A random distribution harmony search algorithm

Gu, Jiadong; Wu, Defeng

doi:10.1109/icaci.2018.8377498

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…It has a good performance on some problems. In recent years, harmony search algorithm has made rapid progress, Gu and Wu [10] introduced a novel harmony search algorithm called distribution harmony search algorithm, in which the random distribution operation can increase convergence speed effectively as well as optimum ability. Adaptive harmony search with best-based search strategy and a self-adaptive global best harmony search algorithm [14], [15] are both proposed as a better strategy to improve the performance of HS.…”

Section: B Harmony Search Algorithm (Hs)mentioning

confidence: 99%

“…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 hybrid harmony search particle swarm optimization with global dimension selection presented by Ouyang et al [54], the experimental results reveal that HHSPSO-GDS performs better in terms of the quality of solution, convergence rate, robustness and scalability. In recent years, many improvements have been made to the harmony search algorithm [8]- [10], [14], [15]. Harmonic search algorithm is widely used in engineering [55]- [57].…”

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: B Harmony Search Algorithm (Hs)mentioning

confidence: 99%

Section: Hybrid Harmony Search Differential Evolution Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Harmony Search Differential Evolution Algorithm

et al. 2021

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“…In the investigation [37] a random distribution is proposed in the creation of the HS Harmonic Memory (RDHS). The quality of the selection of the solution vectors of the harmonic memory is increased, the convergence is monitored and a new parameter is added which will maintain the values of the random distribution with which a new set of solutions is generated.…”

Section: State Of the Art In Hs Versions Focused On Parameters Improvements For The Last 10 Yearsmentioning

confidence: 99%

A Comparative Study of Improved Harmony Search Algorithm in Four Bar Mechanisms

et al. 2020

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There are problems that are difficult to solve through mathematical programming or by classical methods. These problems are called hard problems due to their high complexity or high dimension. On the other hand, mataheuristics intends to seek a better solution to a problem. The Improvement Harmony Search algorithm is proposed under modification of the bandwidth parameter increasing the quality of the exploitation of the solutions. That is why within the state of the art, are mentioned several versions of harmonic search. The state of the art is supports the fact that the algorithm belongs to the category of those who make modifications to its parameters. This research demonstrates the ability of ImHS to solve a problem of high complexity focused on solving four-bar mechanism designs, whose solutions imply high dimension and which are also classified as hard problems. The two problems that are solved in this investigation, are problems very attacked within the state of the art by various metaheuristics. A comparison is then made against previous solutions with traditional metaheuristics and other versions of harmony search algorithm. Finally, the effectiveness of performance is demonstrated, where proposed algorithm it exceeded five metaheuristic algorithms and five harmony search versions. An optimum is provided in an easy and useful way, the parametric statistics are improved and the number of feasible solutions is exceeded in NPhard problems as in the case of problems with four-bar mechanisms.INDEX TERMS algorithms performance, four-bar mechanism, improved harmony search, optimization problems, mechatronic.

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Performance Enhancement of Optimally Tuned PI Controller for Harmonic Minimization

Vishwakarma

Singh

2020

Control Applications in Modern Power System

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A random distribution harmony search algorithm

Cited by 3 publications

References 6 publications

Hybrid Harmony Search Differential Evolution Algorithm

Hybrid Harmony Search Differential Evolution Algorithm

A Comparative Study of Improved Harmony Search Algorithm in Four Bar Mechanisms

Performance Enhancement of Optimally Tuned PI Controller for Harmonic Minimization

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