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Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems

Published: 01 January 2018 Publication History

Abstract

Earthworms can aerate the soil with their burrowing action and enrich the soil with their waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called earthworm optimisation algorithm EWA, is proposed in this paper. The EWA method is inspired by the two kinds of reproduction Reproduction 1 and Reproduction 2 of the earthworms. Reproduction 1 generates only one offspring by itself. Reproduction 2 is to generate one or more than one offspring at one time, and this can successfully be done by nine improved crossover operators. In addition, Cauchy mutation CM is added to EWA method. Nine different EWA methods with one, two and three offsprings based on nine improved crossover operators are respectively proposed. The results show that EWA23 performs the best and it can find the better fitness on most benchmarks than others.

References

[1]
Ali, M. and Pant, M. (2010) 'Improving the performance of differential evolution algorithm using Cauchy mutation', Soft Computing, Vol. 15, No. 5, pp. 991-1007.
[2]
Bäck, T. (1996) Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford University Press, Oxford, UK.
[3]
Beyer, H. and Schwefel, H. (2002) Natural Computing, Kluwer Academic Publishers, Dordrecht, Netherlands.
[4]
Blakemore, R. (2001) Tasmanian Worm Grows Second Head [online] http://www.qvmag.tas.gov.au/zoology/invertebrata/printarchive/printtext/inv20aitems.html#20blakemore (accessed 5 October 2015).
[5]
Cai, X., Wang, L., Kang, Q. and Wu, Q. (2014) 'Bat algorithm with Gaussian walk', International Journal of Bio-Inspired Computation, Vol. 6, No. 3, pp. 166-174.
[6]
Cui, Z., Fan, S., Zeng, J. and Shi, Z. (2013a) 'APOA with parabola model for directing orbits of chaotic systems', International Journal of Bio-Inspired Computation, Vol. 5, No. 1, pp. 67-72.
[7]
Cui, Z., Fan, S., Zeng, J. and Shi, Z. (2013b) 'Artificial plant optimisation algorithm with three-period photosynthesis', International Journal of Bio-Inspired Computation, Vol. 5, No. 2, pp. 133-139.
[8]
Dorigo, M., Maniezzo, V. and Colorni, A. (1996) 'Ant system: optimization by a colony of cooperating agents', IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 26, No. 1, pp. 29-41.
[9]
Duan, H., Zhao, W., Wang, G. and Feng, X. (2012) 'Test-sheet composition using analytic hierarchy process and hybrid metaheuristic algorithm TS/BBO', Mathematical Problems in Engineering, Vol. 2012, pp. 1-22.
[10]
Fong, S., Deb, S. and Yang, X-S. (2015) 'A heuristic optimization method inspired by wolf preying behavior', Neural Computing and Applications, Vol. 26, No. 7, pp. 1725-1738.
[11]
Gandomi, A.H. and Alavi, A.H. (2011) 'Multi-stage genetic programming: a new strategy to nonlinear system modeling', Information Sciences, Vol. 181, No. 23, pp. 5227-5239.
[12]
Gandomi, A.H. and Alavi, A.H. (2012) 'Krill herd: a new bio-inspired optimization algorithm', Communications in Nonlinear Science and Numerical Simulation, Vol. 17, No. 12, pp. 4831-4845.
[13]
Gandomi, A.H., Talatahari, S., Tadbiri, F. and Alavi, A.H. (2013) 'Krill herd algorithm for optimum design of truss structures', International Journal of Bio-Inspired Computation, Vol. 5, No. 5, pp. 281-288.
[14]
Gates, G. (1949) 'Regeneration in an earthworm, Eisenia foetida (Savigny) 1826. I. Anterior regeneration', The Biological Bulletin, Vol. 96, No. 2, pp. 129-139.
[15]
