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29 March 2024 |
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Article overview
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Adaptive strategy in differential evolution via explicit exploitation and exploration controls | Sheng Xin Zhang
; Wing Shing Chan
; Kit Sang Tang
; Shao Yong Zheng
; | Date: |
3 Feb 2020 | Abstract: | When introducing new strategies to the existing one, two key issues should be
addressed. One is to efficiently distribute computational resources so that the
appropriate strategy dominates. The other is to remedy or even eliminate the
drawback of inappropriate strategies. Adaptation is a popular and efficient
method for strategy adjustments and has been widely studied in the literature.
Existing methods commonly involve the trials of multiple strategies and then
reward better-performing one with more resources based on their previous
performance. As a result, it may not efficiently address those two key issues.
On the one hand, they are based on trial-and-error with inappropriate
strategies consuming resources. On the other hand, since multiple strategies
are involved in the trial, the inappropriate strategies could mislead the
search. In this paper, we propose an adaptive differential evolution (DE) with
explicit exploitation and exploration controls (Explicit adaptation DE, EaDE),
which is the first attempt using offline knowledge to separate multiple
strategies to exempt the optimization from trial-and-error. EaDE divides the
evolution process into several SCSS (Selective-candidate with similarity
selection) generations and adaptive generations. Exploitation and exploration
needs are learned in the SCSS generations by a relatively balanced strategy.
While in the adaptive generations, to meet these needs, two other alternative
strategies, an exploitative one or an explorative one is employed. Experimental
studies on 28 benchmark functions confirm the effectiveness of the proposed
method. | Source: | arXiv, 2002.0612 | Services: | Forum | Review | PDF | Favorites |
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