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Adaptive niching selection-based differential evolution for global optimization

  • Optimization
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Abstract

Niching techniques have been widely incorporated into differential evolution (DE) to improve the diversity of the search. Existing niching-based DE schemes, however, typically employ a certain niching technique during the entire evolution. Since different niching techniques possess different diversity preserving properties, employing a fixed niching technique during DE evolution may have limited performance. In this paper, we propose an adaptive niching selection-based DE for global optimization problems. In the proposed method, instead of employing a certain fixed niching technique, an adaptive niching selection scheme has been devised. In this scheme, multiple niching techniques are employed and adaptively used during the DE evolution, thus properly preserving the population diversity. Further, to appropriately facilitate the adaptive niching selection, both the fitness improvement and entropy of population resulting from niching techniques have been considered to measure their effectiveness. The performance of the proposed method has been evaluated on multi-modal and hybrid composition test functions and compared with related methods. The results show that our proposed method can deliver a satisfying performance and is competitive with state-of-the-art algorithms.

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Data availability

The datasets are available from the corresponding author on reasonable request.

References

  • Abbas Q, Ahmad J, Jabeen H (2015) A novel tournament selection based differential evolution variant for continuous optimization problems. Math Probl Eng Theory Methods Appl 2015:1–21

    Article  MATH  Google Scholar 

  • Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-qaness MA, Gandomi AH (2021a) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157(11):107250

  • Abualigah L, Elaziz MA, Sumari P, WooGeem Z, Gandomi AH (2021b) Reptile search algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191(11):116158

  • Abualigah L, Diabat A, Mirjalili S, Elaziz MA, Gandomi AH (2021c) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

  • Agushaka JO, Ezugwu AE, Abualigah L (2022) Dwarf Mongoose optimization algorithm. Comput Methods Appl Mech Eng 391:114570

    Article  MathSciNet  MATH  Google Scholar 

  • Al-Qaness M, Ewees AA (2022) Modified aquila optimizer for forecasting oil production. Geo-spatial Inf Sci. https://doi.org/10.1080/10095020.2022.2068385

    Article  Google Scholar 

  • Al-Qaness M, Ewees AA, Elaziz MA (2021) Modified whale optimization algorithm for solving unrelated parallel machine scheduling problems. Soft Comput 25:9545

    Article  Google Scholar 

  • Al-Qaness M, Ewees AA, Fan H, Abualigah L, Elaziz MA (2022) Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting. Appl Energy 314(15):118851

    Article  Google Scholar 

  • Auer P, Cesa-Bianchi N, Fischer P (2002) Finite-time analysis of the multiarmed bandit problem. Mach Learn 47(2–3):235–256

    Article  MATH  Google Scholar 

  • Cai Y, Wang J, Chen Y, Chen Y (2016) Adaptive direction information in differential evolution for numerical optimization. Soft Comput 20(2):465–494

    Article  Google Scholar 

  • Cai Y, Sun G, Wang T, Tian H, Chen Y, Wang J (2017) Neighborhood-adaptive differential evolution for global numerical optimization. Appl Soft Comput 59:659–706

    Article  Google Scholar 

  • Das S, Abraham A, Chakraborty UK, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. IEEE Trans Evol Comput 13(13):526–553

    Article  Google Scholar 

  • Das S, Maity S, Qu B, Suganthan PN (2011) Real-parameter evolutionary multimodal optimization—a survey of the state-of-the-art. Swarm Evolut Comput 1(2):71–88

    Article  Google Scholar 

  • Davis L (1989) Adapting operator probabilities in genetic algorithms. In: Proceedings of The third international conference on genetic algorithms, pp 61-69

  • Fialho Á (2010) Adaptive operator selection for optimization. Ph.D. dissertation

  • Fialho A, Da Costa L, Schoenauer M, Sebag M (2010) Analyzing bandit-based adaptive operator selection mechanisms. Ann Math Artif Intell 60(1):25-C64

    Article  MathSciNet  MATH  Google Scholar 

  • García S, Fernández A, Luengo J, Herrera F (2009) A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput 13(10):959–977

    Article  Google Scholar 

  • Goldberg DE (1990) Probability matching, the magnitude of reinforcement, and classifier system bidding. Mach Learn 5:407–425

    Article  Google Scholar 

  • Goldberg DE, Richardson J (1987) Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of the international conference on genetic algorithms, pp 41–49

  • Goldberg DE, Segrest P (1987) Finite Markov chain analysis of genetic algorithms. In: International conference on genetic algorithms on genetic algorithms & their application

  • Harik GR (1995) Finding multimodal solutions using restricted tournament selection. In: Proceedings of the 6th international conference on genetic algorithms, pp 24–31

  • Jong KAD (1975) An analysis of the behavior of a class of genetic adaptative systems. Ph.D. dissertation, Univ. Michigan, Ann Arbor, MI

  • Julstrom BA (1995) What have you done for me lately? Adapting operator probabilities in a steady-state genetic algorithm. In: Proceedings of the third international conference on genetic algorithm, pp 81–87

  • Li M, Kou J (2008) Crowding with nearest neighbors replacement for multiple species niching and building blocks preservation in binary multimodal functions optimization. J Heuristics 14(3):243–270

    Article  MATH  Google Scholar 

  • Li M, Lin D, Kou J (2012) A hybrid niching PSO enhanced with recombination-replacement crowding strategy for multimodal function optimization. Appl Soft Comput 12(3):975–987

    Article  Google Scholar 

  • Li K, Fialho Á, Kwong S, Zhang Q (2014) Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 18(1):114–130

    Article  Google Scholar 

  • Li Y, Guo H, Liu X, Li Y, Pan W, Gong B, Pang S (2017) New mutation strategies of differential evolution based on clearing niche mechanism. Soft Comput 21:5939

