Simplified Swarm Optimization for Bi-Objection Active Reliability Redundancy Allocation Problems

• URL: http://arxiv.org/abs/2006.09844v1
• Date: Wed, 17 Jun 2020 13:15:44 GMT
• Title: Simplified Swarm Optimization for Bi-Objection Active Reliability Redundancy Allocation Problems
• Authors: Wei-Chang Yeh
• Abstract summary: The reliability redundancy allocation problem (RRAP) is a well-known problem in system design, development, and management. In this study, a bi-objective RRAP is formulated by changing the cost constraint as a new goal. To solve the proposed problem, a new simplified swarm optimization (SSO) with a penalty function, a real one-type solution structure, a number-based self-adaptive new update mechanism, a constrained non-dominated solution selection, and a new pBest replacement policy is developed.
• Score: 1.5990720051907859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The reliability redundancy allocation problem (RRAP) is a well-known tool in system design, development, and management. The RRAP is always modeled as a nonlinear mixed-integer non-deterministic polynomial-time hardness (NP-hard) problem. To maximize the system reliability, the integer (component active redundancy level) and real variables (component reliability) must be determined to ensure that the cost limit and some nonlinear constraints are satisfied. In this study, a bi-objective RRAP is formulated by changing the cost constraint as a new goal, because it is necessary to balance the reliability and cost impact for the entire system in practical applications. To solve the proposed problem, a new simplified swarm optimization (SSO) with a penalty function, a real one-type solution structure, a number-based self-adaptive new update mechanism, a constrained nondominated-solution selection, and a new pBest replacement policy is developed in terms of these structures selected from full-factorial design to find the Pareto solutions efficiently and effectively. The proposed SSO outperforms several metaheuristic state-of-the-art algorithms, e.g., nondominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO), according to experimental results for four benchmark problems involving the bi-objective active RRAP.

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