An application of artificial bee colony algorithm with least squares support vector machine for real and reactive power tracing in deregulated power system

Sulaiman, Mohd Herwan; Mustafa, Mohd Wazir; Shareef, Hussain; Khalid, Saifulnizam Abd.

doi:10.1016/j.ijepes.2011.12.007

Cited by 62 publications

(26 citation statements)

References 36 publications

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“…Updating the best fruit fly and global best fruit fly according to the fitness value. Then divided the group into two subgroups according to (10) and update the location of the fruit flies in each subgroups according to (11) and (12).…”

Section: Parameters Optimization Of Svm With Ifoamentioning

confidence: 99%

“…But there is an existing fact that the kernel function parameters and the penalty factor of SVM affect its classification performance seriously and the parameters are difficult to select due to the lack of corresponding theoretical basis. In order to find the appropriate parameters of SVM, many intelligent evolutionary algorithm, such as genetic algorithm (GA) [7], particle swarm optimization (PSO) [8], ant colony optimization algorithm (ACO) [9], and artificial bee colony optimization (ABC) [10], have been carried out on SVM parameters optimization, and improved the performance of SVM to some extent. However, due to the defects of algorithm itself, those methods are still time consuming and does not performed very well at many situations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parameters Optimization of SVM Based on Improved FOA and Its Application in Fault Diagnosis

Zhang¹,

Fang²

2015

JSW

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Abstract:In most cases, fault diagnosis is essentially a pattern recognition problem and support vector machine (SVM) provides a new solution for the diagnosis problem of systems in which the fault samples are few. However, the parameters selection in SVM has significant influence on the diagnosis performance. In this paper, improved fruit fly optimization algorithm (IFOA), which is basically the standard fruit fly optimization algorithm (FOA) combined with Levy flight search strategy, is proposed to determine the SVM parameters. Some benchmark datasets are used to evaluate the proposed algorithm. Furthermore, the proposed method is used to diagnose the faults of hydraulic pump. Experiments and engineering application show that the proposed method outperforms standard FOA, genetic algorithm (GA) and particle swarm optimization (PSO) methods.

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Section: Parameters Optimization Of Svm With Ifoamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameters Optimization of SVM Based on Improved FOA and Its Application in Fault Diagnosis

Zhang¹,

Fang²

2015

JSW

View full text Add to dashboard Cite

show abstract

“…But these methods have several drawbacks, for example, both the grid search and cross-validation require long and complicated calculations. In recent years, some intelligent evolutionary algorithm, such as genetic algorithm (GA) [1], particle swarm optimization (PSO) [2], ant colony optimization algorithm (ACO) [3], artificial fish swarm algorithm (AFSA) [4] and artificial bee colony optimization (ABC) [5] have also been used to optimize the SVM parameters for their good global search abilities.…”

Section: Introductionmentioning

confidence: 99%

Research on Parameters Optimization of SVM Based on Improved Fruit Fly Optimization Algorithm

Zhang¹,

Fang²,

Ma³

et al. 2016

IJCTE

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Abstract-The performance of the support vector machine (SVM) is determined to a great extent by the parameter selection. In order to improve the learning and generalization ability of SVM, in this paper, an improved fruit fly optimization algorithm (IFOA) was proposed to optimize kernel parameter and penalty factor of SVM. In IFOA, the fruit fly group is dynamically divided into advanced subgroup and drawback subgroup according to its own evolutionary level. A global search is made for the drawback subgroup under the guidance of the best individual and a finely local search is made for the advanced subgroup in which the fruit flies do Levy flight around the best individual. Two subgroups exchange information by updating the overall optimum and recombining the subgroups. Getting rid of local optimum and improve search ability are ensured by making those changes in basic FOA. The performance of the IFOA and classification accuracy of optimized SVM based on IFOA are respectively examined through several typical benchmark functions and classical data sets from UCI benchmark. The experiment results show that the performance of the new algorithm is obviously more successful than FOA and it is also an effective SVM parameter optimization method which has better performance than some other methods.Index Terms-Support vector machine, fruit fly optimization algorithm, parameter optimization, Levy flight.

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“…The swarm updating in ABC is due to two processes namely, the variation process and the selection process which are responsible for exploration and exploitation, respectively. The ABC algorithm has successfully been tested on almost all domains of science and engineering like electronics engineering (Chidambaram and Lopes 2009;Kavian et al 2012), electrical engineering (Jones and Bouffet 2008;Nayak et al 2009;Sulaiman et al 2012), computer science engineering (Karaboga and Cetinkaya 2011;Lam et al 2012;Lei et al 2010), mechanical engineering (Banharnsakun et al 2012;Pawar et al 2008;Xu and Duan 2010), civil engineering (Akay and Karaboga 2012;Li et al 2011;Mandal et al 2012), medical pattern classification and clustering problems (Karaboga et al 2008) and mathematical graph problems (Xing et al 2007;Singh 2009;Yeh and Hsieh 2011). Many of the recent modifications and applications of ABC algorithm can be studied in Bansal et al (2013b).…”

Section: Introductionmentioning

confidence: 99%

Optimal power flow using artificial bee colony algorithm with global and local neighborhoods

Bansal

Jadon

Tiwari

et al. 2014

Int J Syst Assur Eng Manag

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Optimal power flow (OPF) is one of the most requisite tools for power system operation analysis. This problem has a complex mathematical formulation which is relatively hard to solve. This paper presents a swarm intelligence-based approach to solve the OPF problem. The proposed approach describes the use of a modified artificial bee colony (ABC) algorithm called ABC with global and local neighborhoods (ABCGLN) to determine the optimal settings of OPF control variables. ABCGLN is a recent modified version of basic ABC algorithm that can handle non-differentiable, non-linear, and multi modal objective functions. The ABCGLN approach is tested here on the standard IEEE 30-bus test system with three different objective functions for minimizing quadratic fuel cost function, piecewise quadratic cost function and quadratic cost function with valve point effects. The simulation results demonstrate the potential of ABCGLN algorithm of finding effective and robust quality solutions to solve OPF problem with various objective functions for the considered system as compared to those available in the literature.

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An application of artificial bee colony algorithm with least squares support vector machine for real and reactive power tracing in deregulated power system

Cited by 62 publications

References 36 publications

Parameters Optimization of SVM Based on Improved FOA and Its Application in Fault Diagnosis

Parameters Optimization of SVM Based on Improved FOA and Its Application in Fault Diagnosis

Research on Parameters Optimization of SVM Based on Improved Fruit Fly Optimization Algorithm

Optimal power flow using artificial bee colony algorithm with global and local neighborhoods

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