Top

Soft Computing

Published in:

01-12-2017 | Methodologies and Application

Self-adaptive parameters in differential evolution based on fitness performance with a perturbation strategy

Published in: Soft Computing | Issue 9/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Differential evolution (DE) algorithms have been used widely to solve optimization problems and practical cases and have demonstrated high efficiency, performing favorably using only a few parameters. Compared with other traditional algorithms, DE algorithms perform well when used to solve continuous problems. To obtain an approximate solution using DE, it is critical that appropriate parameter values are selected. However, selecting and dynamically tuning the parameter values during evolution are not easy tasks because the values depend significantly on the problem to be solved. To address these issues, this study presents an enhanced DE algorithm with self-adaptive adjustable parameters and a perturbation strategy based on individual fitness performance. Compared with two existing DE algorithms, the proposed algorithm can solve six benchmark functions and has both high efficiency and stability.

previous article Bee swarm optimization for solving the MAXSAT problem using prior knowledge

next article A vegetable category recognition system: a comparison study for caffe and Chainer DNN frameworks

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Aleti A, Moser I (2016) A systematic literature review of adaptive parameter control methods for evolutionary algorithms. ACM Comput Surv (CSUR) 49(3):56CrossRef

Arasomwan MA, Adewumi AO (2014) Improved particle swarm optimization with a collective local unimodal search for continuous optimization problems. Sci World J 2014:798129. https://doi.org/10.1155/2014/798129

Brest J, Greiner S, Bošković B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evolut Comput 10:646–657CrossRef

Brest J, Bošković B, Greiner S, Žumer V, Maučec MS (2007) Performance comparison of self-adaptive and adaptive differential evolution algorithms. Soft Comput 11:617–629CrossRefMATH

Chen C-A, Chiang T-C (2015) Adaptive differential evolution: a visual comparison. In: IEEE congress on evolutionary computation (CEC), IEEE, pp 401–408

Chiang T-C, Chen C-N, Lin Y-C (2013) Parameter control mechanisms in differential evolution: a tutorial review and taxonomy. In: 2013 IEEE symposium on differential evolution (SDE), IEEE, pp 1–8

Chuan-Kang T, Chih-Hui H (2009) Varying number of difference vectors in differential evolution. In: IEEE congress on evolutionary computation (CEC), pp 1351–1358. https://doi.org/10.1109/CEC.2009.4983101

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, Konar A, Chakraborty UK (2005) Improved differential evolution algorithms for handling noisy optimization problems. In: The 2005 IEEE congress on evolutionary computation, 2005. IEEE, pp 1691–1698

De Falco I, Della Cioppa A, Maisto D, Scafuri U, Tarantino E (2014) An adaptive invasion-based model for distributed differential evolution. Inf Sci 278:653–672MathSciNetCrossRef

Derrac J, García S, Hui S, Suganthan PN, Herrera F (2014) Analyzing convergence performance of evolutionary algorithms: a statistical approach. Inf Sci 289:41–58CrossRef

Dexuan Z, Liqun G (2012) An efficient improved differential evolution algorithm. In: Chinese control conference (CCC), IEEE, pp 2385–2390

Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evolut Comput 3:124–141CrossRef

Fan Q, Yan X (2015) Differential evolution algorithm with self-adaptive strategy and control parameters for P-xylene oxidation process optimization. Soft Comput 19:1363–1391CrossRef

Hsieh S-T, Su T, Wu H-L (2013) An improved differential evolution with efficient parameters adjustment. In: 2013 first international symposium on computing and networking (CANDAR), IEEE, pp 627–629

Hu Z, Xiong S, Su Q, Zhang X (2013) Sufficient conditions for global convergence of differential evolution algorithm. J Appl Math 2013:139196

Iacca G, Caraffini F, Neri F (2012) Compact differential evolution light: high performance despite limited memory requirement and modest computational overhead. J Comput Sci Technol 27:1056–1076MathSciNetCrossRefMATH

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) 42:482–500CrossRef

Jia D, Zheng G, Khan MK (2011) An effective memetic differential evolution algorithm based on chaotic local search. Inf Sci 181:3175–3187CrossRef

Jiang LL, Maskell DL, Patra JC (2013) Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm. Appl Energy 112:185–193CrossRef

