Composite system reliability assessment using dynamically directed Particle Swarm Optimization

This paper presents an enhanced and improved particle swarm optimization (PSO) approach to overcome reliability-redundancy allocation problems in series, series-parallel, and complex systems. The problems mentioned above can be solved by increasing the overall system reliability and minimizing the system cost, weight, and volume. To achieve this with these nonlinear constraints, an approach is developed based on PSO. In particular, the inertia and acceleration coefficients of the classical particle swarm algorithm are improved by considering a normal distribution for the coefficients. The new expressions can enhance the global search ability in the initial stage, restrain premature convergence, and enable the algorithm to focus on the local fine search in the later stage, and this can enhance the perfection of the optimization process. Illustrative examples are provided as proof of the efficiency and effectiveness of the proposed approach. Results show that the overall system reliability is far better when compared with that of some approaches developed in previous studies for all three tested cases.

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An adaptive particle swarm optimization method for multi-objective system reliability optimization

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x19852814 ◽

2019 ◽

Vol 233 (6) ◽

pp. 990-1001 ◽

Cited By ~ 2

Author(s):

Mohamed Arezki Mellal ◽

Enrico Zio

Keyword(s):

Particle Swarm Optimization ◽

System Reliability ◽

Particle Swarm ◽

Superior Performance ◽

Reliability Optimization ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization ◽

Multi Objective ◽

Objective System ◽

System Reliability Optimization

Multi-objective system reliability optimization has attracted the attention of several researchers, due to its importance in industry. In practice, the optimization regards multiple objectives, for example, maximize the reliability, minimize the cost, weight, and volume. In this article, an adaptive particle swarm optimization is presented for multi-objective system reliability optimization. The approach uses a Lévy flight for some particles of the swarm, for avoiding local optima and insuring diversity in the exploration of the search space. The multi-objective problem is converted to a single-objective problem by resorting to the weighted-sum method and a penalty function is implemented to handle the constraints. Nine numerical case studies are presented as benchmark problems for comparison; the results show that the proposed approach has superior performance than a standard particle swarm optimization.

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Multiobjective optimization of reliability–redundancy allocation problems for serial parallel-series systems based on importance measure

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x19844785 ◽

2019 ◽

Vol 233 (5) ◽

pp. 881-897 ◽

Cited By ~ 2

Author(s):

Jiangbin Zhao ◽

Shubin Si ◽

Zhiqiang Cai ◽

Ming Su ◽

Wei Wang

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

System Reliability ◽

Particle Swarm Optimization Algorithm ◽

Harmony Search ◽

Particle Swarm ◽

Importance Measure ◽

Swarm Optimization ◽

Multiobjective Particle Swarm Optimization ◽

Design Cost

For complex equipment, designers are challenged to reduce the expense while satisfying high requirements of system reliability. This article focuses on the multiobjective reliability–redundancy allocation problem for serial parallel-series systems to balance the conflicts between system reliability and design cost. The multiobjective reliability–redundancy allocation problem model for serial parallel-series systems is established with constraints on system reliability and design cost. An importance measure–based and harmony search–based multiobjective particle swarm optimization algorithm is proposed to solve the multiobjective model effectively based on the importance measure–based harmony search and multiobjective particle swarm optimization algorithm. The performance of the importance measure–based and harmony search-based multiobjective particle swarm optimization algorithm is verified by comparison with the nondominated sorting genetic algorithm and importance measure–based multiobjective particle swarm optimization algorithm. In Experiment 1, the performance of the importance measure–based and harmony search-based multiobjective particle swarm optimization algorithm is better than that of the nondominated sorting genetic algorithm and importance measure–based multiobjective particle swarm optimization, and the importance measure–based and harmony search-based multiobjective particle swarm optimization algorithm also can get the Pareto front with better uniformity. Compared to the nondominated sorting genetic algorithm, four cases with different constraints of system reliability and design cost are considered in Experiment 2, and the importance measure–based and harmony search–based multiobjective particle swarm optimization algorithm applies to the systems with the lower system reliability constraints and the higher design cost constraints.

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