Binary-Real Coded Genetic Algorithm  Based  &amp;lt;i&amp;gt;k&amp;lt;/i&amp;gt;-Means Clustering for Unit  Commitment Problem

Farag, M. A.; El-Shorbagy, M. A.; Eldesoky, Islam M.; El‐Sawy, A. A.; Mousa, Abd Allah A.

doi:10.4236/am.2015.611165

Cited by 15 publications

(4 citation statements)

References 46 publications

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“…The most popular clustering tool used in scientific and industrial applications is The K-means clustering algorithm [16]- [20]. It proceeds as follows: Firstly, K objects are randomly selected; where each of which initially represents a cluster mean or center.…”

Section: Clusteringmentioning

confidence: 99%

Hybrid Genetic Algorithm with K-Means for Clustering Problems

Malki¹,

Rizk²,

El-Shorbagy³

et al. 2016

OJOp

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The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty clusters depending on initial center vectors. Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary principles of natural selection and genetics. This paper presents a hybrid version of the k-means algorithm with GAs that efficiently eliminates this empty cluster problem. Results of simulation experiments using several data sets prove our claim.

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Section: Clusteringmentioning

confidence: 99%

Hybrid Genetic Algorithm with K-Means for Clustering Problems

Malki¹,

Rizk²,

El-Shorbagy³

et al. 2016

OJOp

Self Cite

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“…In addition, the hybrid algorithms have also been developed and applied to a variety of MOPs and their application such as [31][32][33][34][35][36]. Hybrid algorithms can be more efficient than the basic versions of the basic approaches, because of the fact that the hybrid algorithm benefits from all the advantages of the basic algorithms.…”

Section: Different Conventional Algorithms Have Been Developed To Solvementioning

confidence: 99%

A New Hybrid Metaheuristic Algorithm for Multiobjective Optimization Problems

Farag¹,

El-Shorbagy²,

Mousa³

et al. 2020

IJCIS

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The elitist nondominated sorting genetic algorithm (NSGA-II) is hybridized with the sine-cosine algorithm (SCA) in this paper to solve multiobjective optimization problems. The proposed hybrid algorithm is named nondominated sorting sine-cosine genetic algorithm (NS-SCGA). The main idea of this algorithm is the following: NS-SCGA integrates the merits of exploitation capability of NSGA-II and exploration capability of SCA for a better search ability and speeds up the searching process. The performance of NS-SCGA is tested on the set of benchmark functions provided for CEC09. The NS-SCGA results are compared with other recently developed multiobjective algorithms in terms of convergence, spacing, and spread of the obtained nondominated solutions to the true Pareto front. The statistical analysis of the results obtained is performed by nonparametric Friedman and Wilcoxon signed-rank tests. The results prove that NS-SCGA is superior to or competitive with other multiobjective optimization algorithms considered in the comparison. Furthermore, the economic emission dispatch problem (EEDP) is solved by NS-SCGA. The operating cost (fuel cost) and pollutant emission of the standard IEEE 30-bus network with six generating units are minimized simultaneously by the NS-SCGA considering the losses. The results show the superiority of NS-SCGA and confirm its ability in solving EEDP. Finally, TOPSIS technique is applied to choose the best compromise solution from the obtained Pareto-optimal solutions of EEDP according to the decision-maker's preference.

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“…On the other hand, GA is one of the PBAs that introduced in 1975 by Holland [60], and described in 1989 by Goldberg [61] as a proficient global technique for solving complex optimization problems based on the technicalities of natural selection, evolution, and genetics. GA is well suitable for solving optimization problems and has still garners great attention by researchers [62][63][64][65]. From the literature we can see that GA was widely applied to solve SNLEs [66][67][68][69][70][71][72].…”

Section: Introductionmentioning

confidence: 99%