Abstract
The multimodal optimization problem (MMOP) requires the algorithm to find multiple global optima of the problem simultaneously. In order to solve MMOP efficiently, a novel differential evolution (DE) algorithm based on the local binary pattern (LBP) is proposed in this paper. The LBP makes use of the neighbors' information for extracting relevant pattern information, so as to identify the multiple regions of interests, which is similar to finding multiple peaks in MMOP. Inspired by the principle of LBP, this paper proposes an LBP-based adaptive DE (LBPADE) algorithm. It enables the LBP operator to form multiple niches, and further to locate multiple peak regions in MMOP. Moreover, based on the LBP niching information, we develop a niching and global interaction (NGI) mutation strategy and an adaptive parameter strategy (APS) to fully search the niching areas and maintain multiple peak regions. The proposed NGI mutation strategy incorporates information from both the niching and the global areas for effective exploration, while APS adjusts the parameters of each individual based on its own LBP information and guides the individual to the promising direction. The proposed LBPADE algorithm is evaluated on the extensive MMOPs test functions. The experimental results show that LBPADE outperforms or at least remains competitive with some state-of-the-art algorithms.
Highlights
D IFFERENTIAL evolution (DE) is a kind of evolutionary algorithm (EAs) proposed by Storn and Price in 1995 [1]
A novel local binary pattern (LBP)-based niching strategy is proposed, which forms a niche for each individual according to its local information by simulating the LBP operator in the image processing
The niching and global interaction (NGI) mutation strategy and the adaptive parameter strategy (APS) technique are incorporated, which results in the LBP-based adaptive DE (LBPADE) algorithm, which can enhance the population diversity and can accelerate convergence speed
Summary
D IFFERENTIAL evolution (DE) is a kind of evolutionary algorithm (EAs) proposed by Storn and Price in 1995 [1]. An efficient evolutionary operator and an adaptive parameter control strategy that cooperate with the niche strategy are in great need to balance the exploration and exploitation abilities to refine the found peaks and to maintain them during the entire evolution process. To these aims, this paper borrows the idea of the local binary pattern (LBP) operator from image processing to the optimization domain for solving MMOPs efficiently.
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