An Efficient Hybrid DE-WOA Algorithm for Numerical Function Optimization

Wang, Zhongyu; Ya-ru, LI; Tang, Yingqi

doi:10.1109/isie.2019.8781121

Cited by 5 publications

(3 citation statements)

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“…Aiming at enhancing the ability of the local search and increasing the individual diversity, the application of hybrid idea in algorithms has been greatly developed in recent years. Many hybrid algorithms have been proposed based on the hybrid idea [2], [7], [10], [16], [21], [22]. Along this line, the pseudo code for the hybrid harmonic differential evolution algorithm is shown below:…”

Section: Hybrid Harmony Search Differential Evolution Algorithmmentioning

confidence: 99%

“…A few algorithms are used in this paper for comparison. They include the hybrid artificial bee colony algorithm with differential evolution (HABCDE) [16], an Efficient Hybrid DE-WOA Algorithm for numerical function optimization by Wang et al [21], and some hybrid DE algorithms (BBO-DE [11], HybGADE [2], PSODEHS [18], and CobiDE [23]). 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).…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Harmony Search Differential Evolution Algorithm

et al. 2021

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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.

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Section: Hybrid Harmony Search Differential Evolution Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Harmony Search Differential Evolution Algorithm

et al. 2021

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“…Even though traditional meta-heuristics have proved to be efficient, hybrid algorithms tend to present better results than canonical meta-heuristics, as we can see in [9], [10], and [11]. In this context, this work presents a hybrid algorithm based on Harmony Search and Differential Evolution for solving Numerical Problems, which are similar to those found in engineering problems.…”

Section: Introductionmentioning

confidence: 97%

On a Cooperative Hybrid Algorithm Based on Harmony Search and Differential Evolution for Numerical Optimization

Sousa¹,

Cortes²

2020

Anais Do XI Computer on the Beach - COTB '20

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Hybrid algorithms aim to mix features from two or more evolutionary/swarm algoprove both the exploration and exploitationabilities of the algorithm. Generally, hybrid algorithms prrithmsto imesent the same quality of solution than the canonical ones,in the worst case scenario. However, it is common that hybridalgorithms present better outcomes than the canonical ones. Inthis context, this paper proposes a cooperative hybrid algorithmbased on Harmony Search and Differential Evolution named HS-DE.The algorithm has been tested in five benchmark functions wellknown in the literature. Results have shown that HS-DE presentsbetter solutions than Genetic Algorithms, Particle Swarm Optimization,Differential Evolution, and Harmony Search in all benchmarkfunctions.

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