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MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems
DOI:10.1109/ACCESS.2021.3087739.png)
摘要
En 中文
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.
Keyword:
Optimization
Plasmas
Energy states
Sorting
Ionization
Symbiosis
Search problems
Constraints optimization problems
crowding distance
meta-heuristics
non-dominated sorting
numerical optimization
Pareto front
structure optimization
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
MOGBO: A new Multiobjective Gradient-Based Optimizer for real-world structural optimization problems
Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems多目标蚁狮优化器: 一种求解工程问题的多目标优化算法
APPLIED INTELLIGENCE
IF3.5


