Abstract
Due to that the performance of differential evolution (DE) significantly depends on offspring generation strategies, various DE variants have been reported with the improved mutation operators. However, on the one hand, the mutation operators in most DE variants are guided by the elites in terms of the fitness value, without considering their distribution information in the fitness landscape. It may lead to the population be evolving towards the unpromising areas more frequently if these elites are clustered in a locally optimal region. On the other hand, in most DE variants, the evolutionary information of the potential trial vectors is not fully utilized to guide the search, which will hamper the local exploitation in the promising regions that they are located in. To overcome these weaknesses, this article proposes an enhanced DE framework (DELDG) with a leaders-detection-and-guidance mechanism that consists of an adaptive leaders detection (ALD) and a neighborhood-based tournament selection (NTS). With these two novel operators, DELDG can not only guide the mutation process of each individual with multiple promising leaders detected by ALD, but also accelerate the convergence speed with the competition among the potential trial vectors by NTS. Therefore, DELDG is characterized by the explicit detection of the promising leaders according to their fitness values and distribution information and the effective use of the potential trial vectors in the neighborhood of each leader. Compared with 36 excellent DE variants and evolutionary algorithms (EAs), the experimental results on 28 IEEE CEC2013 real-parameter functions and 17 IEEE CEC2011 real-world problems have demonstrated the competitive performance of DELDG.
Highlights
Since its invention in 1997, differential evolution (DE) has received rapid popularization and developed as a powerful and successful optimization tool for the optimization problems [1], [2]
From Eq (14), it is clear that the generation of the mutant vector in JADE-LDG is guided by the detected promising leaders in leadership group (LG), instead of the top best solutions in the original
From to the above results, we can draw some conclusions: 1) DELDG with Crowding clustering (CC), Speciation clustering (SC), or K-means clustering (KC) can greatly improve the performance of the considered DE algorithm, which verifies the effectiveness of different division methods in constructing LG under the proposed framework, and 2) DELDG with CC achieves the best results in terms of the average ranking among the three variants
Summary
Since its invention in 1997, differential evolution (DE) has received rapid popularization and developed as a powerful and successful optimization tool for the optimization problems [1], [2]. Two novel operators, i.e., adaptive leaders detection (ALD) and neighborhood-based tournament selection (NTS), are introduced into DE to enhance its performance for global optimization With these two operators, DELDG can provide an effective guidance for each individual with multiple promising leaders and further promote convergence of DE by strengthening the search within the neighboring region of each leader. JADE and its variants (e.g., SHADE, L-SHADE, and jSO) selects a set of the best individuals to guide the process of mutation only based on their fitness values in the objective space, regardless of their distribution information in the decision space. How to effectively utilize the information of the failed trial vectors is an important issue to the local exploitation for the problems being optimized, especially for the computationally expensive problems
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