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

Mellal, Mohamed Arezki; Zio, Enrico

doi:10.1177/1748006x19852814

Cited by 26 publications

(19 citation statements)

References 41 publications

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“…The BAS algorithm is similar to other optimization algorithms, like the ant colony optimization (ACO) algorithm [21], the particle swarm optimization (PSO) algorithm [22], the cuckoo search optimization (CSO) algorithm [23], etc. However, it can intelligently and efficiently search without knowing the specific form and gradient information of the function.…”

Section: Of 17mentioning

confidence: 99%

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

2020

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In order to improve the accuracy of wind power prediction and ensure the effective utilization of wind energy, a short-term wind power prediction model based on variational mode decomposition (VMD) and an extreme learning machine (ELM) optimized by an improved grey wolf optimization (GWO) algorithm is proposed. The original wind power sequence is decomposed into series of modal components with different center frequencies by the VMD method and some new sequences are obtained by phase space reconstruction (PSR). Then, the ELM model is established for different new time series, and the improved GWO algorithm is used to optimize its parameters. Finally, the output results are weighted and merged as the final predicted value of wind power. The root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the proposed VMD-improved GWO-ELM prediction model in the paper are 5.9113%, 4.6219%, and 13.01% respectively, which are better than these of ELM, back propagation (BP), and the improved GWO-ELM model. The simulation results show that the proposed model has higher prediction accuracy than other models in short-term wind power prediction.

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Section: Of 17mentioning

confidence: 99%

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

2020

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“…An adaptive PSO was designed and applied into the reliability optimization of multi-objective system [42]. And a neighborhood fuzzy PSO was used to optimized a novel neural network for forecasting of software reliability [43].…”

Section: Introductionmentioning

confidence: 99%

Team Collaboration Particle Swarm Optimization and Its Application on Reliability Optimization

Zheng¹,

Ma²,

Zhang³

et al. 2021

IJCIS

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Particle swarm optimization (PSO) tends to be premature convergence due to easily trapping into local suboptimal areas. In order to overcome the PSO's defects, the reasons causing the defects are analyzed and summarized as population diversity deficiency, insufficient information sharing, unbalance of exploitation and exploration, and single update strategy. On this basis, inspired by human team collaboration behavior, a team collaboration particle swarm optimization (TCPSO) is proposed. Diversified updates strategies, dynamic grouping strategy, selectivity vector, and decreasing and increasing inertia weight are designed in TCPSO to solve the defects' reasons and improve the optimization performance. Eight typical test functions have been used to evaluate and compare the performance of different PSO variants, and the results have been proven that the optimal results found by TCPSO are better compared with other PSO variants, which demonstrates the rationality and effectiveness of TCPSO. Finally, a real-world problem for reliability optimization are solved by five algorithms, and the results prove the convergence rate and stable optimization performance of TCPSO, TCPSO can provide better support for reliability optimization of complex system.

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“…Industrial plants consist of many components and their system dependability (RAMS ? C: reliability, availability, maintainability, safety, and cost) should be optimized [1][2][3][4][5]. In the last few decades, efforts have been devoted to the question of optimal allocation in system dependability.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective System Design Optimization via PPA and a Fuzzy Method

Mellal

Salhi

2021

Int. J. Fuzzy Syst.

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System design deals with various challenges of targets and resources, such as reliability, availability, maintainability, cost, weight, volume, and configuration. This paper deals with the multi-objective system availability and cost optimization of parallel–series systems by resorting to the multi-objective strawberry algorithm also known as the Plant Propagation Algorithm or PPA and a fuzzy method. It is the first implementation of this optimization algorithm in the literature for this kind of problem to generate the Pareto Front. The fuzzy method allows helping the decision maker to select the best compromise solution. A numerical case study involving 10 subsystems highlights the applicability of the proposed approach.

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

Cited by 26 publications

References 41 publications

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

Team Collaboration Particle Swarm Optimization and Its Application on Reliability Optimization

Multi-objective System Design Optimization via PPA and a Fuzzy Method

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