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A ranking-based adaptive artificial bee colony algorithm for global numerical optimization. (English) Zbl 1435.90152

Summary: The artificial bee colony (ABC) algorithm is a powerful population-based metaheuristic for global numerical optimization and has been shown to compete with other swarm-based algorithms. However, ABC suffers from a slow convergence speed. To address this issue, the natural phenomenon in which good individuals always have good genes and thus should have more opportunities to generate offspring is the inspiration for this paper. We propose a ranking-based adaptive ABC algorithm (ARABC). Specifically, in ARABC, food sources are selected by bees to search, and the parent food sources used in the solution search equation are all chosen based on their rankings. The higher a food source is ranked, the more opportunities it will have to be selected. Moreover, the selection probability of the food source is based on the corresponding ranking, which is adaptively adjusted according to the status of the population evolution. To evaluate the performance of ARABC, we compare ARABC with other ABC variants and state-of-the-art differential evolution and particle swarm optimization algorithms based on a number of benchmark functions. The experimental results show that ARABC is significantly better than the algorithms to which it was compared.

MSC:

90C59 Approximation methods and heuristics in mathematical programming
68W50 Evolutionary algorithms, genetic algorithms (computational aspects)
90C26 Nonconvex programming, global optimization

Software:

JADE; ABC; PS-MEABC
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Full Text: DOI

References:

