An improved adaptive differential evolution algorithm for continuous optimization. (February 2016)

• Record Type: Journal Article
• Authors: Yi, Wenchao; Zhou, Yinzhi; Gao, Liang; Li, Xinyu; Mou, Jianhui
• Is Part Of: Expert systems with applications. Volume 44(2016)
• Page Start: 1
• Page End: 12
• Publication Date: 2016-02
• Subjects: Differential evolution -- pbest retention mechanism -- Adaptive parameter control -- pbest roulette wheel selection -- Real-world application problems; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.09.031
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract : A novel differential evolution algorithm based on adaptive differential evolution algorithm is proposed by implementing p best roulette wheel selection and retention mechanism. Motivated by the observation that individuals with better function values can generate better offspring, we propose a fitness function value based p best selection mechanism. The generated offspring with better fitness function value indicates that the p best vector of current individual is suitable for exploitation, so the p best vector should be retained into the next generation. This modification is used to avoid the individual gather around the p best vector, thus diversify the population. The performance of the proposed algorithm is extensively evaluated both on the 25 famous benchmark functions and four real-world application problems. Experimental results and statistical analyses show that the proposed algorithm is highly competitive when compared with other state-of-the-art differential evolution algorithms. Abstract : p best roulette wheel selection and p best retention mechanism are proposed. Benchmark and real-world application problems have been studied. The experimental results show that the algorithm is competitive. Graphical abstract: