APDDE: self-adaptive parameter dynamics differential evolution algorithm
In real-time high-dimensional optimization problem, how to quickly find the optimal solution and give a timely response or decisive adjustment is very important. This paper suggests a self-adaptive differential evolution algorithm (abbreviation for APDDE), which introduces the corresponding detecting values (the values near the current parameter) for individual iteration during the differential evolution. Then, integrating the detecting values into two mutation strategies to produce offspring population and the corresponding parameter values of champion are retained. In addition, the whole populations are divided into a predefined number of groups. The individuals of each group are attracted by the best vector of their own group and implemented a new mutation strategy DE/Current-to-lbest/1 to keep balance of exploitation and exploration capabilities during the differential evolution. The proposed variant, APDDE, is examined on several widely used benchmark functions in the CEC 2015 Competition on Learning-based Real-Parameter Single Objective Optimization (13 global numerical optimization problems) and 7 well-known basic benchmark functions, and the experimental results show that the proposed APDDE algorithm improves the existing performance of other algorithms when dealing with the high-dimensional and multimodal problems.
KeywordsDifferential evolution Self-adapting strategy Real-time optimization
Financial supports from the National Natural Science Foundation of China (No. 61572074) and the 2012 Ladder Plan Project of Beijing Key Laboratory of Knowledge Engineering for Materials Science (No. Z121101002812005) are highly appreciated.
Compliance with ethical standards
Conflicts of interest
All authors of the paper declare that there is no conflict of interest each other.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
- Ali M, Pant M, Abraham A (2013) Improving differential evolution algorithm by synergizing different improvement mechanisms. ACM Trans Auton Adapt Syst 7(2):20–52Google Scholar
- Feoktistov V, Janaqi S (2004) Generalization of the strategies in differential evolution. In: Proceedings of the 18th IPDPS, p 165aGoogle Scholar
- Hu G, Qiao P (2016) High dimensional differential evolution algorithm based on cloud cluster and its application in network security situation prediction. J Jilin Univ Eng Technol Ed 46(2):568–577Google Scholar
- Li X, Yin M (2014) Modified differential evolution with self-adaptive parameters method. J Comb Optim 29(111):22Google Scholar
- Li X, Luo J, Chen M-R, Wang N (2012) An improved shuffled frog-leaping algorithm with extremal optimisation for continuous optimization. Inf Sci 192:143–151Google Scholar
- Liang JJ, Qu BY, Suganthan PN, Chen Q (2015) Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. Zhengzhou University, Zhengzhou China And Technical Report, Nanyang Technological University, SingaporeGoogle Scholar
- Liu R, Fan J, Jiao L (2015b) Integration of improved predictive model and adaptive differential evolution based dynamic multi-objective evolutionary optimization algorithm. Appl Intell 0924-669xGoogle Scholar
- Liu Z, Xu Y, Wang FM (2015c) Application of Modified differential evolution algorithm to non-linear MPC. J Beijing Univ Technol 41(5):680–685Google Scholar
- Price KV (1997) Differential evolution vs. the functions of the 2nd ICEO. In: Proceedings of the IEEE International Conference on Evolutionary Computing, pp 153–157Google Scholar
- Ronkkonen J, Kukkonen S, Price KV (2005) Real-parameter optimization with differential evolution. In: IEEE congress on evolutionary computation, pp 506–513Google Scholar
- Yang M, Cai Z, Li C (2013) An improved adaptive differential evolution algorithm with population adaptation. In: GECCO’13 Proceedings of the 15th annual conference on Genetic and evolutionary computation, pp 145–152Google Scholar
- Yi W, Gao L, Li X, Zhou Y (2015) A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems. Appl Intell 42(2):642–660Google Scholar