Abstract
A new algorithm has been formulated based on the basic animal migration optimization (AMO) algorithm. During the course of this proposed work it was revealed for the first time that AMO algorithm is a true replica of DEGL algorithm and when mathematical analysis was carried out the similarities were brought to notice which was till date not reported. Further during the investigation it was also witnessed that AMO algorithm in its virgin form is capable of delivering a competitive performance when applied to optimization of non-linear functions referring to CEC 2014 test suite. Such successful achievements using basic AMO algorithm inspired the present authors to take it as a challenge for exploring the possibility of improving the conventional AMO algorithm with an objective of providing it with a new shape and build an efficient framework for tackling the handicaps encountered in original AMO algorithm. In fact the incessant quest of modifying the basic algorithm gave birth to a new algorithm known as augmented animal migration optimization algorithm in the backdrop of differential evolution (AAMO-DE). The proposed algorithm even though incorporated the philosophy of Jaya algorithm,it created a memory hierarchy of worst solutions generated in each iteration. And unlike Jaya algorithm where the position update equation employ the current worst solution the proposed one picks up a random worst solution from the archive to achieve better diversity and also drops the global best term which often yields biased solutions. The proposed AAMO-DE algorithm could accomplish highly encouraging results when CEC 2014 test suite problems were subjected to validation check. The performance of the proposed algorithm was truly impressive in comparison with its counterparts comprising of state-of-the-art algorithms. In case of application to real world engineering problems the outcomes were very promising and really proves that the proposed AAMO-DE algorithm is not only a strong contender in the optimization community exhibiting excellent results but is also a potentially robust algorithm and has the ability to converge towards global optima without being trapped in local minima as evident from test examples.
Similar content being viewed by others
References
Bansal JC, Sharma H, Jadon SS, Clerc M (2014) Spider monkey optimization algorithm for numerical optimization. Memet Comput 6(1):31–47
Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657
Brest J, Maučec MS, Bošković B (2016) il-shade: Improved l-shade algorithm for single objective real-parameter optimization. In: Evolutionary computation (CEC), 2016 IEEE congress on, IEEE, pp 1188–1195
Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112
Chu SC, Tsai PW, Pan JS (2006) Cat swarm optimization. In: Pacific Rim international conference on artificial intelligence, Springer, pp 854–858
Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127
Coello Coello CA (2000) Constraint-handling using an evolutionary multiobjective optimization technique. Civ Eng Syst 17(4):319–346
Das S, Abraham A, Chakraborty UK, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. IEEE Trans Evol Comput 13(3):526–553
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2–4):311–338
Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. no. 99TH8406), IEEE, vol 2, pp 1470–1477
Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Micro machine and human science, 1995. MHS’95. Proceedings of the sixth international symposium on, IEEE, pp 39–43
Erlich I, Rueda JL, Wildenhues S, Shewarega F (2014) Evaluating the mean-variance mapping optimization on the IEEE-CEC 2014 test suite. In: Evolutionary computation (CEC), 2014 IEEE congress on, IEEE, pp 1625–1632
Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68
Goudos SK, Moysiadou V, Samaras T, Siakavara K, Sahalos JN (2010) Application of a comprehensive learning particle swarm optimizer to unequally spaced linear array synthesis with sidelobe level suppression and null control. IEEE Antennas Wirel Propag Lett 9:125–129
He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99
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
Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112:283–294
Li L, Huang Z, Liu F, Wu Q (2007) A heuristic particle swarm optimizer for optimization of pin connected structures. Comput Struct 85(7–8):340–349
Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877
Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10(2):629–640
Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579
Maia RD, de Castro LN, Caminhas WM (2014) Real-parameter optimization with optbees. In: Evolutionary computation (CEC), 2014 IEEE congress on, IEEE, pp 2649–2655
Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696
Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1(4):355–366
Mezura-Montes E, Coello CAC (2008) An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 37(4):443–473
Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191
Pappula L, Ghosh D (2014) Linear antenna array synthesis using cat swarm optimization. AEU Int J Electron Commun 68(6):540–549
Poláková R, Tvrdík J, Bujok P (2014) Controlled restart in differential evolution applied to CEC 2014 benchmark functions. In: Evolutionary computation (CEC), 2014 IEEE congress on, IEEE, pp 2230–2236
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417
Rajo-Iglesias E, Quevedo-Teruel O (2007) Linear array synthesis using an ant-colony-optimization-based algorithm. IEEE Antennas Propag Mag 49(2):70–79
Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34
Rao RV, Waghmare G (2017) A new optimization algorithm for solving complex constrained design optimization problems. Eng Optim 49(1):60–83
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf Sci 179(13):2232–2248
Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13(5):2592–2612
Saxena P, Kothari A (2016a) Ant lion optimization algorithm to control side lobe level and null depths in linear antenna arrays. AEU Int J Electron Commun 70(9):1339–1349
Saxena P, Kothari A (2016b) Linear antenna array optimization using flower pollination algorithm. SpringerPlus 5(1):1
Saxena P (2016) Kothari A (2016c) Optimal pattern synthesis of linear antenna array using grey wolf optimization algorithm. Int J Antennas Propag. https://doi.org/10.1155/2016/1205970
Sharma H, Sharma S, Kumar S (2016) Lbest gbest artificial bee colony algorithm. In: Advances in computing, communications and informatics (ICACCI), 2016 international conference on, IEEE, pp 893–898
Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713
Singh U, Salgotra R (2016) Optimal synthesis of linear antenna arrays using modified spider monkey optimization. Arab J Sci Eng 41(8):2957–2973
Storn R, Price K (1996) Minimizing the real functions of the ICEC’96 contest by differential evolution. In: Evolutionary computation, 1996. Proceedings of IEEE international conference on, IEEE, pp 842–844
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
Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for differential evolution. In: Evolutionary computation (CEC), 2013 IEEE congress on, IEEE, pp 71–78
Tanabe R, Fukunaga AS (2014) Improving the search performance of shade using linear population size reduction. In: Evolutionary computation (CEC), 2014 IEEE congress on, IEEE, pp 1658–1665
Tang W, Wu Q, Saunders J, (2006) Bacterial foraging algorithm for dynamic environments. In: Evolutionary computation, (2006) CEC 2006. IEEE congress on, IEEE, pp 1324–1330
Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66
Wu G, Mallipeddi R, Suganthan PN, Wang R, Chen H (2016) Differential evolution with multi-population based ensemble of mutation strategies. Inf Sci 329:329–345
Yang M, Guan J, Cai Z, Li C (2014) A self-adaptive differential evolutionary algorithm based on population reduction with minimum distance. Int J Innov Comput Appl 6(1):13–24
Yang XS (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds) International symposium on stochastic algorithms: Foundations and Applications, SAGA 2009. Lecture Notes in Computer Science, vol 5792. Springer. Berlin, Heidelberg, pp 169–178
Yang XS (2010) A New Metaheuristic Bat-Inspired Algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Studies in Computational Intelligence, vol 284. Springer. Berlin, Heidelberg, pp 65–74
Yang XS (2012) Flower Pollination Algorithm for Global Optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional Computation and Natural Computation, UCNC 2012. Lecture Notes in Computer Science, vol 7445. Springer. Berlin, Heidelberg, pp 240–249
Yu C, Kelley L, Zheng S, Tan Y (2014) Fireworks algorithm with differential mutation for solving the CEC 2014 competition problems. In: Evolutionary computation (CEC), 2014 IEEE congress on, IEEE, pp 3238–3245
Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958
Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178(15):3043–3074
Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Subhashini, K.R., Chinta, P. An augmented animal migration optimization algorithm using worst solution elimination approach in the backdrop of differential evolution. Evol. Intel. 12, 273–303 (2019). https://doi.org/10.1007/s12065-019-00223-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12065-019-00223-8