Abstract
This paper presents a kernel fuzzy clustering with a novel differential harmony search algorithm to coordinate with the diversion scheduling scheme classification. First, we employed a self-adaptive solution generation strategy and differential evolution-based population update strategy to improve the classical harmony search. Second, we applied the differential harmony search algorithm to the kernel fuzzy clustering to help the clustering method obtain better solutions. Finally, the combination of the kernel fuzzy clustering and the differential harmony search is applied for water diversion scheduling in East Lake. A comparison of the proposed method with other methods has been carried out. The results show that the kernel clustering with the differential harmony search algorithm has good performance to cooperate with the water diversion scheduling problems.
Highlights
Metaheuristics are designed to find, generate, or select a heuristic that provides a good solution to an optimization problem [1]
This paper is organized as follows: Section 2 presents an overview of the harmony search algorithm and kernel fuzzy clustering; Section 3 describes the modification and the combination of kernel fuzzy clustering and the differential harmony search algorithm; Section 4 discusses the computational results; and Section 5 provides the summary of this paper
The main contributions of this work are (i) a novel modification of the harmony search algorithm is proposed to deal with the optimization problem; (ii) a kernel fuzzy clustering algorithm with the harmony search algorithm is proposed; and (iii) the methodology is adopted for a water diversion scheduling assessment in East Lake
Summary
Metaheuristics are designed to find, generate, or select a heuristic that provides a good solution to an optimization problem [1]. Algorithms 2017, 10, 14 needs to specify the new parameters, which are not easy to set, and it still performs local searches for some numerical applications Other modifications, such as the global-best harmony search algorithm (GHS) [13] and chaotic harmony search algorithms (CHS) [14], have shown better performance than the classical HS, but they still have their own shortcomings. Research has shown that WFKCA has good convergence properties and the prototypes obtained can be well represented in the original space These clustering methods have the same problem: the iterative solution is not the optimal solution. We proposed a new differential harmony search algorithm (DHS), and applied it to kernel fuzzy clustering. The purpose of the kernel clustering with differential harmony search method is to classify the results in order to find out the schemes which perform better than others. This paper is organized as follows: Section 2 presents an overview of the harmony search algorithm and kernel fuzzy clustering; Section 3 describes the modification and the combination of kernel fuzzy clustering and the differential harmony search algorithm; Section 4 discusses the computational results; and Section 5 provides the summary of this paper
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