Abstract
The social foraging behavior of Escherichia coli bacteria has been recently used for solving complex real-world search and optimization problems. Bacterial foraging optimization algorithm (BFOA) is an important global optimization method inspired from this behavior. In this paper, a novel method called chemotaxis differential evolution optimization algorithm (CDEOA), which augments BFOA with conditional introduction of differential evolution (DE) and Random Search operators, is proposed. Introduction of these operators is done considering the number of successful run and unsuccessful tumble steps of bacteria. CDEOA was compared with the classical BFOA, two variants of BFOA which use DE operators [Adaptive Chemotactic Bacterial Swarm Foraging Optimization with Differential Evolution Strategy (ACBSFO_DES)], chemotaxis differential evolution (CDE), and the classical DE on all 30 numerical functions of the 2014 Congress on Evolutionary Computation (CEC 2014) Special Session and Competition on Single Objective Real Parameter Numerical Optimization suite. CDEOA was also compared with four state-of-the-art DE variants that competed in CEC 2014. Statistics of the computer simulations over this benchmark suite indicate that CDEOA outperforms, or is comparable to, its competitors in terms of the quality of final solution and its convergence rates for high-dimensional problems.
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Notes
Note that swim and run can be used interchangeably in the literature of BFOA.
References
Abraham A et al (2008) Analysis of reproduction operator in bacterial foraging optimization algorithm. In: Evolutionary computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on. IEEE, pp 1476–1483
Bäck T, Schwefel HP (1993) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1(1):1–23. doi:10.1162/evco.1993.1.1.1. ISSN: 1063–6560
Biswas A et al (2007b) Synergy of PSO and bacterial foraging optimization—a comparative study on numerical benchmarks. In: Innovations in hybrid intelligent systems. Springer, Berlin, pp 255–263
Biswas A et al (2007a) A synergy of differential evolution and bacterial foraging optimization for global optimization. Neural Netw World 17(6):607
Biswas A et al (2010) Stability analysis of the reproduction operator in bacterial foraging optimization. Theor Comput Sci 411(21):2127–2139. doi:10.1016/j.tcs.2010.03.005. ISSN: 03043975
Črepinšek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(3):1–33. doi:10.1145/2480741.2480752. ISSN: 03600300
Das S et al (2009) On stability of the chemotactic dynamics in bacterial-foraging optimization algorithm. IEEE Trans Syst Man Cybern Part A Syst Humans 39(3):670–679. doi:10.1109/TSMCA.2008.2011474. ISSN: 1083–4427
Dasgupta S et al (2009) Adaptive computational chemotaxis in bacterial foraging optimization: an analysis. IEEE Trans Evol Comput 13(4):919–941. doi:10.1109/TEVC.2009.2021982 Issn: 1089–778X
De Jong KA, Spears WM (1992) A formal analysis of the role of multi-point crossover in genetic algorithms. Ann Math Artif Intell 5(1):1–26
Eiben AE, Schippers CA (1998) On evolutionary exploration and exploitation. Fundam Informaticae 35(1):35–50
Hu Z, Bao Y, Xiong T (2014) Partial opposition-based adaptive differential evolution algorithms: evaluation on the CEC 2014 benchmark set for real-parameter optimization. In: Evolutionary computation (CEC), 2014 IEEE Congress on. IEEE, pp 2259–2265
Jarraya Y et al (2013) A hybrid computational chemotaxis in bacterial foraging optimization algorithm for global numerical optimization. In: Cybernetics (CYBCONF), 2013 IEEE international conference on IEEE, pp 213–218
Kennedy J (2011) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, US, pp 760–766 Isbn: 978-0-387-30768-8, 978-0-387-30164-8
Kim DH, Cho JH (2005) Bacterial foraging based neural network fuzzy learning. In: Proc in IICAI, pp 2030–2036
Kim DH, Abraham A, Cho JH (2007) A hybrid genetic algorithm and bacterial foraging approach for global optimization. Inf Sci 177(18):3918–3937
Korani WM, Dorrah HT, Emara HM (2009) Bacterial foraging oriented by particle swarm optimization strategy for PID tuning. In: Computational intelligence in robotics and automation (CIRA), 2009 IEEE international symposium on IEEE, pp 445–450
Li Z et al (2014) Differential evolution strategy based on the constraint of fitness values classification. In: Evolutionary computation (CEC), 2014 IEEE Congress on IEEE, pp 1454–1460
Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. In: Computational intelligence laboratory
Liu Y, Passino K (2002) Biomimicry of social foraging bacteria for distributed optimization: models, principles, and emergent behaviors. J Optim Theory Appl 115(3):603–628. doi:10.1023/A:1021207331209 Issn: 0022-3239
Luke S (2009) Essentials of Metaheuristics. Available for free at http://cs.gmu.edu/~sean/book/metaheuristics/. Lulu
Mishra S (2005) A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation. Evol Comput IEEE Trans 9(1):61– 73
Mishra S, Bhende CN (2007) Bacterial foraging technique-based optimized active power filter for load compensation. Power Deliv IEEE Trans 22(1):457–465
Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. Control Syst IEEE 22(3):52–67
Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57. doi:10.1007/s11721-007-0002-0
Qin AK, Li X (2013) Differential evolution on the CEC-2013 single-objective continuous optimization testbed. In: Evolutionary computation (CEC), 2013 IEEE Congress on IEEE, pp 1099–1106
Qu BY et al (2014) Memetic differential evolution based on fitness Euclidean-distance ratio. In: Evolutionary computation (CEC), 2014 IEEE Congress on IEEE, pp 2266–2273
Storn R (1996) On the usage of differential evolution for function optimization. In: Fuzzy information processing society, 1996. NAFIPS., 1996 Biennial Conference of the North American. IEEE, pp 519–523
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Tripathy M et al (2006) Transmission loss reduction based on FACTS and bacteria foraging algorithm. In: Parallel problem solving from nature-PPSN IX. Springer, Brelin, pp 222–231
Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85. doi:10.1007/BF00175354
Yu C et al (2014) Fireworks algorithm with differential mutation for solving the cec 2014 competition problems. In: Evolutionary computation (CEC), 2014 IEEE Congress on IEEE, pp 3238–3245
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Communicated by E. Lughofer.
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Yıldız, Y.E., Altun, O. Hybrid achievement oriented computational chemotaxis in bacterial foraging optimization: a comparative study on numerical benchmark. Soft Comput 19, 3647–3663 (2015). https://doi.org/10.1007/s00500-015-1687-4
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DOI: https://doi.org/10.1007/s00500-015-1687-4