Ions motion algorithm for solving optimization problems

Applied Soft Computing Journal

Volumn 32, Issue , 2015, Pages 72-79

  Javidy, Behzad ^a   Hatamlou, Abdolreza ^a   Mirjalili, Seyedali ^b  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b GRIFFITH UNIVERSITY   (Australia)

Author keywords

Ions motion algorithm; Meta heuristic algorithms; Optimization problems

Indexed keywords

HEURISTIC ALGORITHMS; IONS; OPTIMIZATION; PROBLEM SOLVING;

FAST CONVERGENCE; LOW COMPUTATIONAL COMPLEXITY; META HEURISTIC ALGORITHM; MOTION ALGORITHM; OPTIMIZATION ALGORITHMS; OPTIMIZATION PROBLEMS; STANDARD TEST FUNCTIONS; TUNING PARAMETER;

ALGORITHMS;

EID: 84926431460     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2015.03.035     Document Type: Article

References (46)

1. D. Beasley, D. Bull, and R. Martin An Overview of Genetic Algorithms. Part 2 1993 University of Cardiff Cardiff
2. D. Beasley, D. Bull, and R. Martin An Overview of Genetic Algorithms. Part 1 1993 University of Cardiff Cardiff
3. D.B. Fogel What is evolutionary computation IEEE Spectrum 2000 26 32
4. D.E. Goldberg Genetic Algorithms in Search 1989 Optimization and Machine Learning Addison Wesley, Reading
5. M. Mitchell An Introduction to Genetic Algorithms 1996 MIT Press Cambridge, MA
6. J. Vesterstrom, and R. Thomsen A comparative study of differential evolution particle swarm optimization and evolutionary algorithms on numerical benchmark problems IEEE Congress on Evolutionary Computation (CEC' 2004) Piscataway, NJ 2004 1980 1987
7. K. Price, and R. Storn Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces J. Global Optim. 11 1997 341 359
8. K. Price, and R. Storn Differential Evolution a Practical Approach to Global Optimization 2005 Springer Natural Computing Series
9. P. Bergey, and C. Ragsdale Modified differential evolution: a greedy random strategy for genetic recombination Omega 33 2005 255 265
10. A. Salman, A. Engelbrecht, and M. Omran Empirical analysis of self-adaptive differential evolution Eur. J. Oper. Res. 183 2007 785 804
11. J. Kennedy, and R.C. Eberhart A Discrete Binary Version of the Particle Swarm Algorithm 1997 4104 4108
12. J. Kennedy, and R.C. Eberhart Particle swarm optimization Proceedings of IEEE International Conference on Neural Networks Piscataway 1995 1942 1948
13. M. Clerc, and J. Kennedy The particle swarm: explosion, stability and convergence in multi-dimensional complex space IEEE Trans. Evol. Comput. 20 2002 1671 1676
14. O.M. Nezami, A. Bahrampour, and P. Jamshidlou Dynamic Diversity Enhancement in Particle Swarm Optimization (DDEPSO) algorithm for preventing from premature convergence Procedia Comput. Sci. 24 2013 54 65
15. B. Akay, and D. Karaboga A modified artificial bee colony algorithm for real-parameter optimization Information Sciences 2012 120 142
16. D. Karaboga, and B. Akay A comparative study of artificial bee colony algorithm Appl. Math. Comput. 214 2009 108 132
17. D. Karaboga, and B. Basturk On the performance of artificial bee colony (ABC) algorithm Applied Soft Computing 2008 687 697
18. D. Karaboga An idea based on honey bee swarm for numerical optimization Technical Report-TR06 2005
19. W. Gu, M. Yin, and C. Wang Self adaptive artificial bee colony for global numerical optimization IERI Procedia 1 2012 59 65
20. M.S. Silberberg Principles of General Chemistry 2007 McGraw-Hill New York
21. P.J. Van Laarhoven, and E.H. Aarts Simulated Annealing 1987 Springer
22. S. Mirjalili, S.M. Mirjalili, and A. Lewis Grey wolf optimizer Adv. Eng. Softw. 69 2014 46 61
23. M. Dorigo, M. Birattari, and T. Stutzle Ant colony optimization IEEE Comput. Intell. Mag. 1 2006 28 39
24. X.-S. Yang A new metaheuristic bat-inspired algorithm Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) 2010 Springer 65 74
25. K. Krishnanand, and D. Ghose Glowworm swarm optimisation: a new method for optimising multi-modal functions Int. J. Comput. Intell. Stud. 1 2009 93 119
26. B. Niu, Y. Zhu, X. He, H. Wu, and O. MCPS A multi-swarm cooperative particle swarm optimizer Appl. Math. Comput. 185 2007 1050 1062
27. X.-L. Li, and J.-X. Qian Studies on artificial fish swarm optimization algorithm based on decomposition and coordination techniques J. Circuits Syst. 1 2003 1 6
28. X.-S. Yang Firefly algorithm, stochastic test functions and design optimisation Int. J. Bio-Inspir. Comput. 2 2010 78 84
29. X.-S. Yang, and S. Deb Cuckoo search via Lévy flights Nature & Biologically Inspired Computing, NaBIC 2009 2009 World Congress on IEEE 210 214
30. A.H. Gandomi, and A.H. Alavi Krill herd: a new bio-inspired optimization algorithm Commun. Nonlinear Sci. Numer. Simul. 17 2012 4831 4845
31. A. Hatamlou Heart: a novel optimization algorithm for cluster analysis Prog. Artif. Intell. 2 2014 167 173
32. J.H. Holland Genetic algorithms Sci. Am. 267 1992 66 72
33. R. Storn, and K. Price Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces J. Global Optim. 11 1997 341 359
34. X. Yao, Y. Liu, and G. Lin Evolutionary programming made faster IEEE Trans. Evol. Comput. 3 1999 82 102
35. J.R. Koza Genetic Programming 1992
36. D. Simon Biogeography-based optimization IEEE Trans. Evol. Comput. 12 2008 702 713
37. E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi GSA: a gravitational search algorithm Inf. Sci. 179 2009 2232 2248
38. A. Kaveh, and M. Khayatazad A new meta-heuristic method: ray optimization Comput. Struct. 112 2012 283 294
39. E. Cuevas, A. Echavarría, and M.A. Ramírez-Ortegón An optimization algorithm inspired by the states of matter that improves the balance between exploration and exploitation Appl. Intell. 40 2014 256 272
40. O.K. Erol, and I. Eksin A new optimization method: big bang-big crunch Adv. Eng. Softw. 37 2006 106 111
41. A. Hatamlou Black hole: a new heuristic optimization approach for data clustering Inf. Sci. 222 2013 175 184
42. B. Alatas ACROA: artificial chemical reaction optimization algorithm for global optimization Expert Syst. Appl. 38 2011 13170 13180
43. S. Moein, and R. Logeswaran KGMO. A swarm optimization algorithm based on the kinetic energy of gas molecules Inf. Sci. 275 2014 127 144
44. D.H. Wolpert, and W.G. Macready No free lunch theorems for optimization IEEE Trans. Evol. Comput. 1 1997 67 82
45. M. Jamil, and X. Yang A literature survey of benchmark functions forglobal optimization problems Int. J Math. Modell. Numer. Optim. 4 2013 150 194
46. D. Corne, M. Dorigo, and F. Glover New Ideas in Optimization 1999 McGraw-Hill