An improved optimization method based on krill herd and artificial bee colony with information exchange

Kundu S, Parhi DR (2016) Navigation of underwater robot based on dynamically adaptive harmony search algorithm. Memet Comput 8(2):125–146. doi: 10.1007/s12293-016-0179-0

Zhang Y, Liu J, Zhou M, Jiang Z (2016) A multi-objective memetic algorithm based on decomposition for big optimization problems. Memet Comput 8(1):45–61. doi: 10.1007/s12293-015-0175-9

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. doi: 10.1023/A:1008202821328

Beyer H, Schwefel H (2002) Nat Comput. Kluwer Academic Publishers, Dordrecht

Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning, vol 412. Addison-Wesley, Boston

Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471. doi: 10.1007/s10898-007-9149-x

Wang G-G, Deb S, Gao X-Z, Coelho LdS (2016) A new metaheuristic optimization algorithm motivated by elephant herding behavior. Int J of Bio Inspir Comput 8(6):394–409. doi: 10.1504/IJBIC.2016.10002274

Wang G-G (2016) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memet Comput. doi: 10.1007/s12293-016-0212-3

Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68. doi: 10.1177/003754970107600201

Wang G-G, Deb S, Cui Z (2015) Monarch butterfly optimization. Neural Comput Appl. doi: 10.1007/s00521-015-1923-y

Kennedy J, Eberhart R (1995) Particle swarm optimization. Paper presented at the proceeding of the IEEE international conference on neural networks, Perth, Australia, 27 November–1 December

Le MN, Ong Y-S, Jin Y, Sendhoff B (2009) Lamarckian memetic algorithms: local optimum and connectivity structure analysis. Memet Comput 1(3):175–190. doi: 10.1007/s12293-009-0016-9

Meuth R, Lim M-H, Ong Y-S, Wunsch DC (2009) A proposition on memes and meta-memes in computing for higher-order learning. Memet Comput 1(2):85–100. doi: 10.1007/s12293-009-0011-1

Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845. doi: 10.1016/j.cnsns.2012.05.010

Črepinšek M, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms. ACM Comput Surv 45(3):1–33. doi: 10.1145/2480741.2480752

Wang G-G, Guo L, Gandomi AH, Hao G-S, Wang H (2014) Chaotic krill herd algorithm. Inf Sci 274:17–34. doi: 10.1016/j.ins.2014.02.123

Wang G-G, Gandomi AH, Yang X-S, Alavi AH (2016) A new hybrid method based on krill herd and cuckoo search for global optimization tasks. Int J of Bio Inspir Comput 8(5):286–299. doi: 10.1504/IJBIC.2016.10000414

Mukherjee A, Mukherjee V (2015) Solution of optimal power flow using chaotic krill herd algorithm. Chaos Solitons Fractals 78:10–21. doi: 10.1016/j.chaos.2015.06.020

Wang G-G, Gandomi AH, Alavi AH (2014) Stud krill herd algorithm. Neurocomputing 128:363–370. doi: 10.1016/j.neucom.2013.08.031

Bolaji ALa, Al-Betar MA, Awadallah MA, Khader AT, Abualigah LM (2016) A comprehensive review: krill herd algorithm (KH) and its applications. Appl Soft Compt 49:437–446. doi: 10.1016/j.asoc.2016.08.041

Bolaji AL, Khader AT, Al-Betar MA, Awadallah MA (2014) University course timetabling using hybridized artificial bee colony with hill climbing optimizer. J Comput Sci 5(5):809–818. doi: 10.1016/j.jocs.2014.04.002

Kıran SM, Gündüz M (2013) A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems. Appl Soft Compt 13(4):2188–2203. doi: 10.1016/j.asoc.2012.12.007

Awadallah MA, Bolaji ALa, Al-Betar MA (2015) A hybrid artificial bee colony for a nurse rostering problem. Appl Soft Compt 35:726–739. doi: 10.1016/j.asoc.2015.07.004

Bullinaria JA, AlYahya K (2014) Artificial bee colony training of neural networks: comparison with back-propagation. Memet Comput 6(3):171–182. doi: 10.1007/s12293-014-0137-7

Li JQ, Pan QK, Duan PY (2016) An improved artificial bee colony algorithm for solving hybrid flexible flowshop with dynamic operation skipping. IEEE Trans Cybern 46(6):1311–1324. doi: 10.1109/TCYB.2015.2444383

Krüger TJ, Davidović T, Teodorović D, Šelmić M (2016) The bee colony optimization algorithm and its convergence. Int J Bio Inspir Comput 8(5):340–354. doi: 10.1504/IJBIC.2016.079573

Hussein WA, Sahran S, Sheikh Abdullah SNH (2017) The variants of the bees algorithm (BA): a survey. Artif Intell Rev 47(1):67–121. doi: 10.1007/s10462-016-9476-8

Zhang Y, Wu L (2012) Artificial bee colony for two dimensional protein folding. Adv Electr Eng Syst 1(1):19–23

Wang G, Guo L, Wang H, Duan H, Liu L, Li J (2014) Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput Appl 24(3–4):853–871. doi: 10.1007/s00521-012-1304-8

Wang G-G, Deb S, Gandomi AH, Zhang Z, Alavi AH (2016) Chaotic cuckoo search. Soft Comput 20(9):3349–3362. doi: 10.1007/s00500-015-1726-1

Wang G, Guo L, Duan H, Wang H, Liu L, Shao M (2013) Hybridizing harmony search with biogeography based optimization for global numerical optimization. J Comput Theor Nanos 10(10):2318–2328. doi: 10.1166/jctn.2013.3207

Li Z-Y, Yi J-H, Wang G-G (2015) A new swarm intelligence approach for clustering based on krill herd with elitism strategy. Algorithms 8(4):951–964. doi: 10.3390/a8040951

Wang G-G, Chang B, Zhang Z (2015) A multi-swarm bat algorithm for global optimization. In: 2015 IEEE congress on evolutionary computation (CEC 2015), Sendai, Japan, May 25–28, 2015. IEEE, pp 480–485. doi: 10.1109/CEC.2015.7256928

Wang G-G, Lu M, Zhao X-J (2016) An improved bat algorithm with variable neighborhood search for global optimization. Paper presented at the 2016 IEEE congress on evolutionary computation (IEEE CEC 2016), Vancouver, 25–29 July, 2016

Wang G-G, Deb S, Gandomi AH, Alavi AH (2016) Opposition-based krill herd algorithm with Cauchy mutation and position clamping. Neurocomputing 177:147–157. doi: 10.1016/j.neucom.2015.11.018

Das S, Suganthan P (2010) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur Univ., Nanyang Technol. Univ., Kolkata, India