MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems

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 p...

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Published in:	IEEE access Vol. 9; pp. 84982 - 85016
Main Authors:	Kumar, Sumit, Jangir, Pradeep, Tejani, Ghanshyam G., Premkumar, Manoharan, Alhelou, Hassan Haes
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Complexity Constraints optimization problems crowding distance Design optimization Energy levels Energy states Excitation Heuristic methods Ionization meta-heuristics Multiple objective analysis non-dominated sorting numerical optimization Optimization Pareto front Performance measurement Plasma Plasma (physics) Plasmas Search algorithms Search problems Slime Sorting Sorting algorithms structure optimization Symbiosis Trusses
ISSN:	2169-3536, 2169-3536
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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-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 truss'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. 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 .
AbstractList	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 truss’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. 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 .
Author	Jangir, Pradeep Tejani, Ghanshyam G. Premkumar, Manoharan Alhelou, Hassan Haes Kumar, Sumit
Author_xml	– sequence: 1 givenname: Sumit orcidid: 0000-0003-3042-3779 surname: Kumar fullname: Kumar, Sumit organization: Australian Maritime College, College of Sciences and Engineering, University of Tasmania, Launceston, TAS, Australia – sequence: 2 givenname: Pradeep orcidid: 0000-0001-6944-4775 surname: Jangir fullname: Jangir, Pradeep organization: Rajasthan Rajya Vidyut Prasaran Nigam, Sikar, India – sequence: 3 givenname: Ghanshyam G. orcidid: 0000-0001-9106-0313 surname: Tejani fullname: Tejani, Ghanshyam G. organization: Department of Mechanical Engineering, School of Technology, GSFC University, Vadodara, India – sequence: 4 givenname: Manoharan orcidid: 0000-0003-1032-4634 surname: Premkumar fullname: Premkumar, Manoharan email: mprem.me@gmail.com organization: Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, India – sequence: 5 givenname: Hassan Haes orcidid: 0000-0002-7427-2848 surname: Alhelou fullname: Alhelou, Hassan Haes email: alhelou@ieee.org organization: Faculty of Mechanical and Electrical Engineering, Tishreen University, Lattakia, Syria
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SubjectTerms	Algorithms Complexity Constraints optimization problems crowding distance Design optimization Energy levels Energy states Excitation Heuristic methods Ionization meta-heuristics Multiple objective analysis non-dominated sorting numerical optimization Optimization Pareto front Performance measurement Plasma Plasma (physics) Plasmas Search algorithms Search problems Slime Sorting Sorting algorithms structure optimization Symbiosis Trusses
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Title	MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems
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