    Article  Google Scholar 

  • Liang J, Qu B, 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 and Nanyang Technological University, Technical Report

  • Lynn N, Suganthan PN (2017) Ensemble particle swarm optimizer. Appl Soft Comput 55:533–548

    Article  Google Scholar 

  • Lynn N, Ali MZ, Suganthan PN (2018) Population topologies for particle swarm optimization and differential evolution. Swarm Evol Comput 39:24–35

    Article  Google Scholar 

  • Mahfoud SW (1992) Crowding and preselection revisited. In: Parallel problem solving from nature, pp 27–36

  • Mahfoud S (1995) Niching methods for genetic algorithms. University of Illinois at Urbana-Champaign

    Google Scholar 

  • Martin WN, Lienig J, Cohoon JP (1999) Island (migration) models: evolutionary algorithms based on punctuated equilibria. In: Handbook of evolutionary computation

  • Maturana J, Saubion F (2007a) On the design of adaptive control strategies for evolutionary algorithms. In: International conference on evolution artificielle, vol 4926

  • Maturana J, Saubion F (2007b) Towards a generic control strategy for EAs: an adaptive fuzzy-learning approach. In: Proceedings of IEEE international conference on evolutionary computation, pp 4546–4553

  • Maturana J, Lardeux F, Saubion F (2010) Autonomous operator management for evolutionary algorithms. J Heuristics 16(6):881–909

    Article  MATH  Google Scholar 

  • Mengshoel OJ (1999) Probabilistic crowding: deterministic crowding with probabilistic replacement

  • Mengshoel OJ, Goldberg DE (2008) The crowding approach to niching in genetic algorithms. Evol Comput 16(3):315–354

    Article  Google Scholar 

  • Mohamed AW, Suganthan PS (2018) Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation. Soft Comput 22(10):32150–3235

    Article  Google Scholar 

  • Moore JH, Hahn L (2002) Cellular automata and genetic algorithms for parallel problem solving in human genetics. In: International conference on parallel problem solving from nature, pp 821–830

  • Mukherjee R, Patra GR, Kundu R, Das S (2014) Cluster-based differential evolution with Crowding Archive for niching in dynamic environments. Inf Sci 267:58–82

    Article  MathSciNet  Google Scholar 

  • Osuna EC, Sudholt D (2017) Analysis of the clearing diversity-preserving mechanism. In: Conference: the 14th ACM/SIGEVO conference

  • Oyelade ON, Ezugwu AE (2021) Ebola Optimization Search Algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. In: International conference on electrical, computer and energy technologies (ICECET)

  • Petrowski A (1996) A clearing procedure as a niching method for genetic algorithms. In: IEEE international conference on evolutionary computation, pp 798–803

  • Price KV, Awad NH, Ali MZ, Suganthan PN (2018) Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. Technical Report, Nanyang Technological University, Singapore

  • Qu B, Suganthan PN (2010) Novel multimodal problems and differential evolution with ensemble of restricted tournament selection. In: IEEE transactions on evolutionary computation

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

    Article  Google Scholar 

  • Qu B, Liang J, Suganthan PN, Chen T (2012b) Ensemble of clearing differential evolution for multi-modal optimization. In: International conference on advances in swarm intelligence, Springer

  • Sareni B, Krähenbüh L (1998) Fitness sharing and niching methods revisited. IEEE Trans Evol Comput 2(3):97–106

    Article  Google Scholar 

  • Sheng W, Chen S, Sheng M, Xiao G, Mao J, Zhen Y (2016) Adaptive multisubpopulation competition and multiniche crowding-based memetic algorithm for automatic data clustering. IEEE Trans Evol Comput 20(6):838–858

    Google Scholar 

  • Storn R, Price KV (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  • Suganthan PN, Hansen N, Liang J, Deb K, Chen Y, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC\(2005\) special session on real-parameter optimization. Nanyang Technological University, Singapore, KanGAL Report No. 2005005, IIT Kanpur, India

  • Thierens D (2005) An adaptive pursuit strategy for allocating operator probabilities. In: Proceedings of annual conference on genetic and evolutionary computation, pp 1539–1546

  • Thomsen R (2004) Multimodal optimization using crowding-based differential evolution. In: Proceedings of the \(2004\) congress on evolutionary computation

  • Vitela JE, Castanos O (2008) A real-coded niching memetic algorithm for continuous multimodal function optimization. In: IEEE World congress on computational intelligence

  • Wang J, Zhou Y, Cai Y, Yin J (2012) Learnable tabu search guided by estimation of distribution for maximum diversity problems. Soft Comput 16:711–728

    Article  Google Scholar 

  • Wong YI, Lee KH, Leung KS, Ho CW (2003) A novel approach in parameter adaptation and diversity maintenance for GAs. Soft Comput 7(8):506–515

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Yu X, Gen MS (2010) Introduction to evolutionary algorithms, vol 271. Springer

    Book  MATH  Google Scholar 

  • Yu EL, Suganthan PN (2010) Ensemble of niching algorithms. Inf Sci 180(15):2815–2833

  • Zaharie D (2005) Density based clustering with crowding differential evolution. In: International symposium on symbolic & numeric algorithms for scientific computing

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

    Article  Google Scholar 

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61873082 and Grant No. 62003121, the Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ20F030014.

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by LY, XM, and WS. The first draft of the manuscript was written by LY, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Weguo Sheng.

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Le Yan and Xiaomei Mo contributed equally.

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Yan, L., Mo, X., Li, Q. et al. Adaptive niching selection-based differential evolution for global optimization. Soft Comput 26, 13509–13525 (2022). https://doi.org/10.1007/s00500-022-07510-0

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