论文标题
通过明确的剥削和勘探控制措施,在差异演化中的自适应策略
Adaptive strategy in differential evolution via explicit exploitation and exploration controls
论文作者
论文摘要
现有的多策略自适应差异进化(DE)通常涉及多种策略的试验,然后奖励具有更多资源的表现更好的策略。但是,剥削或探索策略的试验可能导致过度开发或过度探索。为了提高绩效,本文提出了一种新的策略适应方法,称为显式适应方案(EA计划),该方法将多种策略分开并按需采用它们。这是通过将演变过程分为具有相似性选择(SCSS)代和自适应世代的几个选择性候选方法来完成的。在SCSS世代中,利用平衡策略可以学习剥削和探索需求。为了满足这些需求,在自适应世代中,可以适应使用另外两种剥削性或探索性的策略。对基准功能的实验研究表明,与其变体和其他适应方法相比,EA方案的有效性。此外,性能与最先进的进化算法和基于群体智能算法的绩效比较表明,EADE非常有竞争力。
Existing multi-strategy adaptive differential evolution (DE) commonly involves trials of multiple strategies and then rewards better-performing ones with more resources. However, the trials of an exploitative or explorative strategy may result in over-exploitation or over-exploration. To improve the performance, this paper proposes a new strategy adaptation method, named explicit adaptation scheme (Ea scheme), which separates multiple strategies and employs them on-demand. It is done by dividing the evolution process into several Selective-candidate with Similarity Selection (SCSS) generations and adaptive generations. In the SCSS generations, the exploitation and exploration needs are learnt by utilizing a balanced strategy. To meet these needs, in adaptive generations, two other strategies, exploitative or explorative is adaptively used. Experimental studies on benchmark functions demonstrate the effectiveness of Ea scheme when compared with its variants and other adaptation methods. Furthermore, performance comparisons with state-of-the-art evolutionary algorithms and swarm intelligence-based algorithms show that EaDE is very competitive.