Abstract
Differential evolution (DE) algorithm has some excellent attributes including strong exploration capability. However, it cannot balance the exploitation with exploration ability in the search process. To enhance the performance of the DE algorithm, this paper proposes a new algorithm named hybrid harmony differential evolution algorithm (HHSDE). The key features of HHSDE algorithm are as follows. First, a new mutation operation is developed for improving the efficiency of mutation, in which the New Harmony generation mechanics of the harmony algorithm (HS) is employed. Second, the harmony memory size is updated with the iteration. Third, a self-adaptive parameter adjustment strategy is presented to control scaling factor. Fourth, a new evaluation method is proposed to effectively assess the algorithm convergence performance. Two classical DE algorithms, HS algorithm, improvement Differential evolution algorithm(ISDE) and Hybrid Artificial Bee Colony algorithm with Differential Evolution(HABCDE) have been tested against HHSDE based on 25 benchmark functions of CEC2005 and the results reveal that the proposed algorithm is better than the other algorithms under consideration.
Highlights
For solving some numerical optimization algorithms, swarm intelligence algorithm is an effective solution tool
Kong et al [58] proposed A simplified binary harmony search algorithm (SBHS) for large scale 0–1knapsack problems, the harmonies stored in harmony memory are employed to produce new solutions, and the harmony memory considering rate is dynamically adjusted in terms of the dimension size, The results show that the algorithm has excellent performance in solving large-scale problems
EXPERIMENTAL SETTING The proposed Hybrid Harmony search Differential evolution Algorithm (HHSDE) algorithm is implemented in MATLAB
Summary
For solving some numerical optimization algorithms, swarm intelligence algorithm is an effective solution tool. Lin et al [7] introduced a new hybrid algorithm named hybrid differential evolution and particle swarm optimization algorithm (RWDEPSO) This algorithm introduces the random inertia weight which obeys the standard state distribution and can generate larger or smaller weight values randomly in the early and late stages of the algorithm. In order to improve the global research ability, the evolutionary part of this algorithm is a mixture of the mutation step of the differential evolution algorithm and the New Harmony step of harmony algorithm This increases the search ability of algorithm. In order to enhance the search ability of differential evolutionary algorithm in the later stage of the algorithm and improve the stability of the algorithm, the two meta-heuristic algorithms are combined, and the parameter F is adjusted through a parameter self-adaptive strategy, which is proposed Hybrid Harmony search Differential evolution Algorithm (HHSDE).
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