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MOPGO: a new physics-based multi-objective plasma generation optimizer for solving structural optimization problems
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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-worldstructural optimization design problems. The Plasma Generation Optimization (PGO) algorithm is a recentlyreported physics-based algorithm inspired by the generation process of plasma in which electron movementand its energy level are based on excitation modes, de-excitation, and ionization processes. As the searchprogresses, 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 proposedposteriori method exercises a non-dominated sorting strategy to preserve population diversity, which is acrucial problem in multi-objective meta-heuristic algorithms. In truss design problems, minimization of thetruss’s 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. Theefficacy of MOPGO is evaluated based on ten performance metrics. The results demonstrate that the proposedMOPGO algorithm achieves the optimal solution with less computational complexity and has a betterconvergence, coverage, diversity, and spread. The Pareto fronts of MOPGO are compared and contrasted withmulti-objective passing vehicle search algorithm, multi-objective slime mould algorithm, multi-objectivesymbiotic organisms search algorithm, and multi-objective ant lion optimization algorithm. This study willbe further supported with external guidance at https://premkumarmanoharan.wixsite.com/mysite.
Item Type: | Article |
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Authors/Creators: | Kumar, S and Jangir, P and Tejani, GG and Premkumar, M and Alhelou, HH |
Keywords: | constraints optimization problems, crowding distance, meta-heuristics, non-dominated sorting, numerical optimization, Pareto front, structure optimization |
Journal or Publication Title: | IEEE Access |
Publisher: | United States |
ISSN: | 2169-3536 |
DOI / ID Number: | 10.1109/ACCESS.2021.3087739 |
Copyright Information: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
Item Statistics: | View statistics for this item |
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