Improving differential evolution by differential vector archive and hybrid repair method for global optimization
- 283 Downloads
Differential evolution (DE) has been extensively studied in the past decade, though researchers may ignore the effect of an archive storing previous search information. Moreover, boundary repair issue is seldom handled in the literature. This paper attempts to improve the performance of DE algorithm from these two aspects. First, a differential vector archive is constructed and adaptively updated during the optimization process of DE. The archive stores a set of differential vectors representing potential good search directions. Second, inspired by recently reported results about repair methods, a hybrid of four commonly used repair methods is proposed. The hybrid method is more applicable to unknown optimization problems than a single repair method. A test suite containing 28 benchmark functions is employed for experimental investigation. Experimental results show that the proposed algorithm usually affects the search to attain better performance in the later evolutionary stage. Our algorithm significantly outperforms a state- of-the-art algorithm. This result verifies the effectiveness of the proposed algorithm.
KeywordsDifferential evolution Global optimization Real- parameter optimization Differential vector archive Boundary repair
This research was supported in part by the Tianjin Thousand Youth Talents Plan Project of Tianjin Normal University (ZX110023) and the Applied Basic Research Program of Tianjin (15JCYBJC51500, 15JCYBJC52300).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Arabas J, Szczepankiewicz A, Wroniak T (2010) Experimental comparison of methods to handle boundary constraints in differential evolution. In: PPSN XI, pp 411–420Google Scholar
- Brest J, Maucec MS (2008) Population size reduction for the differential evolution algorithm. Appl Intell 29(3):228–247Google Scholar
- Das S, Konar A, Chakraborty UK (2005) Two improved differential evolution schemes for faster global search. In: Proceedings of ACM-SIGEVO GECCO. Washington, pp 991–998Google Scholar
- Liang JJ, Qu BY, Suganthan PN, Hemandez-Diaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session and competition on real-parameter optimization. Technical Report 201212, Computational Intelligence Laboratory, Zhenzhou University and Nanyang Technological University, Zhenzhou, China and SingaporeGoogle Scholar
- Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for differential evolution. In: IEEE congress on evolutionary computation. Cancun, pp 71–78Google Scholar
- Tanabe R, Fukunaga A (2014) Improving the search performance of SHADE using linear population size reduction. In: IEEE congress on evolutionary computation. Beijing, pp 1658–1665Google Scholar
- Wessing S (2013) Repair methods for box constraints revisited. In: Esparcia-Alcázar AI (ed) Applications of evolutionary computation. 16th European conference, EvoApplications 2013, Vienna, Austria, April 3–5, 2013. Proceedings. Springer, Berlin, Heidelberg, pp 469–478Google Scholar
- Zhang X, Zhang X, Ho SL, Fu WN (2014) Designing loudspeaker by ensemble of composite differential evolution ingredients. IEEE Trans Magn 50(11):1–4Google Scholar