Music Composition Using Harmony Search Algorithm

Geem, Zong Woo; Choi, Jeongyoon

doi:10.1007/978-3-540-71805-5_65

Cited by 62 publications

(28 citation statements)

References 9 publications

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“…It is shown that the HS-ANN model gives the most excellent predictive ability and stability with HMCR = 0.7 and PAR = 0.5 or HMCR = 0.9 and PAR = 0.1. This result corresponds to Geem [33] who suggested that the optimal ranges of HMCR = 0.7-0.95 and PAR = 0.1-0.5. Comparing the statistical parameters between the best HS-ANN model and the ANN model, the HS-ANN model with proper values of HMCR and PAR can give much better and stable prediction than the conventional ANN model.…”

Section: Assessment Of Accuracy and Stability Of The Modelssupporting

confidence: 87%

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Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones

2016

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Abstract:In this study, an artificial neural network (ANN) model is developed to predict the stability number of breakwater armor stones based on the experimental data reported by Van der Meer in 1988. The harmony search (HS) algorithm is used to determine the near-global optimal initial weights in the training of the model. The stratified sampling is used to sample the training data. A total of 25 HS-ANN hybrid models are tested with different combinations of HS algorithm parameters. The HS-ANN models are compared with the conventional ANN model, which uses a Monte Carlo simulation to determine the initial weights. Each model is run 50 times and the statistical analyses are conducted for the model results. The present models using stratified sampling are shown to be more accurate than those of previous studies. The statistical analyses for the model results show that the HS-ANN model with proper values of HS algorithm parameters can give much better and more stable prediction than the conventional ANN model.

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Section: Assessment Of Accuracy and Stability Of The Modelssupporting

confidence: 87%

“…There are two major components in metaheuristic algorithms: diversification and intensification [33]. These two components seem to be contradicting each other, but balancing their combination is crucial and important to the success of a metaheuristic algorithm.…”

Section: Aspect Of Transition Of Weightsmentioning

confidence: 99%

Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones

2016

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“…Random selection explores global search space more widely and efficiently while the pitch adjustment makes the new solution good enough and near the existing good solutions. The intensification in HS algorithm is controlled by memory consideration, leading the searching process toward the searching space of good solutions [19]. Also the use of the HM in HS allows the selection of the best vectors that may represent different regions in the search space.…”

Section: B the Hybrid Ifa-hs Methodsmentioning

confidence: 99%

Optimum Cost Design of Reinforced Concrete Retaining Walls Using Hybrid Firefly Algorithm

Sheikholeslami¹,

Khalili²,

Zahrai³

2014

IJET

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Abstract-This paper develops a novel optimization method namely hybrid firefly algorithm with harmony search technique (IFA-HS), to obtain the optimal cost of the reinforced concrete retaining walls satisfying the stability criteria. The hybrid IFA-HS is utilized to find the economical design adhering to provisions of ACI 318-05. Also Coulomb lateral earth pressure theory is used to derive the lateral total thrust on the wall. Some design examples are tested using the new method. The results carried out on these examples confirm the validity of the proposed algorithm. The IFA-HS method can be considered as an improvement of the recently developed firefly algorithm. The improvements include the utilizing of a memory that contains some information extracted online during the search, adding of pitch adjustment operation in harmony search serving as mutation operator during the process of the firefly updating, and modifying the movement phase of firefly algorithm. The detailed implementation procedure for this improved meta-heuristic method is also described.Index Terms-Concrete retaining wall, firefly algorithm, harmony search.

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“…The harmony search algorithm is a recently proposed metaheuristic search technique that mimics the process of music improvisation [23][24][25][26][27]. There are different variants of this algorithm that have been created to improve it or solve specific problems [19,[28][29][30].…”

Section: Harmony Search Algorithmmentioning

confidence: 99%

Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm

2017

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This article focuses on the dynamic parameter adaptation in the harmony search algorithm using Type-1 and interval Type-2 fuzzy logic. In particular, this work focuses on the adaptation of the parameters of the original harmony search algorithm. At present there are several types of algorithms that can solve complex real-world problems with uncertainty management. In this case the proposed method is in charge of optimizing the membership functions of three benchmark control problems (water tank, shower, and mobile robot). The main goal is to find the best parameters for the membership functions in the controller to follow a desired trajectory. Noise experiments are performed to test the efficacy of the method.

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Music Composition Using Harmony Search Algorithm

Cited by 62 publications

References 9 publications

Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones

Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones

Optimum Cost Design of Reinforced Concrete Retaining Walls Using Hybrid Firefly Algorithm

Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm

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