Gates, G. (1953) 'On regenerative capacity of earthworms of the family Lumbricidae', American Midland Naturalist, Vol. 50, No. 2, pp. 414-419.
[16]
Goldberg, D.E. (1998) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, New York.
[17]
Hand, D. (1992) Genetic Programming: on the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA.
[18]
Hickman, C.P., Roberts, L.S., Larson, A., L'Anson, H. and Eisenhour, D.J. (2006) Integrated Principles of Zoology, McGraw-Hill, New York.
[19]
Hu, Y., Yin, M. and Li, X. (2011) 'A novel objective function for job-shop scheduling problem with fuzzy processing time and fuzzy due date using differential evolution algorithm', The International Journal of Advanced Manufacturing Technology, Vol. 56, No. 9, pp. 1125-1138.
[20]
Karaboga, D. and Basturk, B. (2007) 'A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm', Journal of Global Optimization, Vol. 39, No. 3, pp. 459-471.
[21]
Kennedy, J. and Eberhart, R. (1995) 'Particle swarm optimization', in Proceeding of the IEEE International Conference on Neural Networks, IEEE, pp. 1942-1948.
[22]
Khatib, W. and Fleming, P. (1998) 'The stud GA: a mini revolution?', in Eiben, A., Back, T., Schoenauer, M. and Schwefel, H. (Eds.): Proc. of the 5th International Conference on Parallel Problem Solving from Nature, Springer-Verlag, New York, USA, pp. 683-691.
[23]
Kirkpatrick, S., Gelatt Jr., C.D. and Vecchi, M.P. (1983) 'Optimization by simulated annealing', Science, Vol. 220, No. 4598, pp. 671-680.
[24]
Li, X. and Yin, M. (2012) 'Self-adaptive constrained artificial bee colony for constrained numerical optimization', Neural Computing and Applications, Vol. 24, Nos. 3-4, pp. 723-734.
[25]
Li, X. and Yin, M. (2013) 'Multiobjective binary biogeography based optimization for feature selection using gene expression data', IEEE Transactions on NanoBioscience, Vol. 12, No. 4, pp. 343-353.
[26]
Li, X., Wang, J. and Yin, M. (2013) 'Enhancing the performance of cuckoo search algorithm using orthogonal learning method', Neural Computing and Applications, Vol. 24, No. 6, pp. 1233-1247.
[27]
Li, X., Zhang, J. and Yin, M. (2014) 'Animal migration optimization: an optimization algorithm inspired by animal migration behavior', Neural Computing and Applications, Vol. 24, Nos. 7-8, pp. 1867-1877.
[28]
Mirjalili, S. (2015a) 'The ant lion optimizer', Advances in Engineering Software, Vol. 83, pp. 80-98.
[29]
Mirjalili, S. (2015b) 'Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems', Neural Computing and Applications,
[30]
Mirjalili, S., Mirjalili, S.M. and Hatamlou, A. (2015) 'Multiverse optimizer: a nature-inspired algorithm for global optimization', Neural Computing and Applications,
[31]
Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014) 'Let a biogeography-based optimizer train your multi-layer perceptron', Information Sciences, Vol. 269, pp. 188-209.
[32]
Mirjalili, S., Mirjalili, S.M. and Yang, X-S. (2013) 'Binary bat algorithm', Neural Computing and Applications, Vol. 25, Nos. 3-4, pp. 663-681.
[33]
Saaty, T.L. (1980) The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, McGraw-Hill, New York.
[34]
Saremi, S., Mirjalili, S. and Lewis, A. (2014) 'Biogeography-based optimisation with chaos', Neural Computing and Applications, Vol. 25, No. 5, pp. 1077-1097.
[35]
Simon, D. (2008) 'Biogeography-based optimization', IEEE Transactions on Evolutionary Computation, Vol. 12, No. 6, pp. 702-713.
[36]
Srivastava, P.R., Varshney, A., Nama, P. and Yang, X.S. (2012) 'Software test effort estimation: a model based on cuckoo search', International Journal of Bio-Inspired Computation, Vol. 4, No. 5, pp. 278-285.