[1] Babaoglu, I., Artificial bee colony algorithm with distribution-based update rule, Appl. Soft Comput., 34, 851-861 (2015)
[2] Banitalebi, A.; Aziz, M. I.A.; Bahar, A.; Aziz, Z. A., Enhanced compact artificial bee colony, Inform. Sci., 298, 491-511 (2015)
[3] Cai, Y. Q.; Wang, J. H., Differential evolution with neighborhood and direction information for numerical optimization, IEEE Trans. Cybern., 43, 6, 2202-2215 (2013)
[4] Caraffini, F.; Neri, F.; Picinali, L., An analysis on separability for memetic computing automatic design, Inform. Sci., 265, 1-22 (2014)
[5] Chang, W. L.; Zeng, D. Z.; Chen, R. C., An artificial bee colony algorithm for data collection path planning in sparse wireless sensor networks, Int. J. Mach. Learn. Cyber., 6, 3, 375-383 (2015)
[6] Cheng, J.; Zhang, G.; Caraffini, F.; Neri, F., Multicriteria adaptive differential evolution for global numerical optimization, Integr. Comput. Aid. E, 22, 2, 103-117 (2015)
[7] Coelho, L. S.; Alotto, P., Gaussian artificial bee colony algorithm approach applied to Loney’s solenoid benchmark problem, IEEE Trans. Magn., 47, 5, 1326-1329 (2011)
[8] Cui, L. Z.; Li, G. H.; Lin, Q. Z.; Chen, J. Y.; Lu, N., Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations, Comput. Oper. Res., 67, 155-173 (2016) · Zbl 1349.90852
[9] Cui, L. Z.; Li, G. H.; Lin, Q. Z.; Du, Z. H.; Gao, W. F.; Chen, J. Y.; Lu, N., A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation, Inform. Sci., 367-368, 1012-1044 (2016)
[10] Das, S.; Abraham, A.; Chakraborty, U. K.; Konar, A., Differential evolution using a neighborhood-based mutation operator, IEEE Trans. Evol. Comput., 13, 3, 526-553 (2009)
[11] Diwold, K.; Aderhold, A.; Scheidler, A.; Middendorf, M., Performance evaluation of artificial bee colony optimization and new selection schemes, Memetic Comput., 3, 3, 149-162 (2011)
[12] Gao, W. F.; Chan, F. T.S.; Huang, L. L.; Liu, S. Y., Bare bones artificial bee colony algorithm with parameter adaptation and fitness-based neighborhood, Infrom. Sci., 316, 180-200 (2015)
[13] Gao, W. F.; Huang, L. L.; Liu, S. Y.; Chan, F. T.S.; Dai, C., Artificial bee colony algorithm with multiple search strategies, Appl. Math. Comput., 271, 269-287 (2015) · Zbl 1410.90262
[14] Gao, W. F.; Liu, S. Y.; Huang, L. L., A global best artificial bee colony algorithm for global optimization, J. Comput. Appl. Math., 236, 11, 2741-2753 (2012) · Zbl 1241.65058
[15] Gao, W. F.; Liu, S. Y.; Huang, L. L., A novel artificial bee colony algorithm based on modified search equation and orthogonal learning, IEEE Trans. Cybern., 43, 3, 1011-1024 (2013)
[16] Gao, W. F.; Liu, S. Y.; Huang, L. L., Enhancing artificial bee colony algorithm using more information-based search equations, Inform. Sci., 270, 112-133 (2014) · Zbl 1341.68201
[17] Gong, W. Y.; Cai, Z. H., Differential evolution with ranking-based mutation operators, IEEE Trans. Cybern., 43, 6, 2066-2081 (2013)
[18] Hu, Y. H.; Sim, CK.; Yang, X. Q., A subgradient method based on gradient sampling for solving convex optimization problems, Numer. Func. Anal. Opt., 36, 12, 1559-1584 (2015) · Zbl 1333.90093
[19] Hu, Y. H.; Yu, C. K.W.; Li, C., Stochastic subgradient method for quasi-convex optimization problems, J. Nonlinear Convex A., 174, 4, 711-724 (2016) · Zbl 1339.65077
[20] Jadon, S. S.; Bansal, J. C.; Tiwari, R.; Sharma, H., Accelerating artificial bee colony algorithm with adaptive local search, Memetic. Comp., 7, 215-230 (2015)
[21] Kang, F.; Li, J. J., Artificial bee colony algorithm with local search for numerical optimization, J. Softw., 6, 3, 490-497 (2011)
[22] Kang, F.; Li, J. J.; Li, H. J., Artificial bee colony algorithm and pattern search hybridized for global optimization, Appl. Soft Comput., 13, 4, 1781-1791 (2013)
[23] Karaboga, D., An Idea Based On Honey Bee Swarm For Numerical Optimization (2005), Department of Computer Science, Erciyes Univ.: Department of Computer Science, Erciyes Univ. Kayseri, Turkey, Tech. Rep. TR06, Oct.
[24] Karaboga, D.; Basturk, B., A comparative study of artificial bee colony algorithm, Appl. Math. Comput., 214, 1, 108-132 (2009) · Zbl 1169.65053
[25] Karaboga, D.; Gorkemli, B., A quick artificial bee colony (qABC) algorithm and its performance on optimization problems, Appl. Soft Comput., 23, 227-238 (2014)
