Hybrid particle swarm optimization with differential evolution and chaotic local search to solve reliability-redundancy allocation problems

Abstract

In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization (PSO) with differential evolution (DE) and a new chaotic local search. In the CDEPSO algorithm, DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO, while the best solution in the PSO is performed by chaotic local search. To investigate the performance of CDEPSO, four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO (a hybrid algorithm which only combines PSO with chaotic local search). And, compared with the other six improved meta-heuristics, CDEPSO also exhibits more robust performance. In addition, a new performance was proposed to more fairly compare CDEPSO with the same six improved meta-heuristics, and CDEPSO algorithm is the best in solving these problems.

Keywords

particle swarm optimization / differential evolution / chaotic local search / reliability-redundancy allocation

References

[1] MisraK B, LjubojevicM D. Optimal reliability design of a system: A new look [J]. IEEE Transaction on Reliability, 1973, 22(5): 255-258

[2] LiD, SunX L, MckinnonK. An exact solution method for reliability optimization in complex systems [J]. Annals of Operations Research, 2005, 133(1/4): 129-148

[3] BillionnetA. Redundancy allocation for series-parallel systems using integer linear programming [J]. IEEE Transaction on Reliability, 2008, 57(3): 507-516

[4] AgarwalM, GuptaR. Penalty function approach in heuristic algorithms for constrained redundancy reliability optimization [J]. IEEE Transaction on Reliability, 2005, 54(3): 549-558

[5] HaC, KuoW. Multi-path approach for reliability-redundancy allocation using a scaling method [J]. Journal of Heuristics, 2005, 11(3): 201-217

[6] Tavakkoli-moghaddamR, SafariJ, SassaniF. Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm [J]. Reliability Engineering and System Safety, 2008, 93(4): 550-556

[7] TianZ G, LevitinG, ZuoM J. A joint reliability-redundancy optimization approach for multi-state series-parallel system [J]. Reliability Engineering and System Safety, 2009, 94(10): 1568-1576

[8] AhmadizarF, SoltanpanahH. Reliability optimization of a series system with multiple-choice and budget constraints using an efficient ant colony approach [J]. Expert Systems with Applications, 2011, 38(4): 3640-3646

[9] ChenT C. IAs based approach for reliability redundancy allocation problems [J]. Applied Mathematics and Computation, 2006, 182(2): 1556-1567

[10] WuP F, GaoL Q, ZouD X, LiS. An improved particle swarm optimization algorithm for reliability problems [J]. ISA Transactions, 2011, 50(1): 71-81

[11] CoelhoL S. Reliability-redundancy optimization by means of a chaotic differential evolution approach [J]. Chaos, Solitons and Fractals, 2009, 41(2): 594-602

[12] CoelhoL S, BernertD L A, MarianiV CAdaptation, learning, and optimization [M], 2010BerlinSpringer503-516

[13] WangL, LiL P. A coevolutionary differential evolution with harmony search for reliability-redundancy optimization [J]. Expert Systems with Applications, 2012, 39(5): 5271-5278

[14] Tavakkoli-MoghaddamR, SafariJ, SassaniF. Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm [J]. Reliability Engineering and System Safety, 2008, 93(4): 550-556

[15] GuptaR K, BhuniaA K, RoyD. A GA based penalty function technique for solving constrained redundancy allocation problem of series system with interval valued reliability of components [J]. Journal of Computational and Applied Mathematics, 2009, 232(2): 275-284

[16] NahasN, NourelfathM, Ait-kadiD. Coupling ant colony and the degraded ceiling algorithm for the redundancy allocation problem of series-parallel systems [J]. Reliability Engineering and System Safety, 2007, 92(2): 211-222

[17] OuzinebM, NourelfathM, GendreauM. Tabu search for the redundancy allocation problem of homogenous series-parallel multi-state systems [J]. Reliability Engineering and System Safety, 2008, 93(8): 1257-1272

[18] AlatasB, AkinE, OzerA B. Chaos embedded particle swarm optimization algorithms [J]. Chaos, Solitons and Fractals, 2009, 40(4): 1715-1734

[19] MoslehiG, MahnamM. A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search [J]. Int J Production Economics, 2011, 129(1): 14-22

[20] FeiS W, WangM J, MiaoY B, TuJ, LiuC L. Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil [J]. Energy Conversion and Management, 2009, 50(6): 1604-1609

[21] DavendraD, ZelinkaI, SenkerikR. Chaos driven evolutionary algorithms for the task of PID control [J]. Computers and Mathematics with Applications, 2010, 60(4): 1088-1104

[22] WangL F, SinghC. Reserve-constrained multiarea environmental/economic dispatch based on particle swarm optimization with local search [J]. Engineering Applications of Artificial Intelligence, 2009, 22(2): 298-307

[23] AcharjeeP, GoswamiS K. Chaotic particle swarm optimization based robust load flow [J]. Electrical Power and Energy Systems, 2010, 32(2): 141-146

[24] CoelhoL S. Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach [J]. Chaos, Solitons and Fractals, 2009, 39(4): 1504-1514

[25] WangJ Z, ZhuS L, ZhaoW G, ZhuW J. Optimal parameters estimation and input subset for grey model based on chaotic particle swarm optimization algorithm [J]. Expert Systems with Applications, 2011, 38(7): 8151-8158

[26] TanY, TanG Z, WuX D. Hybrid real-coded genetic algorithm with chaotic local search for global optimization [J]. Journal of Information & Computational Science, 2011, 8(14): 3171-3179

[27] AliW M, HegazyZ S. Constrained optimization based on modified differential evolution algorithm [J]. Information Sciences, 2012, 194(1): 171-208

[28] LiuH, CaiZ X, WangY. Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization [J]. Applied Soft Computing, 2010, 10(2): 629-640

[29] ZouD X, GaoL Q, WuJ H, LiS, LiY. A novel global harmony search algorithm for reliability problems [J]. Computers & Industrial Engineering, 2010, 58(2): 307-316

[30] CoelhoL S. An efficient particle swarm approach for mixed-integer programming in reliability-redundancy optimization applications [J]. Reliability Engineering and System Safety, 2009, 94(4): 830-837