Self-feedback differential evolution adapting to fitness landscape characteristics
- 96 Downloads
Differential evolution (DE) is one of the most powerful and versatile evolutionary algorithms for efficiently solving complex real-world optimization problems in recent years. Since its introduction in 1995, the research focus in DE has mostly been on the variant side with so many new algorithms proposed based on the original DE algorithm. However, each new algorithm is only suitable for certain fitness landscapes, and, therefore, some types of optimization problems cannot be solved efficiently. To tackle this issue, this paper presents a new self-feedback DE algorithm, named the SFDE; its optimal variation strategy is selected by extracting the local fitness landscape characteristics in each generation population and combing the probability distributions of unimodality and multimodality in each local fitness landscape. The proposed algorithm is tested on a suite of 17 benchmark functions, and the experimental results demonstrated its advantages in a high search dimension in that it can ensure that the population moves to a better fitness landscape, then speeds up convergence to the global optimum, and avoids falling into local optima.
KeywordsDifferential evolution Self-feedback Fitness landscape Probability distribution Optimization problem
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61573157 and 61561024, the Science and Technology Planning Project of Guangdong Province with the Grant No. 2017A010101037, the Science and Technology Research Project of Jiangxi Province under Grant No. GJJ160631, and the Science Foundation of Jiangxi University of Science and Technology under the Grant No. NSFJ2015-K13.
Compliance with ethical standards
Conflicts of interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
- Chaofeng G, Meilian L (2013) Improved differential evolution algorithm and its application in dynamic programming. J Henan Univ (Nat Sci) 43(1):79–84Google Scholar
- Davidor Y (1991) Epistasis variance: a viewpoint on GA-hardness. Found Genet Algorithms 1:23–35Google Scholar
- Jones T, Forrest S (1995) Fitness distance correlation as a measure of problem difficulty for genetic algorithms. Santa Fe Institute. Working paper 95-02-022Google Scholar
- Liu J, Lampinen J (2002) A fuzzy adaptive differential evolution algorithm. In TENCON’02. Proceedings of 2002 IEEE region 10 conference on computers, communications, control and power engineering, vol 1. IEEE, pp 606–611Google Scholar
- Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10(2):629–640Google Scholar
- Liu H, Zhang P, Wang K, Yang B, Chen Z (2016) Performance and scalability analysis for parallel reservoir simulations on three supercomputer architectures. In: Proceedings of the XSEDE16 conference on diversity, big data, and science at scale. ACM, p 9Google Scholar
- Merz P, Freisleben B (1999) Fitness landscapes and memetic algorithm design. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimization. McGraw-Hill, London, pp 245–260Google Scholar
- Nghiem L. Mirzabozorg,A. Yang,C. Chen Z (2013) Differential evolution for assisted history matching process: Sagd case study. In: SPE heavy oil conference-Canada. Society of Petroleum EngineersGoogle Scholar
- Radcliffe NJ, Surry PD (1994) Fitness variance of formae and performance prediction. In FOGA 3:51–72Google Scholar
- Shen L, He, J (2010) A mixed strategy for evolutionary programming based on local fitness landscape. In: 2010 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8Google Scholar
- Weilin W, Yonghua Z (2014) The swarm intelligence algorithm based on combination of difference evolution and cat swarm optimization algorithms. Comput Technol Autom 33(4):78–83Google Scholar
- Zhao S, Hao Z, Huang H, Tan Y (2013) Multi-objective differential evolution algorithm based on adaptive mutation and partition selection. JCP 8(10):2695–2700Google Scholar