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A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems

机译：解决可靠性冗余分配问题的多目标粒子群算法

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摘要

In this paper, a new dynamic self-adaptive multi-objective particle swarm optimization (DSAMOPSO) method is proposed to solve binary-state multi-objective reliability redundancy allocation problems (MORAPs). A combination of penalty function and modification strategies is used to handle the constraints in the MORAPs. A dynamic self-adaptive penalty function strategy is utilized to handle the constraints. A heuristic cost-benefit ratio is also supplied to modify the structure of violated swarms. An adaptive survey is conducted using several test problems to illustrate the performance of the proposed DSAMOPSO method. An efficient version of the epsilon-constraint (AUGMECON) method, a modified non-dominated sorting genetic algorithm (NSGA-II) method, and a customized time-variant multi-objective particle swarm optimization (cTV-MOPSO) method are used to generate non-dominated solutions for the test problems. Several properties of the DSAMOPSO method, such as fast-ranking, evolutionary-based operators, elitism, crowding distance, dynamic parameter tuning, and tournament global best selection, improved the best known solutions of the benchmark cases of the MORAP. Moreover, different accuracy and diversity metrics illustrated the relative preference of the DSAMOPSO method over the competing approaches in the literature.
机译：为了解决二态多目标可靠性冗余分配问题，提出了一种新的动态自适应多目标粒子群优化算法（DSAMOPSO）。惩罚函数和修改策略的组合用于处理MORAP中的约束。动态自适应惩罚函数策略用于处理约束。还提供了启发式成本效益比来修改受侵害群体的结构。使用几个测试问题进行了自适应调查，以说明所提出的DSAMOPSO方法的性能。高效的epsilon约束（AUGMECON）方法，改进的非主导排序遗传算法（NSGA-II）方法和定制的时变多目标粒子群优化（cTV-MOPSO）方法用于生成针对测试问题的非主导解决方案。 DSAMOPSO方法的一些属性，例如快速排序，基于进化的算子，精英，拥挤距离，动态参数调整和锦标赛全局最佳选择，改善了MORAP基准案例的最佳解决方案。此外，不同的准确性和多样性指标说明了DSAMOPSO方法相对于文献中竞争方法的相对偏爱。

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