Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems

Kumar, Vijay; Chhabra, Jitender Kumar; Kumar, Dinesh

doi:10.1016/j.jocs.2013.12.001

Cited by 118 publications

(44 citation statements)

References 16 publications

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“…The fixed value of HMCR prevented to achieve globally optimized solution. Kumar et al [30] proposed a dynamic change in the values of PAR and HMCR consequently modifying the improvisation step of IHS. Initially HS algorithm explores the entire search space, and thereafter a few initial generations, making it confined to a local space.…”

Section: Step 5: Check the Termination Criterionmentioning

confidence: 99%

Evaluation of Harmony Search Optimization to Predict Local Scour Depth around Complex Bridge Piers

2018

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One of the main causes of bridge collapse may be flood flow scour near piers. Several experimental and local field investigations were carried out to study scour depth. However, existing empirical equations do not commonly provide accurate scour prediction due to the complexity of the scour process. Physical and economic considerations often lead to bridge foundation constructs which included a pier column based on a pile cap supported by an array of piles. Piers with this configuration are referred to as complex piers. A few studies have been done on complex bridge pier scour depth estimation. Such efforts may be classified into theoretical and empirical equations. This paper investigates local scour around complex bridge piers by using harmony search algorithm under clear water conditions. Statistical indices such as the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were used to evaluate the performance of these methods. By designing laboratory tests, 82 experimental data points were measured by authors. Also 615 experimental data sets with the same measured experimental conditions were collected from published literature and used for optimization. The results show that the developed HS model can predict scour depth better than other equations according to statistical indices.

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Section: Step 5: Check the Termination Criterionmentioning

confidence: 99%

Evaluation of Harmony Search Optimization to Predict Local Scour Depth around Complex Bridge Piers

2018

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“…They claimed that a higher selection probability of HM vector would trigger memory consideration in choosing the value of the decision variable from the vector. A recent study by [19] made an adjustment to the parameter setting in HMCR and PAR. Their study recommended combinations of four different cases of HMCR and PAR with all of the parameters of PAR are linearly and exponentially decreased.…”

Section: Previous Improvementmentioning

confidence: 99%

A New HMCR Parameter of Harmony Search for Better Exploration

Mansor

Abas

Rahman

et al. 2015

Advances in Intelligent Systems and Computing

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As a meta-heuristic algorithm, Harmony Search (HS) algorithm is a population-based meta-heuristics approach that is superior in solving diversified large scale optimization problems. Several studies have pointed that Harmony Search (HS) is an efficient and flexible tool to resolve optimization problems in diversed areas of construction, engineering, robotics, telecommunication, health and energy. In this respect, the three main operators in HS, namely the Harmony Memory Consideration Rate (HMCR), Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing the local exploitation and the global exploration. These parameters influence the overall performance of HS algorithm, and therefore it is very crucial to fine turn them. However, when performing a local search, the harmony search algorithm can be easily trapped in the local optima. Therefore, there is a need to improve the fine tuning of the parameters.

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“…It is a model based approach to solve clustering problems [18]. It clusters data in different manner than K-Means.…”

Section: Expectation-maximization Clustering Algorithmmentioning

confidence: 99%

“…To enrich the searching behaviour and to avoid being trapped in a local optimum, Kumar et al [18] proposed a parameter adaptive harmony search (PAHS) algorithm. In PAHS algorithm, the two control parameter named as Harmony Memory Consideration Rate (HMCR) and Pitch Adjustment Rate (PAR), were being allowed to change dynamically.…”

Section: Parameter Adaptive Harmony Search Algorithmmentioning

confidence: 99%

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A Hybrid Approach for Data Clustering using Expectation-Maximization and Parameter Adaptive Harmony Search Algorithm

2015

March 29-30, 2015 Singapore

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This paper presents a novel hybrid data clustering algorithm based on parameter adaptive harmony search algorithm. The recently developed parameter adaptive harmony search algorithm (PAHS) is used to refine the cluster centers, which are further used in initializing Expectation-Maximization clustering algorithm. The optimal number of clusters are determined through four well-known cluster validity indices. The proposed algorithm is evaluated on three real life datasets and compared with the performance of K-Means, Fuzzy CMeans and HS initialize EM (HSEM). Experimental results reveal that the proposed approach provide better results in terms of precision, recall, weighted average, F-Measure and G-Measure.

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Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems

Cited by 118 publications

References 16 publications

Evaluation of Harmony Search Optimization to Predict Local Scour Depth around Complex Bridge Piers

Evaluation of Harmony Search Optimization to Predict Local Scour Depth around Complex Bridge Piers

A New HMCR Parameter of Harmony Search for Better Exploration

A Hybrid Approach for Data Clustering using Expectation-Maximization and Parameter Adaptive Harmony Search Algorithm

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