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An improved differential evolution algorithm with triangular mutation for global numerical optimization |
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| Affiliation: | 1. Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, Hunan, PR China;2. School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, South Korea;3. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore, Singaporen;4. College of Information System and Management, National University of Defense Technology, Changsha 410073, Hunan, PR China;1. Key Lab of Industrial Computer Control Engineering of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, PR China;2. Department of Computer Science and Technology, Tangshan College, Tangshan 063000, PR China;1. Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.;2. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China. |
| Abstract: | This paper presents an Improved Differential Evolution (IDE) algorithm for solving global numerical optimization problems over continuous space. The proposed algorithm introduces a new triangular mutation rule based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vector between the best and the worst individuals among the three randomly selected vectors. The mutation rule is combined with the basic mutation strategy through a non-linear decreasing probability rule. Furthermore, a restart mechanism is also proposed to avoid premature convergence. IDE is tested on a well-known set of unconstrained problems and shows its superiority to state-of-the-art differential evolution variants. |
| Keywords: | Evolutionary computation Global optimization Differential evolution Triangular mutation Dynamic non-linear crossover |
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