2020
DOI: 10.3390/info11020086
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Harmony Search Method with Global Sharing Factor Based on Natural Number Coding for Vehicle Routing Problem

Abstract: This paper proposes an improved Harmony Search algorithm, and gives the definition of the Global Sharing Factor of the Harmony Search (HS) algorithm. In the definition, the number of creations of the HS algorithm is applied to the sharing factor and calculated. In this algorithm, the natural harmony encoding method is used to encode the initial harmony, and the total path length of all vehicles is taken as the optimization objective function. A new harmony generation strategy is proposed as follows: each tone … Show more

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Cited by 11 publications
(5 citation statements)
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“…The computational complexity of HS-FRI can be divided into two core parts, O HS−F RI = O HS +O P romotion . From [31], the time complexity of harmony search algorithm can be derived as O HS (T max × D BS ), where T max is the total number of improvisations and D BS is the dimension of the best harmony vector…”
Section: Resultsmentioning
confidence: 99%
“…The computational complexity of HS-FRI can be divided into two core parts, O HS−F RI = O HS +O P romotion . From [31], the time complexity of harmony search algorithm can be derived as O HS (T max × D BS ), where T max is the total number of improvisations and D BS is the dimension of the best harmony vector…”
Section: Resultsmentioning
confidence: 99%
“…The computational complexity of the DE variant in the experimental study; DE/rand/1/bin is computed as O(n • d • K) [38]. Also, the computational complexity of the HS algorithm is O(n • d • K) [39]. As it can be shown, either the algorithmic structures of DE and HS are not complex, which makes efficient alternatives to implement to solve multiple optimization problems.…”
Section: Computational Effort and Time Complexitymentioning
confidence: 99%
“…However, it is difficult to constrain the speed and acceleration of the manipulator in the planning, which often leads to the over-limit problem of the manipulator's joints in pursuit of time optimization. For such problems, trajectory planning models need to be built, and then relevant constraints are added in combination with actual problems, and optimization algorithms are used to solve them, such as MOABC [12], MOHS [13], PSO [14], MOSFLA [15], PHD [16], etc.…”
Section: Introductionmentioning
confidence: 99%