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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, Jangir, P, Tejani, GG, Premkumar, M and Alhelou, HH 2021 , 'MOPGO: a new physics-based multi-objective plasma generation optimizer for solving structural optimization problems' , IEEE Access, vol. 9 , pp. 84982-85016 , doi: 10.1109/ACCESS.2021.3087739.

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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

Item Type: Article
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
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This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see

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