Reliability optimization using hybrid genetic and particle swarm optimization algorithm

Q3 Decision Sciences Yugoslav Journal of Operations Research Pub Date : 2022-01-01 DOI:10.2298/yjor220316020d
Tripti Dahiya, D. Garg
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引用次数: 2

Abstract

Redundancy-allocation problem i.e. RAP is among the reliability optimization problems which make use of non-linear programming method to improve the reliability of complex system. The objective of this research paper is reliability optimization through the application of Genetic Algorithm i.e. GA and Hybrid Genetic & Particle Swarm Optimization (H-GAPSO) on a RAP. Certain shortcomings have been seen when results are obtained by application of single algorithms. In order to get rid of these shortcomings, HGA-PSO is introduced where attractive properties of GA and PSO are combined. This hybrid method makes use of iterative process of GA after obtaining initial best population from PSO. Comparative Analysis of results of GA and H-GAPSO is done with respect to reliability and computation (CPU) time and it is observed that H-GAPSO improved system reliability up to maximum by 63.10%. MATLprogramming has been used for computation of results from GA and HGA-PSO algorithms.
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基于混合遗传和粒子群算法的可靠性优化
冗余分配问题(RAP)是利用非线性规划方法提高复杂系统可靠性的可靠性优化问题之一。本文的研究目标是通过遗传算法(GA)和混合遗传与粒子群算法(H-GAPSO)在RAP上的应用进行可靠性优化。应用单一算法得到的结果存在一定的缺陷。为了克服这些缺点,将遗传算法和粒子群算法的优点结合起来,引入了遗传算法-粒子群算法。该混合方法利用遗传算法从粒子群中得到初始最优种群后的迭代过程。通过对遗传算法和H-GAPSO算法在可靠性和计算时间方面的比较分析,发现H-GAPSO算法最大可提高系统可靠性63.10%。用matlab编程对遗传算法和HGA-PSO算法的结果进行了计算。
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来源期刊
Yugoslav Journal of Operations Research
Yugoslav Journal of Operations Research Decision Sciences-Management Science and Operations Research
CiteScore
2.50
自引率
0.00%
发文量
14
审稿时长
24 weeks
期刊最新文献
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