An effective krill herd algorithm with migration operator in biogeography-based optimization

Applied Mathematical Modelling

Volumn 38, Issue 9-10, 2014, Pages 2454-2462

  Wang, Gai Ge ^a   Gandomi, Amir H ^b   Alavi, Amir H ^c  

^a JIANGSU NORMAL UNIVERSITY   (China)

^b The University of Akron   (United States)

^c Michigan State University   (United States)

Author keywords

Biogeography based optimization; Global optimization; Krill herd; Krill migration operator

Indexed keywords

GLOBAL OPTIMIZATION; HEURISTIC METHODS; OPTIMIZATION;

BENCHMARK FUNCTIONS; BIOGEOGRAPHY-BASED OPTIMIZATIONS; COMPLEX OPTIMIZATION; KRILL HERD; KRILL MIGRATION OPERATOR; OPTIMIZATION ALGORITHMS; OPTIMIZATION PROBLEMS; SEARCH HEURISTICS;

HEURISTIC ALGORITHMS;

EID: 84898548583     PISSN: 0307904X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apm.2013.10.052     Document Type: Article

References (53)

1. Zou D., Gao L., Wu J., et al. A novel global harmony search algorithm for reliability problems. Comput. Ind. Eng. 2010, 58(2):307-316.
2. Wu P., Gao L., Zou D., et al. An improved particle swarm optimization algorithm for reliability problems. ISA Trans. 2011, 50(1):71-81.
3. Zou D., Gao L., Li S., et al. Solving 0-1 knapsack problem by a novel global harmony search algorithm. Appl. Soft Comput. 2011, 11(2):1556-1564.
4. Li X., Yin M. An opposition-based differential evolution algorithm for permutation flow shop scheduling based on diversity measure. Adv. Eng. Softw. 2013, 55:10-31.
5. Alfi A., Modares H. System identification and control using adaptive particle swarm optimization. Appl. Math. Model. 2011, 35(3):1210-1221.
6. Soolaki M., Mahdavi I., Mahdavi-Amiri N., et al. A new linear programming approach and genetic algorithm for solving airline boarding problem. Appl. Math. Model. 2012, 36(9):4060-4072.
7. Wang G., Guo L., Duan H., et al. Path planning for UCAV using bat algorithm with mutation. Sci. World J. 2012, 2012:1-15.
8. Duan H., Zhao W., Wang G., et al. Test-sheet composition using analytic hierarchy process and hybrid metaheuristic algorithm TS/BBO. Math. Probl. Eng. 2012, 2012:1-22.
9. Yang X.S., Gandomi A.H., Talatahari S., et al. Metaheuristics in Water, Geotechnical and Transport Engineering 2013, Elsevier, Waltham, MA.
10. Gandomi A.H., Yang X.S., Talatahari S., et al. Metaheuristic Applications in Structures and Infrastructures 2013, Elsevier, Waltham, MA.
11. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine learning 1998, Addison-Wesley, New York.
12. Nazif H., Lee L.S. Optimised crossover genetic algorithm for capacitated vehicle routing problem. Appl. Math. Model. 2012, 36(5):2110-2117.
13. Geem Z.W., Kim J.H., Loganathan G.V. A new heuristic optimization algorithm: harmony search. Simulation 2001, 76(2):60-68.
14. Gholizadeh S., Barzegar A. Shape optimization of structures for frequency constraints by sequential harmony search algorithm. Eng. Optim. 2013, 45(6):627-646.
15. Wang G., Guo L. A novel hybrid bat algorithm with harmony search for global numerical optimization. J. Appl. Math. 2013, 2013:1-21.
16. Gandomi A.H., Yang X.-S., Alavi A.H. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 2013, 29(1):17-35.
17. Wang G., Guo L., Duan H., et al. A hybrid meta-heuristic DE/CS algorithm for UCAV three-dimension path planning. Sci. World J. 2012, 2012:1-11.
18. Yang X.S., Deb S. Cuckoo search via Lévy flights. Proceeding of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009) 2009, 210-214. IEEE Publications, Coimbatore, India.
19. Gandomi A.H., Talatahari S., Yang X.S., et al. Design optimization of truss structures using cuckoo search algorithm. Struct. Des. Tall Spec. 2013, 22(17):1330-1349.
20. Back T. Evolutionary algorithms in theory and practice 1996, Oxford University Press, Oxford.
21. Beyer H. The theory of evolution strategies 2001, Springer, New York.
22. Storn R., Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 1997, 11(4):341-359.
23. Gandomi A.H., Yang X.-S., Talatahari S., et al. Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Comput. Math. Appl. 2012, 63(1):191-200.
24. Li X., Yin M. Application of differential evolution algorithm on self-potential data. PLoS ONE 2012, 7(12):e51199.