[37]
Storn, R. and Price, K. (1997) 'Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces', Journal of Global Optimization, Vol. 11, No. 4, pp. 341-359.
[38]
Wang, G-G., Deb, S. and Cui, Z. (2015) 'Monarch butterfly optimization', Neural Computing and Applications,
[39]
Wang, G-G., Gandomi, A.H. and Alavi, A.H. (2014a) 'An effective krill herd algorithm with migration operator in biogeography-based optimization', Applied Mathematical Modelling, Vol. 38, Nos. 9-10, pp. 2454-2462.
[40]
Wang, G-G., Gandomi, A.H. and Alavi, A.H. (2014b) 'Stud krill herd algorithm', Neurocomputing, Vol. 128, pp. 363-370.
[41]
Wang, G-G., Gandomi, A.H., Yang, X-S. and Alavi, A.H. (2014c) 'A novel improved accelerated particle swarm optimization algorithm for global numerical optimization', Engineering Computations, Vol. 31, No. 7, pp. 1198-1220.
[42]
Wang, G-G., Gandomi, A.H., Zhao, X. and Chu, H.E. (2014d) 'Hybridizing harmony search algorithm with cuckoo search for global numerical optimization', Soft Computing,
[43]
Wang, G-G., Guo, L., Gandomi, A.H., Hao, G-S. and Wang, H. (2014e) 'Chaotic krill herd algorithm', Information Sciences, Vol. 274, pp. 17-34.
[44]
Wang, G., Guo, L., Wang, H., Duan, H., Liu, L. and Li, J. (2014f) 'Incorporating mutation scheme into krill herd algorithm for global numerical optimization', Neural Computing and Applications, Vol. 24, Nos. 3-4, pp. 853-871.
[45]
Wang, H., Li, H., Liu, Y., Li, C. and Zeng, S. (2007) 'Opposition-based particle swarm algorithm with Cauchy mutation', in IEEE Congress on Evolutionary Computation 2007 (CEC 2007), IEEE, pp. 4750-4756.
[46]
Wang, H., Wu, Z., Rahnamayan, S., Liu, Y. and Ventresca, M. (2011) 'Enhancing particle swarm optimization using generalized opposition-based learning', Information Sciences, Vol. 181, No. 20, pp. 4699-4714.
[47]
Wu, Q. (2011) 'Hybrid forecasting model based on support vector machine and particle swarm optimization with adaptive and Cauchy mutation', Expert Systems with Applications, Vol. 38, No. 8, pp. 9070-9075.
[48]
Xue, F., Cai, Y., Cao, Y., Cui, Z. and Li, F. (2015) 'Optimal parameter settings for bat algorithm', International Journal of Bio-Inspired Computation, Vol. 7, No. 2, pp. 125-128.
[49]
Yang, X.S. (2010) Nature-inspired Metaheuristic Algorithms, Luniver Press, Frome.
[50]
Yang, X.S. and Cui, Z. (2014) 'Bio-inspired computation: success and challenges of IJBIC', International Journal of Bio-Inspired Computation, Vol. 6, No. 1, pp. 1-6.
[51]
Yang, X.S. and Deb, S. (2009) 'Cuckoo search via Lévy flights', in Abraham, A., Carvalho, A., Herrera, F. and Pai, V. (Eds.): Proceeding of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), IEEE Publications, Coimbatore, India, pp. 210-214.
[52]
Yang, X-S., Cui, Z., Xiao, R., Gandomi, A.H. and Karamanoglu, M. (2013) Swarm Intelligence and Bio-Inspired Computation, Elsevier, Waltham, MA.
[53]
Yao, X., Liu, Y. and Lin, G. (1999) 'Evolutionary programming made faster', IEEE Transactions on Evolutionary Computation, Vol. 3, No. 2, pp. 82-102.

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Published In

cover image International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation  Volume 12, Issue 1
January 2018
69 pages
ISSN:1758-0366
EISSN:1758-0374
Issue’s Table of Contents

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Inderscience Publishers

Geneva 15, Switzerland

Publication History

Published: 01 January 2018

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