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Soft Computing

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24.06.2019 | Focus

A simple differential evolution with time-varying strategy for continuous optimization

verfasst von: Gaoji Sun, Geni Xu, Nan Jiang

Erschienen in: Soft Computing | Ausgabe 4/2020

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Abstract

We propose a novel simple variant of differential evolution (DE) algorithm and call it TVDE because it is a time-varying strategy-based DE algorithm. In our TVDE, three functions with time-varying characteristics are applied to create a new mutation operator and automatically tune the values of two key control parameters (scaling factor and crossover rate) during the evolutionary process. To verify its availability, the proposed TVDE has been tested on the CEC 2014 benchmark sets and four real-life problems and compared to seven state-of-the-art DE variants. The experimental results indicate that the proposed TVDE algorithm obtains the best overall performance among the eight DE algorithms.

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Al-Dabbagh RD, Neri F, Idris N, Baba MS (2018) Algorithmic design issues in adaptive differential evolution schemes: review and taxonomy. Swarm Evol Comput 43:284–311CrossRef

Arce F, Zamora E, Sossa H, Barróna R (2018) Differential evolution training algorithm for dendrite morphological neural networks. Appl Soft Comput 68:303–313CrossRef

Črepinšek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(3):1–33CrossRef

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

Das S, Suganthan PN (2011b) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur University, Kolkata, India, and Nanyang Technological University, Singapore, Dec. 2010

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 27:99sC126CrossRef

Draa A, Chettah K, Talbi H (2018) Compound sinusoidal differential evolution algorithm for continuous optimization. Comput Swarm Evol. https://doi.org/10.1016/j.swevo.2018.10.001 CrossRef

Fan Q, Yan X (2016) Self-adaptive differential evolution algorithm with zoning evolution of control parameters and adaptive mutation strategies. IEEE Trans Cybern 46:219–232CrossRef

García-Martínez C, Lozano M, Herrera F, Molina D, Sánchez A (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res 185(3):1088–1113CrossRef

Gong W, Cai Z (2013) Differential evolution with ranking-based mutation operators. IEEE Trans Cybern 43(6):2066C2081CrossRef

Gong W, Cai Z, Liang D (2015) Adaptive ranking mutation operator based differential evolution for constrained optimization. IEEE Trans Cybern 45:716–727CrossRef

Han MF, Liao SH, Chang JY, Lin CT (2013) Dynamic group-based differential evolution using a self-adaptive strategy for global optimization problems. Appl Intell 39(1):41C56CrossRef

Herrera F, Lozano M (2000) Gradual distributed real-coded genetic algorithms. IEEE Trans Evol Comput 4(1):43–63CrossRef

Hu J, Guo P, Poh KL (2018) Flexible capacity planning for engineering systems based on decision rules and differential evolution. Comput Ind Eng 123:254–262CrossRef

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):482C500CrossRef

Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Zhengzhou University, China, and Nanyang Technological University, Singapore

Liang J, Xu W, Yue C, Yu K, Song H, Crisalle OD, Qu B (2019) Multimodal multiobjective optimization with differential evolution. Swarm Evol Comput 44:1028–1059CrossRef

Mohamed AW, Sabry HZ (2012) Constrained optimization based on modified differential evolution algorithm. Inf Sci 194:171–208CrossRef

Mohamed AW, Hadi AA, Jambi KM (2018) Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization. Comput Swarm Evol. https://doi.org/10.1016/j.swevo.2018.10.006 CrossRef

Mukherjee R, Debchoudhury S, Das S (2016) Modified differential evolution with locality induced genetic operators for dynamic optimization. Eur J Oper Res 253:337–355CrossRef

Opara K, Arabas J (2018) Comparison of mutation strategies in differential evolution-a probabilistic perspective. Swarm Evol Comput 39:53–69CrossRef

Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398C417CrossRef

Qiu X, Xu JX, Tan KC, Abbass HA (2016) Adaptive cross-generation differential evolution operators for multiobjective optimization. IEEE Trans Evol Comput 20:232–244CrossRef

Sarker RA, Elsayed SM, Ray T (2014) Differential evolution with dynamic parameters selection for optimization problems. IEEE Trans Evol Comput 18(5):689C707CrossRef

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

Sun G, Peng J, Zhao R (2018) Differential evolution with individual-dependent and dynamic parameter adjustment. Soft Comput 22:5747–5773CrossRef

Sun G, Lan Y, Zhao R (2019) Differential evolution with Gaussian mutation and dynamic parameter adjustment. Soft Comput 23:1615–1642CrossRef

Sun G, Yang B, Yang Z, Xu G (2019) An adaptive differential evolution with combined strategy for global numerical optimization. Soft Comput. https://doi.org/10.1007/s00500-019-03934-3 CrossRef

Tang L, Dong Y, Liu J (2015) Differential evolution with an individual-dependent mechanism. IEEE Trans Evol Comput 19(4):560C574

Wang H, Rahnamayan S, Sun H, Omran MGH (2013) Gaussian bare-bones differential evolution. IEEE Trans Cybern 43(2):634C647

Yi W, Zhou Y, Gao L, Li X, Zhang C (2018) Engineering design optimization using an improved local search based epsilon differential evolution algorithm. J Intell Manuf 29:1559–1580CrossRef

Yu WJ, Shen M, Chen WN, Zhan ZH, Gong YJ, Lin Y (2014) Differential evolution with two-level parameter adaption. IEEE Trans Cybern 44(7):1080C1099CrossRef

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

Zhou XG, Zhang GJ (2019) Differential evolution with underestimation-based multimutation strategy. IEEE Trans Cybern 49:1353–1364CrossRef

Zhu T, Hao Y, Luo W, Ning H (2018) Learning enhanced differential evolution for tracking optimal decisions in dynamic power systems. Appl Soft Comput 67:812–821CrossRef

Titel: A simple differential evolution with time-varying strategy for continuous optimization
verfasst von: Gaoji Sun
Geni Xu
Nan Jiang
Publikationsdatum: 24.06.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 4/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-04159-0

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