[26] Kennedy, J.; Eberhart, R. C., Particle swarm optimization, (Proc. IEEE Int. Conf. Neural Netw, 4 (1995)), 1942-1948
[27] kiran, M. S.; Findik, O., A directed artificial bee colony algorithm, Appl. Soft Comput., 26, 454-462 (2015)
[28] Kiran, M. S.; Hakli, H.; Gunduz, M.; Uguz, H., Artificial bee colony algorithm with variable search strategy for continuous optimization, Inform. Sci., 300, 140-157 (2015)
[29] Li, G. H.; Lin, Q. Z.; Cui, L. Z.; Du, Z. H.; Liang, Z. P.; Chen, J. Y.; Lu, N.; Ming, Z., A novel hybrid differential evolution algorithm with modified CoDE and JADE, Appl. Soft Comput., 47, 577-599 (2016)
[30] Li, G. H.; Cui, L. Z.; Fu, X. H.; Wen, Z. K.; Lu, N.; Lu, J., Artificial bee colony algorithm with gene recombination for numerical function optimization, Appl. Soft Comput., 52, 146-159 (2017)
[31] Liang, J. J.; Qin, A. K.; Suganthan, P. N.; Baskar, S., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Trans. Evol. Comput., 10, 3, 281-295 (2006)
[32] Martínez-Soto, R.; Castillo, O., A hybrid optimization method with PSO and GA to automatically design type-1 and type-2 fuzzy logic controllers, Int. J. Mach. Learn. Cyber., 6, 2, 175-196 (2015)
[33] Mendes, R.; Kennedy, J.; Neves, J., The fully informed particle swarm: simpler, maybe better, IEEE Trans. Evol. Comput., 8, 3, 204-210 (2004)
[34] Neri, F.; Tirronen, V., Recent advances in differential evolution: a review and experimental analysis, Artif. Intell. Rev., 33, 1, 61-106 (2010)
[35] Poikolainen, I.; Neri, F.; Caraffini, F., Cluster-based population initialization for differential evolution frameworks, Inform. Sci., 297, 216-235 (2015)
[36] Qin, A. K.; Huang, V. L.; Suganthan, P. N., Differential evolution algorithm with strategy adaptation for global numerical optimization, IEEE Trans. Evol.Comput., 12, 1, 398-417 (2009)
[37] Rahnamayan, S.; Tizhoosh, H. R.; Salama, M. M.A., Opposition-Based Differential Evolution, IEEE Trans. Evol. Comput., 12, 1, 64-79 (2008)
[38] Ratnaweera, A.; Halgamuge, S.; Watson, H., Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Trans. Evol. Comput., 8, 3, 240-255 (2004)
[39] Souravlias, D.; Parsopoulos, K. E., Particle swarm optimization with neighborhood-based budget allocation, Int. J. Mach. Learn. Cyber., 7, 3, 451-477 (2016)
[40] Storn, R.; Price, K., Differential evolution: a simple and efficient heuristic for global optimization over continuous space, J. Glob. Optim., 11, 4, 341-359 (1997) · Zbl 0888.90135
[41] Tian, N.; Ji, Z. C.; Lai, C. H., Simultaneous estimation of nonlinear parameters in parabolic partial differential equation using quantum-behaved particle swarm optimization with Gaussian mutation, Int. J. Mach. Learn. Cyber., 6, 2, 307-318 (2015)
[42] Wang, Y.; Cai, Z. X.; Zhang, Q. F., Differential evolution with composite trial vector generation strategies and control parameters, IEEE Trans. Evol. Comput., 15, 1, 55-66 (2011)
[43] Wang, H.; Wu, Z.; Rahnamayan, S.; Sun, H.; Liu, Y.; Pan, J., Multi-strategy ensemble artificial bee colony algorithm, Inform. Sci., 279, 587-603 (2014) · Zbl 1354.68242
[44] Wei, Y. H.; Hu, Q. Y.; Xu, C., ordering, pricing and allocation in a service supply chain, Int. J. Prod. Econ., 144, 2, 590-598 (2013)
[45] Wei, Y. H.; Xu, C.; Hu, Q. Y., Transformation of optimization problems in revenue management, queueing system, and supply chain management, Int. J. Prod. Econ., 146, 2, 588-597 (2015)
[46] Xiang, Y.; Peng, Y. M.; Zhong, Y. B.; Chen, Z. Y.; Lu, X. W.; Zhong, X. J., A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization, Comput. Optim. Appl., 57, 2, 493-516 (2014) · Zbl 1304.90222
[47] Zhan, Z. H.; Zhang, J.; Li, Y.; Shi, Y. H., Orthogonal learning particle swarm optimization, IEEE Trans. Evol. Comput., 15, 6, 832-847 (2011)
[48] Zhang, J. Q.; Sanderson, A. C., JADE: adaptive differential evolution with optional external archive, IEEE Trans. Evol. Comput., 13, 5, 945-958 (2009)
[49] Zhang, H.; Song, S. M.; Zhou, A. M., A multiobjective cellular genetic algorithm based on 3D structure and cosine crowding measurement, Int. J. Mach. Learn. Cyber., 6, 3, 487-500 (2015)
[50] Zhu, G.; Kwong, S., Gbest-guided artificial bee colony algorithm for numerical function optimization, Appl. Math. Comput., 217, 7, 3166-3173 (2010) · Zbl 1204.65074
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