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Neural Computing and Applications

Erschienen in:

01.11.2014 | Original Article

A chaotic-based big bang–big crunch algorithm for solving global optimisation problems

verfasst von: A. Rezaee Jordehi

Erschienen in: Neural Computing and Applications | Ausgabe 6/2014

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Abstract

Big bang–big crunch (BBBC) algorithm is a fairly novel gradient-free optimisation algorithm. It is based on theories of evolution of the universe, namely the big bang and big crunch theory. The big challenge in BBBC is that it is easily trapped in local optima. In this paper, chaotic-based strategies are incorporated into BBBC to tackle this challenge. Five various chaotic-based BBBC strategies with three different chaotic map functions are investigated and the best one is selected as the proposed chaotic strategy for BBBC. The results of applying the proposed chaotic BBBC to different unimodal and multimodal benchmark functions vividly show that chaotic-based BBBC yields quality solutions. It significantly outperforms conventional BBBC, cuckoo search optimisation and gravitational search algorithm.

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Bashiri M (2014) Optimal scheduling of distributed energy resources in a distribution system based on imperialist competitive algorithm considering reliability worth. Neural Comput Appl 1–8

Huang H, Lin W, Lin Z, Hao Z, Lim A (2014) An evolutionary algorithm based on constraint set partitioning for nurse rostering problems. Neural Comput Appl 1–13

Ouaarab A, Ahiod B, Yang X-S (2013) Discrete cuckoo search algorithm for the travelling salesman problem. Neural comput Appl 1–11

Jordehi AR, Joorabian M (2011) Optimal placement of multi-type FACTS devices in power systems using evolution strategies. Power Engineering and Optimization Conference (PEOCO), 2011 5th International: IEEE, pp 352–357

Jordehi AR, Jasni J (2011) A comprehensive review on methods for solving FACTS optimization problem in power systems. Int Rev Electr Eng 6:1916–1926

Jordehi AR, Jasni J (2012) Approaches for FACTS optimization problem in power systems. Power Engineering and Optimization Conference (PEDCO) Melaka, Malaysia, 2012 IEEE International: IEEE, pp 355–360

Jordehi R (2011) Heuristic methods for solution of FACTS optimization problem in power systems. IEEE Student Conference on Research and Development, pp 30–35

Rezaee Jordehi A, Jasni J, Abdul Wahab NI, Kadir A, Abidin MZ (2013) Particle swarm optimisation applications in FACTS optimisation problem. Power Engineering and Optimization Conference (PEOCO), IEEE 7th International: IEEE, pp 193–198

Erol OK, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37:106–111CrossRef

10.

Rezaee Jordehi A, Jasni J (2013) Parameter selection in particle swarm optimisation: a survey. J Exp Theor Artif Intell 25:527–542CrossRef

11.

Jordehi AR, Jasni J (2013) Particle swarm optimisation for discrete optimisation problems: a review. Artif Intell Rev 1–16

12.

Tavazoei MS, Haeri M (2007) Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms. Appl Math Comput 187:1076–1085MathSciNetCrossRefMATH

13.

Talatahari S, Farahmand Azar B, Sheikholeslami R, Gandomi A (2012) Imperialist competitive algorithm combined with chaos for global optimization. Commun Nonlinear Sci Numer Simul 17:1312–1319MathSciNetCrossRefMATH

14.

Jiang C, Ma Y, Wang C (2006) PID controller parameters optimization of hydro-turbine governing systems using deterministic-chaotic-mutation evolutionary programming (DCMEP). Energy Convers Manag 47:1222–1230CrossRef

15.

Coelho LS (2009) Reliability–redundancy optimization by means of a chaotic differential evolution approach. Chaos Solitons Fractals 41:594–602CrossRefMATH

16.

Coelho LS, Lee C-S (2008) Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches. Int J Electr Power Energy Syst 30:297–307CrossRef

17.

Pan Q-K, Wang L, Gao L (2011) A chaotic harmony search algorithm for the flow shop scheduling problem with limited buffers. Appl Soft Comput 11:5270–5280CrossRef

18.

Cai J, Ma X, Li Q, Li L, Peng H (2010) A multi-objective chaotic ant swarm optimization for environmental/economic dispatch. Int J Electr Power Energy Syst 32:337–344CrossRef

19.

Alatas B (2011) Uniform big bang–chaotic big crunch optimization. Commun Nonlinear Sci Numer Simul 16:3696–3703CrossRefMATH

20.

Talatahari S, Kaveh A, Sheikholeslami R (2011) An efficient charged system search using chaos for global optimization problems. Int J Optim Civil Eng 2:305–325

21.

Alatas B (2010) Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 37:5682–5687CrossRef

22.

Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. Evolutionary Computation, 1999 CEC 99. Proceedings of the 1999 Congress on: IEEE

23.

Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188:1567–1579MathSciNetCrossRefMATH

24.

Yang X-S, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1:330–343MATH

25.

Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248CrossRefMATH

Titel: A chaotic-based big bang–big crunch algorithm for solving global optimisation problems
verfasst von: A. Rezaee Jordehi
Publikationsdatum: 01.11.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 6/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1613-1

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