25. Zou D., Wu J., Gao L., et al. A Modified Differential Evolution Algorithm for Unconstrained Optimization Problems. Neurocomputing 2013, 120:469-481.
26. Talatahari S., Farahmand Azar B., Sheikholeslami R., et al. Imperialist competitive algorithm combined with chaos for global optimization. Commun. Nonlinear Sci. Numer. Simul. 2012, 17(3):1312-1319.
27. Dorigo M., Stutzle T. Ant Colony Optimization 2004, MIT Press, Cambridge.
28. Yang X.S., Gandomi A.H. Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 2012, 29(5):464-483.
29. X. Li, J. Zhang, M. Yin, Animal migration optimization: an optimization algorithm inspired by animal migration behavior, Neural Comput. Appl., In press.
30. J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceeding of the IEEE International Conference on Neural Networks, vol. 4, IEEE, 1995, pp. 1942-1948.
31. Talatahari S., Kheirollahi M., Farahmandpour C., et al. A multi-stage particle swarm for optimum design of truss structures. Neural Comput. Appl. 2013, 23(5):1297-1309.
32. Sadeghpour M., Salarieh H., Alasty A. Minimum entropy control of chaos via online particle swarm optimization method. Appl. Math. Model. 2012, 36(8):3931-3940.
33. Zhang Y., Huang D., Ji M., et al. Image segmentation using PSO and PCM with Mahalanobis distance. Expert Syst. Appl. 2011, 38(7):9036-9040.
34. Karaboga D., Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 2007, 39(3):459-471.
35. Shumeet B. Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning 1994, Carnegie Mellon University, Pittsburgh, PA.
36. Gandomi A.H., Alavi A.H. Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf. Sci. 2011, 181(23):5227-5239.
37. Erol O.K., Eksin I. A new optimization method: Big Bang-Big Crunch. Adv. Eng. Softw. 2006, 37(2):106-111.
38. Kaveh A., Talatahari S. Size optimization of space trusses using Big Bang-Big Crunch algorithm. Comput. Struct. 2009, 87(17-18):1129-1140.
39. Kaveh A., Talatahari S. Optimal design of Schwedler and ribbed domes via hybrid Big Bang-Big Crunch algorithm. J. Constr. Steel Res. 2010, 66(3):412-419.
40. Kaveh A., Talatahari S. A discrete big bang-big crunch algorithm for optimal design of skeletal structures. Asian J. Civ. Eng. 2010, 11(1):103-122.
41. Simon D. Biogeography-based optimization. IEEE Trans. Evol. Comput. 2008, 12(6):702-713.
42. Kaveh A., Talatahari S. A novel heuristic optimization method: charged system search. Acta Mech. 2010, 213(3-4):267-289.
43. Talatahari S., Sheikholeslami R., Shadfaran M., et al. Optimum design of gravity retaining walls using charged system search algorithm. Math. Probl. Eng. 2012, 2012:1-10.
44. Gandomi A.H., Alavi A.H. Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simulat. 2012, 17(12):4831-4845.
45. G. Wang, L. Guo, H. Wang, et al., Incorporating mutation scheme into krill herd algorithm for global numerical optimization, Neural Comput. Appl., In press.
46. Wang G., Guo L., Gandomi A.H., et al. Lévy-flight krill herd algorithm. Math. Probl. Eng. 2013, 2013:1-14.
47. Yao X., Liu Y., Lin G. Evolutionary programming made faster. IEEE Trans. Evol. Comput. 1999, 3(2):82-102.
48. Yang X.-S., Cui Z., Xiao R., et al. Swarm Intelligence and Bio-Inspired Computation 2013, Elsevier, Waltham, MA.
49. Niksirat M., Ghatee M., Mehdi Hashemi S. Multimodal K-shortest viable path problem in Tehran public transportation network and its solution applying ant colony and simulated annealing algorithms. Appl. Math. Model. 2012, 36(11):5709-5726.
50. Yang X.S., Deb S. Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optim. 2010, 1(4):330-343.
51. Gandomi A.H., Yun G.J., Yang X.-S., et al. Chaos-enhanced accelerated particle swarm optimization. Commun. Nonlinear Sci. Numer. Simulat. 2013, 18(2):327-340.
52. W. Khatib, P. Fleming, The stud GA: a mini revolution?, in: A. Eiben, T. Back, M. Schoenauer, H. Schwefel (Eds.) Proc. of the 5th International Conference on Parallel Problem Solving from Nature, Springer-Verlag, New York, USA, 1998, pp. 683-691.
53. G.-G. Wang, A.H. Gandomi, A.H. Alavi, G.-S. Hao, Hybrid krill herd algorithm with differential evolution for global numerical optimization, Neural Comput. Appl., in press.