An efficient simulated annealing algorithm for the redundancy allocation problem with a choice of redundancy strategies

Chambari, Amirhossain; Najafi, Amir Abbas; Rahmati, Seyed Habib A.; Aida, Kô

doi:10.1016/j.ress.2013.05.016

Cited by 72 publications

(41 citation statements)

References 26 publications

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“…the ground state. This amounts to the global minimum point of energy function, theoretically, SA is a globally optimal algorithm [9]. The motivation of this paper is to improve the structure and performance of PSO, including convergence analysis, parameter selection and optimization, and combined with other algorithms.…”

Section: Introductionmentioning

confidence: 99%

A cooperative particle swarm optimization with constriction factor based on simulated annealing

Zhang

Wang

2018

Computing

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As many engineering optimization problems are rather complicated, it is usually necessary to search the optimal solution in a complex and huge search space. When faced with these large-scale problems, conventional optimization algorithms need to traverse the entire search space and it is impossible for them to finish the search within polynomial time. Moreover, it can't meet requirements in terms of computation velocity, convergence and sensitivity to initial value. So, it is very difficult to apply them to engineering optimization problems. Swarm intelligence methods simulate the collective behaviors of social creatures in the nature and they come from the relationship between the community formed by simple individuals and the environment as well as the interactions between the individuals. A single individual can only perform simple tasks, but the population formed by single individuals can fulfill complex tasks. Such intelligence presented by such population is called swarm intelligence. Due to the limitations of existing optimization algorithms, it is usually impractical to obtain excellent computational performance with only one optimization algorithm. In consideration of the jumping property of simulated annealing, it is not easy to get trapped into local minimum and it has strong local search capability near the optimal value and fast convergence velocity. This paper combines it with particle swarm optimization, proposes a cooperative particle swarm optimization with constriction factor based on simulated annealing (SA-CPSO), offers guidelines on selection of related parameters and dynamically adjusts the particle velocity according to its movement track. In this way, it improves the convergence velocity of the algorithm by improving the spatial search ability of the particle so as to make the particle accept the solution which makes the fitness of the objective function "better" as well as the solution that makes the said fitness "worse" at a certain probability during the flight of the particle. The experiment shows that the SA-CPSO improves the diversity of the particle and enhances its ability to get rid of locally optimal solutions. So, SA-CPSO is not easy to be trapped into local optimum and it has stronger ability of global optimization, a faster convergence velocity and higher convergence accuracy.

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Section: Introductionmentioning

confidence: 99%

A cooperative particle swarm optimization with constriction factor based on simulated annealing

Zhang

Wang

2018

Computing

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“…Safaei et al [20] studied the performance of a Particle Swarm Optimization (PSO) algorithm, named Annealing-based PSO (APSO), to solve RAP with multiple component choices. Moreover, Chambari et al [21] solved the problems in [12] using an e cient Simulated Annealing (SA) and compared the results with the ones obtained in [19]. Teimouri et al [22] presented an e cient Memory-Based Electromagnetismlike Mechanism (MBEM) to solve the RAP.…”

Section: Introductionmentioning

confidence: 99%

A bi-objective model to optimize reliability and cost of k-out-of-n series-parallel systems with tri-state components

et al. 2017

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Abstract. Redundancy Allocation Problem (RAP) is one way to increase system reliability. In most of the models developed so far for the RAP, system components are considered to have a binary state consisting of \working perfect" or \completely failed". However, to suit the real-world applications, this assumption has been relaxed in this paper, such that components can have three states. Moreover, a Bi-Objective RAP (BORAP) is modeled for a system with serial subsystems, in which non-repairable tri-state components of each subsystem are con gured in parallel and the subsystem works under k-out-of-n policy. Furthermore, to enhance system reliability, technical and organizational activities that can a ect failure rates of the components, and hence can improve the system performance are also taken into account. The aim is to nd the optimum number of redundant components in each subsystem, such that the system reliability is maximized while the cost is minimized within some real-world constraints. In order to solve the complicated NP-hard problem at hand, the multi-objective Strength Pareto Evolutionary Algorithm (SPEA-II) is employed. As there is no benchmark available, the Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to validate the results obtained. Finally, the performances of the algorithms are analyzed using 20 test problems.

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“…After the rst article by Fy e et al [5], in which they studied the redundancy allocation problem in seriesparallel systems, a great number of researchers have tried to develop this knowledge. The investigations of Coelho [6], Zou et al [7], Ramirez-Marquez and Coit [8], Nahas et al [9], Safaei et al [10], Liang and Chen [11], Ha and Kuo [12], Soltani et al [13], Liang et al [14], Juang et al [15], Zhang et al [16], and Chambari et al [17] are of those investigations into the redundancy allocation problem in series-parallel systems. For more information about the redundancy allocation problems, readers are referred to a work by Soltani [18].…”

Section: Introductionmentioning

confidence: 99%

Solving the redundancy allocation problem of k-out-of-n with non-exponential repairable components using optimization via simulation approach

2017

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Abstract. In this article, a new model and a novel solving method are provided to address the non-exponential redundancy allocation problem in series-parallel k-out-of-n systems with repairable components based on Optimization Via Simulation (OVS) technique. Despite the previous studies, in this model, the failure and repair times of each component were considered to have non-negative exponential distributions. This assumption makes the model closer to the reality where the majority of used components have greater chance to face a breakdown in comparison to new ones. The main objective of this research is the optimization of Mean Time to the First Failure (MTTFF) of the system via allocating the best redundant components to each subsystem. Since this objective function of the problem could not be explicitly mentioned, the simulation technique was applied to model the problem, and di erent experimental designs were produced using DOE methods. To solve the problem, some meta-Heuristic Algorithms were integrated with the simulation method. Several experiments were carried out to test the proposed approach; as a result, the proposed approach is much more real than previous models, and the near optimum solutions are also promising.

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An efficient simulated annealing algorithm for the redundancy allocation problem with a choice of redundancy strategies

Cited by 72 publications

References 26 publications

A cooperative particle swarm optimization with constriction factor based on simulated annealing

A cooperative particle swarm optimization with constriction factor based on simulated annealing

A bi-objective model to optimize reliability and cost of k-out-of-n series-parallel systems with tri-state components

Solving the redundancy allocation problem of k-out-of-n with non-exponential repairable components using optimization via simulation approach

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