A knowledge-based archive multi-objective simulated annealing algorithm to optimize series–parallel system with choice of redundancy strategies

Zaretalab, Arash; Hajipour, Vahid; Sharifi, Mani; Shahriari, Mohammadreza

doi:10.1016/j.cie.2014.11.008

Cited by 59 publications

(17 citation statements)

References 43 publications

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“…Meanwhile, Safari [41] also proposed a variant of the Nondominated Sorting Genetic Algorithm (NSGA-II) to solve a novel mathematical model for multi-objective redundancy allocation problems (MORAP) with a choice of redundancy strategies. Recently, an efficient knowledge-based archive multiobjective simulated annealing (KBAMOSA) [42] was developed to solve the above MORAP. KBAMOSA applies a memory matrix to reinforce the neighborhood structure to achieve better quality solutions.…”

Section: Introductionmentioning

confidence: 99%

Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization

Kong

Gao

Ouyang

et al. 2015

Reliability Engineering & System Safety

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Section: Introductionmentioning

confidence: 99%

Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization

Kong

Gao

Ouyang

et al. 2015

Reliability Engineering & System Safety

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“…[36]2015MultiHomosCE-NRGAMultipleTaguchiConstantZaretalab et al. [37]2015MultiHomosMOSAMultiple-ConstantCurrent Research---MultiHomosNSGA-II/NRGASingleRSMConstant…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Redundancy Allocation Problem with weighted-k-out-of-n subsystems

Sharifi

Moghaddam

Shahriari

2019

Heliyon

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Redundancy Allocation Problem (RAP) is one of the most practical problems in the reliability area. Many assumptions have been added to RAP in recent years. The aim was to better represent real-world problems with RAP. One of these assumptions is considering weighted-k-out-of-n sub-systems. This method has been used to model various systems like power and hydro transitions systems. In this paper, we present a new multi-objective RAP (MORAP) model for optimizing the reliability and cost of the weighted-k-out-of-n parallel systems. In our model, the sub-systems are considered as weighted-k-out-of-n. Also, we use the universal generating function and adapt this technique to obtain an exact formula to calculate each sub-system reliability. Since RAP belongs to NP-hard class of problems, we decided to employ the non-dominated sorting genetic algorithm and non-dominated ranked genetic algorithm. Several criteria were used to compare the result of these two algorithms.

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“…In the past, the multiobjective RRAP has been solved by various multiobjective heuristic algorithms. [22][23][24][25][26][27] Cao et al 28 proposed a decomposition method for serial-parallel systems, in which the original problem is divided into several multiobjective subproblems, and the optimal solution can be obtained systematically based on the solution of subproblems. Khalili-Damghani et al 29 proposed a decision support system that can reduce the dimension and obtain the Pareto front using a nondominated sorting method and data envelopment analysis.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Cai

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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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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A knowledge-based archive multi-objective simulated annealing algorithm to optimize series–parallel system with choice of redundancy strategies

Cited by 59 publications

References 43 publications

Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization

Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization

Multi-objective Redundancy Allocation Problem with weighted-k-out-of-n subsystems

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

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