi-Objective Plasma Generation Optimization (MOPGO) algorithm, and its non-dominated sorting mechanism is investigated for numerous challenging real-world structural optimization design problems. The Plasma Generation Optimization (PGO) algorithm is a recently reported physics-based algorithm inspired by the generation process of plasma in which electron movement and its energy level are based on excitation modes, de-excitation, and ionization processes. As the search progresses, a better balance between exploration and exploitation has a more significant impact on the results; thus, the crowding distance feature is incorporated in the proposed MOPGO algorithm. Also, the proposed posteriori method exercises a non-dominated sorting strategy to preserve population diversity, which is a crucial problem in multi-objective meta-heuristic algorithms. In truss design problems, minimization of the trussís mass and maximization of nodal displacement are considered objective functions. In contrast, elemental stress and discrete cross-sectional areas are assumed to be behavior and side constraints, respectively. The usefulness of MOPGO to solve complex problems is validated by eight truss-bar design problems. The efficacy of MOPGO is evaluated based on ten performance metrics. The results demonstrate that the proposed MOPGO algorithm achieves the optimal solution with less computational complexity and has a better convergence, coverage, diversity, and spread. The Pareto fronts of MOPGO are compared and contrasted with multi-objective passing vehicle search algorithm, multi-objective slime mould algorithm, multi-objective symbiotic organisms search algorithm, and multi-objective ant lion optimization algorithm. This study will be further supported with external guidance at https://premkumarmanoharan.wixsite.com/mysite.

Item Details

Item Type:Refereed Article
Keywords:constraints optimization problems, crowding distance, meta-heuristics, non-dominated sorting, numerical optimization, Pareto front, structure optimization
Research Division:Engineering
Research Group:Maritime engineering
Research Field:Maritime engineering not elsewhere classified
Objective Division:Energy
Objective Group:Energy storage, distribution and supply
Objective Field:Energy systems and analysis
UTAS Author:Kumar, S (Mr Sumit Kumar)
ID Code:146864
Year Published:2021
Web of Science® Times Cited:15
Deposited By:Australian Maritime College
Deposited On:2021-09-30
Last Modified:2021-11-15
Downloads:4 View Download Statistics

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