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An improved adaptive differential evolution algorithm for continuous optimization
DOI:10.1016/j.eswa.2015.09.031.png)
摘要
En 中文
A novel differential evolution algorithm based on adaptive differential evolution algorithm is proposed by implementing pbest 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 pbest selection mechanism. The generated offspring with better fitness function value indicates that the pbest vector of current individual is suitable for exploitation, so the pbest vector should be retained into the next generation. This modification is used to avoid the individual gather around the pbest 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. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
Differential evolution
pbest retention mechanism
Adaptive parameter control
pbest roulette wheel selection
Real-world application problems
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期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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引用论文
A differential evolution algorithm with self-adapting strategy and control parameters一种具有自适应策略和控制参数的差分进化算法
Scalability of generalized adaptive differential evolution for large-scale continuous optimization
SOFT COMPUTING
IF2.5
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization求解全局数值优化问题的策略自适应差分进化算法

