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

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.