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Soft Computing

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18.02.2020 | Foundations

A simple two-phase differential evolution for improved global numerical optimization

verfasst von: Arka Ghosh, Swagatam Das, Asit Kr. Das

Erschienen in: Soft Computing | Ausgabe 8/2020

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Abstract

In the evolutionary computing community, differential evolution (DE) is well appreciated as a simple yet versatile population-based, non-convex optimizer designed for continuous optimization problems. A simple two-phase DE algorithm is presented in this article, which aims to identify promising basins of attraction on a non-convex functional landscape in the first phase, and starting from those previously identified search regions, a success history-based switch parameter DE is employed to further fine tune the search process leading to the optima of the landscape. Our proposed framework has been validated on the well-known IEEE Congress on Evolutionary Computation (CEC) benchmark suites (CEC 2013, 2014 and 2017). Results of the proposed method are compared with corresponding CEC winners (SHADE for CEC 2013, L-SHADE for CEC 2014 and jSO for CEC 2017).

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Ali MZ, Awad NH, Suganthan PN, Reynolds RG (2017) An adaptive multipopulation differential evolution with dynamic population reduction. IEEE Trans Cybern 47(9):2768–2779CrossRef

Awad N, Liang J, Qu B, Suganthan P (2016) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained real-parameter numerical optimization. Technical Report, Nanyang Technological University, Singapore

Brest J, Maucec MS, Boskovic B (2017) Single objective real-parameter optimization: Algorithm jSO. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE

Chatterjee I, Zhou M (2017) Differential evolution algorithms under multi-population strategy. In: 2017 26th wireless and optical communication conference (WOCC). IEEE

Cui L, Li G, Lin Q, Chen J, Lu N (2016) Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Comput Oper Res 67:155–173MathSciNetMATHCrossRef

Cui L, Li G, Zhu Z, Lin Q, Wong K-C, Chen J, Lu N, Lu J (2018) Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism. Inf Sci 422:122–143MathSciNetCrossRef

Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31CrossRef

Das S, Mullick SS, Suganthan PN (2016) Recent advances in differential evolution-an updated survey. Swarm Evol Comput 27:1–30CrossRef

Draa A, Bouzoubia S, Boukhalfa I (2015) A sinusoidal differential evolution algorithm for numerical optimisation. Appl Soft Comput 27:99–126CrossRef

Epitropakis MG, Tasoulis DK, Pavlidis NG, Plagianakos VP, Vrahatis MN (2011) Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Trans Evol Comput 15(1):99–119CrossRef

Gao W-F, Yen GG, Liu S-Y (2015) A dual-population differential evolution with coevolution for constrained optimization. IEEE Trans Cybern 45(5):1108–1121CrossRef

Ghosh A, Das S, Mullick SS, Mallipeddi R, Das AK (2017a) A switched parameter differential evolution with optional blending crossover for scalable numerical optimization. Appl Soft Comput 57:329–352CrossRef

Ghosh A, Das S, Panigrahi BK, Das AK (2017b) A noise resilient differential evolution with improved parameter and strategy control. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE, pp 2590–2597

Ghosh A, Mallipeddi R, Das S, Das AK (2018) A switched parameter differential evolution with multi-donor mutation and annealing based local search for optimization of lennard-jones atomic clusters. In: 2018 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8

Ghosh A, Das S, Das AK, Gao L (2019) Reusing the past difference vectors in differential evolution–a simple but significant improvement. IEEE Trans Cybern

Gosh A, Das S, Mallipeddi R, Das AK, Dash SS (2017) A modified differential evolution with distance-based selection for continuous optimization in presence of noise. IEEE Access 5:26944–26964CrossRef

Guo S-M, Yang C-C (2015) Enhancing differential evolution utilizing eigenvector-based crossover operator. IEEE Trans Evol Comput 19(1):31–49MathSciNetCrossRef

Islam SM, Das S, Ghosh S, Roy S, Suganthan PN (2012) An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans Syst Man Cybern Part B (Cybern) 42(2):482–500CrossRef

Li G, Lin Q, Cui L, Du Z, Liang Z, Chen J, Lu N, Ming Z (2016) A novel hybrid differential evolution algorithm with modified code and jade. Appl Soft Comput 47:577–599CrossRef

Liang J, Qu B, Suganthan P (2013a) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore

Liang J, Qu B, Suganthan P, Hernández-Díaz AG (2013b) Problem definitions and evaluation criteria for the cec 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report, vol 201212, no 34, pp 281–295

Lynn N, Suganthan PN (2015) Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol Comput 24:11–24CrossRef

Mallipeddi R, Suganthan PN, Pan Q-K, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696CrossRef

Mohamed AW, Mohamed AK (2019) Adaptive guided differential evolution algorithm with novel mutation for numerical optimization. Int J Mach Learn Cybern 10(2):253–277CrossRef

Mohamed AW, Suganthan PN (2018) Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation. Soft Comput 22(10):3215–3235CrossRef

Mohamed AW, Hadi AA, Fattouh AM, Jambi KM (2017) LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE, pp 145–152

Mohamed AW, Hadi AA, Jambi KM (2019) Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization. Swarm Evol Comput 50:100455 CrossRef

Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417CrossRef

Qiu X, Tan KC, Xu J-X (2017) Multiple exponential recombination for differential evolution. IEEE Trans Cybern 47(4):995–1006CrossRef

Qu B-Y, Suganthan PN, Liang J-J (2012) Differential evolution with neighborhood mutation for multimodal optimization. IEEE Trans Evol Comput 16(5):601–614CrossRef

Roy S, Islam SM, Das S, Ghosh S (2013) Multimodal optimization by artificial weed colonies enhanced with localized group search optimizers. Appl Soft Comput 13(1):27–46CrossRef

Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetMATHCrossRef

Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for differential evolution. In: 2013 IEEE congress on evolutionary computation. IEEE, pp 71–78

Tanabe R, Fukunaga AS (2014) Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC). IEEE

Tang L, Dong Y, Liu J (2015) Differential evolution with an individual-dependent mechanism. IEEE Trans Evol Comput 19(4):560–574CrossRef

Tong L, Dong M, Jing C (2018) An improved multi-population ensemble differential evolution. Neurocomputing 290:130–147CrossRef

Wu G, Mallipeddi R, Suganthan P, Wang R, Chen H (2016) Differential evolution with multi-population based ensemble of mutation strategies. Inf Sci 329:329–345CrossRef

Wu G, Shen X, Li H, Chen H, Lin A, Suganthan PN (2018) Ensemble of differential evolution variants. Inf Sci 423:172–186MathSciNetCrossRef

Yu WJ, Zhang J (2011) Multi-population differential evolution with adaptive parameter control for global optimization. In: Proceedings of the 13th annual conference on Genetic and evolutionary computation—GECCO’11. ACM Press

Zhang J, Sanderson AC (2009) Jade: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958CrossRef

Zhong J-H, Shen M, Zhang J, Chung HS-H, Shi Y-H, Li Y (2013) A differential evolution algorithm with dual populations for solving periodic railway timetable scheduling problem. IEEE Trans Evol Comput 17(4):512–527CrossRef

Zhou X-G, Zhang G-J (2018) Differential evolution with underestimation-based multimutation strategy. IEEE Trans Cybern 99:1–12

Titel: A simple two-phase differential evolution for improved global numerical optimization
verfasst von: Arka Ghosh
Swagatam Das
Asit Kr. Das
Publikationsdatum: 18.02.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 8/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-04750-w

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