Lee W-PC, Chang-Yu Cai, Wan-Ting (2011) A differential evolution algorithm with perturb strategy. In: International journal of advanced information technologies (IJAIT) p 5

Lee W-P, Chiang C-Y (2011) A self-adaptive differential evolution algorithm with dimension perturb strategy. J Comput 6:524–531

Li X, Yin M (2016) Modified differential evolution with self-adaptive parameters method. J Comb Optim 31:546–576MathSciNetCrossRefMATH

Lin Y-C, Cheng C-Y (2015) Self-adaptive parameters adjusting in differential evolution based on fitness information. Paper presented at the 15’ CIIE Chinese institute of industrial engineers,

Liu J, Lampinen J (2005) A fuzzy adaptive differential evolution algorithm. Soft Comput 9:448–462CrossRefMATH

Mezura-Montes E, Velázquez-Reyes J, Coello Coello CA (2006) A comparative study of differential evolution variants for global optimization. In: Proceedings of the 8th annual conference on genetic and evolutionary computation, ACM, pp 485–492

Mi M, Huifeng X, Ming Z, Yu G (2010) An improved differential evolution algorithm for TSP problem. In: International conference on intelligent computation technology and automation (ICICTA), IEEE, pp 544–547

Omran MG, Salman A, Engelbrecht AP (2005) Self-adaptive differential evolution. In: Computational intelligence and security. Springer, Berlin, pp 192–199

Ponsich A, Coello CAC (2013) A hybrid differential evolution–tabu search algorithm for the solution of job-shop scheduling problems. Appl Soft Comput 13(1):462–474CrossRef

Price K, Storn R, Lampinen J (2005) Differential evolution–a practical approach to global optimization. Springer, BerlinMATH

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

Rajesh K, Bhuvanesh A, Kannan S, Thangaraj C (2016) Least cost generation expansion planning with solar power plant using differential evolution algorithm. Renew Energy 85:677–686CrossRef

Salman A, Engelbrecht AP, Omran MG (2007) Empirical analysis of self-adaptive differential evolution. Eur J Oper Res 183:785–804CrossRefMATH

Sauer JG, Coelho LDS (2008) Discrete differential evolution with local search to solve the traveling salesman problem: fundamentals and case studies. In: IEEE international conference on cybernetic intelligent systems. IEEE, pp 1–6

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

Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y-P, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report 2005005:2005

Tang L, Zhao Y, Liu J (2014) An improved differential evolution algorithm for practical dynamic scheduling in steelmaking-continuous casting production. IEEE Trans Evolut Comput 18:209–225CrossRef

Trivedi A, Srinivasan D, Biswas S, Reindl T (2015) Hybridizing genetic algorithm with differential evolution for solving the unit commitment scheduling problem. Swarm Evolut Comput 23:50–64CrossRef

Wang HB, Ren XN, Li GQ, Tu XY (2016) APDDE: self-adaptive parameter dynamics differential evolution algorithm. Soft Comput 1–21

Xue F, Sanderson AC, Graves RJ (2009) Multiobjective evolutionary decision support for design-supplier-manufacturing planning Systems. IEEE Trans Man Cybern, Part A: Syst Hum 39:309–320CrossRef

Yildiz AR (2013) Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Appl Soft Comput 13(3):1433–1439CrossRef

Zaharie D (2007) A comparative analysis of crossover variants in differential evolution. In: Proceedings of IMCSIT pp 171–181

Zhang J, Sanderson AC (2007) JADE: self-adaptive differential evolution with fast and reliable convergence performance. In: IEEE congress on evolutionary computation, IEEE, pp 2251–2258

Title: Self-adaptive parameters in differential evolution based on fitness performance with a perturbation strategy
Publication date: 01-12-2017
Published in: Soft Computing / Issue 9/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-2958-z

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 9/2019

Novel soft fuzzy rough rings (ideals) of rings and their application in decision making

Implementation of an intelligent hybrid simulation systems for WMNs based on particle swarm optimization and simulated annealing: performance evaluation for different replacement methods

MIP-based heuristic approaches for the capacitated edge activation problem: the effect of non-compactness

A decision support system to improve performances of airport check-in services

Patch-based fuzzy clustering for image segmentation

Special issue on: Optimization methods for decision making: advances and applications

Premium Partner