[1]

Haghbayan P., Nezamabadi-Pour H., Kamyab S., A niche GSA method with nearest neighbor scheme for multimodal optimization, Swarm and Evolutionary Computation, 35, 2017, 78-92 Web of ScienceCrossrefGoogle Scholar

[2]

Subhrajit R., Minhazul I., Das S., Ghosh S., Multimodal optimization by artificial weed colonies enhanced with localized group search optimizers, Applied Soft Computing, 13, 2013, 27-46 CrossrefWeb of ScienceGoogle Scholar

[3]

Tang K. S., Man K., Kwong S., He Q., Genetic algorithms and their applications, IEEE Signal Processing Magazine, 13, 1996, 6, 22-37 CrossrefGoogle Scholar

[4]

Kirkpatrick S., Vecchi M., Optimization by simmulated annealing, Science, 220, 1983, 4598, 671-680 CrossrefGoogle Scholar

[5]

Farmer J. D., Packard N. H., Perelson A. S., The immune system, adaptation, and machine learning, Physica D: Nonlinear Phenomena, 22, 1986, 1, 187-204 CrossrefGoogle Scholar

[6]

Dorigo M., Maniezzo V., Colorni A., Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26, 1996, 1, 29-41 CrossrefGoogle Scholar

[7]

Kenndy J., Particle Swarm Optimization, in “Encyclopedia of machine learning”, Sammut C., Webb G. I., eds., Springer, Boston, MA, 2011, 760-766 Google Scholar

[8]

Rashedi E., Nezamabadi-Pour H., Saryazdi S., GSA: a Gravitational Search Algorithm, Information sciences, 179, 2009, 13, 2232-2248 CrossrefWeb of ScienceGoogle Scholar

[9]

Rashedi E., Nezamabadi-Pour H., Saryazdi S., BGSA: binary gravitational search algorithm, Natural Computing, 9, 2010, 3, 727-745 Web of ScienceCrossrefGoogle Scholar

[10]

Brits R., Engelbrecht A. P., Van den Bergh F., A niching particle swarm optimizer, In: Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning, Paper Presented at Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning (Orchid Country Club, Singapore), 2002, November 18, Orchid Country Club, 692-696 Google Scholar

[11]

Thiemard E., Economic generation of low-discrepancy sequences with a b-ary Gray code, Technical Report, 1998, Department of Mathematics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Google Scholar

[12]

Streichert F., Stein G., Ulmer H., Zell A., A clustering based niching method for evolutionary algorithms, In: Genetic and Evolutionary Computation Conference, Paper Presented at Genetic and Evolutionary Computation Conference (Chicago, USA), 2003, July 9-11, Springer, 644-645 Google Scholar

[13]

Li X., Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization, In: Genetic and Evolutionary Computation Conference, Paper Presented at Genetic and Evolutionary Computation Conference, (Seattle, USA), 2004, June 26-30, Springer, 105-116 Google Scholar

[14]

Seo J. H., Im C. H, Heo C. G., Kim J. K., Jung H. K., Lee C. G., Multimodal function optimization based on particle swarm optimization, IEEE Transactions on Magnetics, 42, 2006, 4, 1095-1098 CrossrefGoogle Scholar

[15]

Yazdani S., Nezamabadi-Pour H., Kamyab S., A gravitational search algorithm for multimodal optimization, Swarm and Evolutionary Computation, 14, 2014, 1-14 Web of ScienceCrossrefGoogle Scholar

[16]

Cuevas E., Reyna-Orta A., A cuckoo search algorithm for multimodal optimization, 2014, The Scientific World Journal, 2014 Google Scholar

[17]

Chang W. D., A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems, Applied Soft Computing, 33, 2014, 170-182 Web of ScienceGoogle Scholar

[18]

Naik M. K., Panda R., A new hybrid CS-GSA algorithm for function optimization, In: Proceedings of the IEEE International Conference on Electrical, Electronics, Signals, Communication and Optimization, Paper Presented at IEEE International Conference on Electrical, Electronics, Signals, Communication and Optimization, (Visakhapatnam, India), 2015, January 24-25, IEEE, 1-6 Google Scholar

[19]

Nekouie N., Yaghoobi M., A new method in multimodal optimization based on firefly algorithm, Artificial Intelligence Review, 46, 2016, 2, 267-287 Web of ScienceCrossrefGoogle Scholar

