Evolutionary population dynamics and grey wolf optimizer

Webster B, Bernhard PJ (2003) A local search optimization algorithm based on natural principles of gravitation. In: Proceedings of the 2003 international conference on information and knowledge engineering (IKE’03), Las Vegas, NV, USA, 2003, pp 255–261

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

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

Formato RA (2007) Central force optimization: a new metaheuristic with applications in applied electromagnetics. Prog Electromagn Res 77:425–491

Alatas B (2011) ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst Appl 38:13170–13180

Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184

Du H, Wu X, Zhuang J (2006) Small-world optimization algorithm for function optimization. In: Advances in natural computation. Springer, pp 264–273

Shah-Hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 6:132–140

Moghaddam FF, Moghaddam RF, Cheriet M (2012) Curved space optimization: a random search based on general relativity theory. arXiv preprint arXiv:1208.2214

Simon D (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12:702–713

Abbass HA (2001) MBO: marriage in honey bees optimization—a haplometrosis polygynous swarming approach. In: Proceedings of the 2001 congress on evolutionary computation, 2001, pp 207–214

Li X (2003) A new intelligent optimization-artificial fish swarm algorithm. Doctor thesis, Zhejiang University of Zhejiang, China

Roth M (2005) Termite: a swarm intelligent routing algorithm for mobile wireless ad-hoc networks. Ph. D thesis, Cornel University

Pinto PC, Runkler TA, Sousa JM (2007) Wasp swarm algorithm for dynamic MAX-SAT problems. In: Adaptive and natural computing algorithms. Springer, pp 350–357

Mucherino A, Seref O (2007) Monkey search: a novel metaheuristic search for global optimization. In: AIP conference proceedings, p 162

Lu X, Zhou Y (2008) A novel global convergence algorithm: bee collecting pollen algorithm. In: Advanced intelligent computing theories and applications. With aspects of artificial intelligence. Springer, pp 518–525

Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: World congress on nature and biologically inspired computing, 2009. NaBIC 2009, pp 210–214

Shiqin Y, Jianjun J, Guangxing Y (2009) A dolphin partner optimization. In: WRI global congress on intelligent systems, 2009. GCIS’09, pp 124–128

Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bioinspired Comput 2:78–84

Askarzadeh A, Rezazadeh A (2013) A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: bird mating optimizer. Int J Energy Res 37(10):1196–1204

Pan W-T (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74

Abdel-Kader RF (2011) Hybrid discrete PSO with GA operators for efficient QoS-multicast routing. Ain Shams Eng J 2:21–31

Kao Y-T, Zahara E (2008) A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl Soft Comput 8:849–857

Mirjalili S, Hashim SZM (2010) A new hybrid PSOGSA algorithm for function optimization. In: 2010 international conference on computer and information application (ICCIA), 2010, pp 374–377

Khamsawang S, Wannakarn P, Jiriwibhakorn S (2010) Hybrid PSO-DE for solving the economic dispatch problem with generator constraints. In: 2010 the 2nd international conference on computer and automation engineering (ICCAE), 2010, pp 135–139

Shuang B, Chen J, Li Z (2011) Study on hybrid PS-ACO algorithm. Appl Intell 34:64–73

El-Abd M (2011) A hybrid ABC-SPSO algorithm for continuous function optimization. In: 2011 IEEE symposium on swarm intelligence (SIS), 2011, pp 1–6

Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217:3166–3173

Coelho LdS (2008) A quantum particle swarm optimizer with chaotic mutation operator. Chaos Solitons Fractals 37:1409–1418

Lee Z-J, Su S-F, Chuang C-C, Liu K-H (2008) Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment. Appl Soft Comput 8:55–78

Duan H, Yu Y, Zhang X, Shao S (2010) Three-dimension path planning for UCAV using hybrid meta-heuristic ACO–DE algorithm. Simul Model Pract Theory 18:1104–1115

Lewis A, Mostaghim S, Randall (2008) Evolutionary population dynamics and multi-objective optimisation problems. In: Multi-objective optimization in computational intelligence: theory and practice, pp 185–206

Bak P, Tang C, Wiesenfeld K (1987) Self-organized criticality: an explanation of the 1/f noise. Phys Rev Lett 59:381

Bak P (1997) How nature works. Oxford University Press, Oxford

Boettcher S, Percus AG (1999) Extremal optimization: methods derived from co-evolution. arXiv preprint arXiv:math/9904056

Lewis A, Abramson D, Peachey T (2004) An evolutionary programming algorithm for automatic engineering design. In: Parallel processing and applied mathematics. Springer, pp 586–594

Randall M, Lewis A (2006) An extended extremal optimisation model for parallel architectures. In: Second IEEE international conference on e-science and grid computing, 2006. e-Science’06, pp 114–114

Fogel LJ (1962) Autonomous automata. Ind Res 4:14–19

Xie D, Luo Z, Yu F (2009) The computing of the optimal power consumption for semi-track air-cushion vehicle using hybrid generalized extremal optimization. Appl Math Model 33:2831–2844

Randall M (2007) Enhancements to extremal optimisation for generalised assignment. In: Progress in artificial life. Springer, pp 369–380

Randall M, Hendtlass T, Lewis A (2009) Extremal optimisation for assignment type problems. In: Biologically-inspired optimisation methods. Springer, pp 139–164

Gómez-Meneses P, Randall M, Lewis A (2010) A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems. In: 2010 IEEE congress on evolutionary computation (CEC), 2010, pp 1–8

Tamura K, Kitakami H, Nakada A (2013) Distributed modified extremal optimization using island model for reducing crossovers in reconciliation graph. Eng Lett 21:81–88

Gomez Meneses PS (2012) Extremal optimisation applied to constrained combinatorial multi-objective optimisation problems. Ph. D thesis, Bond University

Tamura K, Kitakami H, Nakada A (2014) Island-model-based distributed modified extremal optimization for reducing crossovers in reconciliation graph. In: Transactions on engineering technologies. Springer, pp 141–156

Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

Mirjalili S, Lewis A (2014) Adaptive gbest-guided gravitational search algorithm. Neural Comput Appl 25(7–8):1569–1584

Mirjalili S, Lewis A, Sadiq AS (2014) Autonomous particles groups for particle swarm optimization. Arab J Sci Eng 39(6):4683–4697

Mirjalili S, Mirjalili S, Yang X-S (2014) Binary bat algorithm. Neural Comput Appl 25:663–681

Mirjalili S, Wang G-G, Coelho LdS (2014) Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput Appl 25(6):1423–1435

Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evolut Comput 3:82–102

Digalakis J, Margaritis K (2001) On benchmarking functions for genetic algorithms. Int J Comput Math 77:481–506

Molga M, Smutnicki C (2005) Test functions for optimization needs. In: Test functions for optimization needs

Yang X-S (2010) Test problems in optimization. arXiv preprint arXiv:1008.0549

Mirjalili S, Lewis A (2013) S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evolut Comput 9:1–14

Saremi S, Mirjalili S, Lewis A (2014) How important is a transfer function in discrete heuristic algorithms. Neural Comput Appl 1–16. doi: 10.1007/s00521-014-1743-5

Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25(5):1077–1097

Saremi S, Mirjalili SM, Mirjalili S (2014) Chaotic krill herd optimization algorithm. Proc Technol 12:180–185