[20]

Rim C., Piao S., Li G., Pak U., A niching chaos optimization algorithm for multimodal optimization, Soft Computing, 22, 2016, 2, 621-633 Web of ScienceGoogle Scholar

[21]

Tang K., Yang P., Yao S., Negatively correlated search, IEEE Journal on Selected Areas in Communications, 34, 2016, 3, 542-550 CrossrefWeb of ScienceGoogle Scholar

[22]

Chang W. D., Multimodal function optimizations with multiple maximums and multiple minimums using an improved PSO algorithm, Applied Soft Computing, 60, 2017, 60-72 Web of ScienceCrossrefGoogle Scholar

[23]

Goldberg D. E., Richardson J., Genetic algorithms with sharing for multimodal function optimization, In: Proceedings of the Second International Conference on Genetic Algorithms and their applications, Paper Presented at the Second International Conference on Genetic Algorithms and their applications, (Cambridge, USA), 1987, Hillsdale: Lawrence Erlbaum, 41-49 Google Scholar

[24]

Pétrowski A., A clearing procedure as a niching method for genetic algorithms, In: Proceedings of IEEE International Conference on Evolutionary Computation, Paper Presented at Proceedings of IEEE International Conference on Evolutionary Computation, (Nagoya, Japan), 1996, IEEE, May 20-22, 798-803 Google Scholar

[25]

De Jong K. A., An Analysis of the Behavior of a Class of Genetic Adaptive Systems, In: Techincal Report, 1975, Computer and Communication Sciences Department, College of Literature, Science, and the Arts, University of Michigan Google Scholar

[26]

Mahfoud S. W., Simple Analytical Models of Genetic Algorithms for Multimodal Function Optimization, In: Proceedings of the 5th International Conference on Genetic Algorithms, Paper Presented at Proceedings of the 5th International Conference on Genetic Algorithms (San Fransisco, USA), 1993, San Fransisco: Morgan Kaufman Publishers, 643 Google Scholar

[27]

Mahfoud S. W., Crossover Interactions Among Niches, In: Proceedings of the First IEEE Conference on Evolutionary Computation, Paper Presented at the First IEEE Conference on Evolutionary Computation, (Orlando, USA), 1994, June 27-29, IEEE, 188-193 Google Scholar

[28]

Mahfoud S. W., Niching methods for genetic algorithms, Urbana, 51, 1994, 95001, 62-94 Google Scholar

[29]

Mengshoel O. J., Goldberg D. E., Probabilistic crowding: Deterministic crowding with probabilistic replacement. In: Genetic and Evolutionary Computation Conference, Paper Presented at Genetic and Evolutionary Computation Conference, (San Fransisco, USA), 1999, July 8-12, San Fransisco: Morgan Kaufman Publishers, 409 Google Scholar

[30]

Kimura S., Matsumura K., Constrained multimodal function optimization using a simple evolutionary algorithm, In: IEEE Congress on Evolutionary Computation, Paper Presented at IEEE Congress on Evolutionary Computation, (New Orleans, USA), 2011, June 5-8, IEEE, 447-454 Google Scholar

[31]

Li X., Niching without niching parameters: particle swarm optimization using a ring topology, IEEE Transactions on Evolutionary Computation, 14, 2010, 1, 150-169 Web of ScienceCrossrefGoogle Scholar

[32]

Parrott D., Li X., Locating and tracking multiple dynamic optima by a particle swarm model using speciation, IEEE Transactions on Evolutionary Computation, 10, 2006, 4, 440-458 CrossrefGoogle Scholar

[33]

Li X., Multimodal function optimization based on fitness-euclidean distance ratio, In: Genetic and Evolutionary Computation Conference, Paper Presented at Genetic and Evolutionary Computation Conference, (Washington, USA), 2007, June 25-29, New York: ACM, 78-85 Google Scholar

[34]

Zhang J., Zhang J. R., Li K., A sequential niching technique for particle swarm optimization, In: Advances in Intelligent Computing, Paper Presented at Advances in Intelligent Computing, (Hefei, China), 2005, August 23-26, Berlin Heidelberg: Springer-Verlag, 390-399 Google Scholar

[35]

García S., Fernández A., Luengo J., Herrera F., Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power, Information Sciences, 180, 2010, 10, 2044-2064 Web of ScienceCrossrefGoogle Scholar

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