On the Analysis of Performance of the Improved Artificial-Bee-Colony Algorithm

Quan, Haiyan; Shi, Xinling

doi:10.1109/icnc.2008.211

Cited by 29 publications

(15 citation statements)

References 2 publications

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“…The flow chart of the ABC algorithm is given in Fig. 1 [28]. In the first step of the algorithm random solutions are produced in the interval of the variables xi (i= 1, ..., S).…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

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Active power loss minimization in electric power systems through chaotic artificial bee colony algorithm

et al. 2016

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Original scientific paper Reactive power optimization (RPO) is a major field of study to ensure power systems for operating in a secure and economical manner. RPO can be used for decreasing of active power losses, voltage control, and for the optimization of the power coefficients in power systems. The non-linear power loss function is used as an object function that will be minimized. In this study Chaotic Artificial Bee Colony (CABC) algorithm is used to minimize the active power loss of power systems. Chaotic maps such as logistic map and Henon map are used against the random number generator. CABC is applied on the IEEE6-bus and IEEE 30-bus test systems and the results are shown. Accordingly, the results have been evaluated and observed that the stability critical values found by CABC can be used to produce good potential solutions. Simulation results are promising and show the effectiveness of the applied approach. Keywords: chaos; Chaotic Artificial Bee Colony; optimization; power systemsSmanjenje gubitka aktivne snage u elektro-energetskim sustavima primjenom algoritma kaotične umjetne kolonije pčela Izvorni znanstveni članak Optimizacija reaktivne snage -Reactive power optimization (RPO) -osnovno je područje istraživanja u svrhu sigurnog i ekonomičnog rada energetskih sustava. RPO se može primijeniti za smanjenje gubitaka aktivne snage, reguliranje napona te za optimizaciju energetskih koeficijenata u energetskim sustavima. Funkcija ne-linearnog gubitka snage koristi se kao funkcija cilja koju treba smanjiti. U ovom se radu algoritam Kaotične umjetne kolonije pčela -Chaotic Artificial Bee Colony (CABC) -primjenjuje za smanjenje gubitka aktivne snage u energetskim sustavima. Rabe se kaotične mape kao što su logistička mapa i Henon mapa. CABS se primjenjuje na provjeravanim sustavima IEEE 6-sabirnice i IEEE 30-sabirnice i daju se rezultati. Provjerom rezultata ustanovilo se da primjena kritičnih vrijednosti stabilnosti dobivenih pomoću CABS može rezultirati dobrim potencijalnim rješenjima. Rezultati simulacije su obećavajući i pokazuju učinkovitost primijenjenog pristupa.

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“…The flow chart of the ABC algorithm is given in Fig. 1 [28]. In the first step of the algorithm random solutions are produced in the interval of the variables xi (i= 1, ..., S).…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

“…Scout bees do not use any pre knowledge while they are searching for nectar sources, but they absolutely do their researches randomly [28]. The scout bees are chosen among the employed bees.…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

Active power loss minimization in electric power systems through chaotic artificial bee colony algorithm

et al. 2016

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“…Quan and Shi introduced the Improved Artificial-Bee-Colony Algorithm [17]. In this work, a new search cycle operator based on the fixed point theorem of contractive mapping in banach spaces was proposed.…”

Section: Modified Versions Of Abcmentioning

confidence: 99%

A review on Artificial Bee Colony algorithm

Verma

Kumar

2013

IJET

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In recent years large number of algorithms based on the swarm intelligence has been proposed by various researchers. The Artificial Bee Colony (ABC) algorithm is one of most popular stochastic, swarm based algorithm proposed by Karaboga in 2005 inspired from the foraging behavior of honey bees. In short span of time, ABC algorithm has gain wide popularity among researchers due to its simplicity, easy to implementation and fewer control parameters. Large numbers of problems have been solved using ABC algorithm such as travelling salesman problem, clustering, routing, scheduling etc. the aim of this paper is to provide up to date enlightenment in the field of ABC algorithm and its applications.

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“…The elitist ABC (EABC) [22] is a hybrid ABC variant which hybridizes ABC with two different local search operators to intensify the exploitations around the best solutions. Quan and Shi [23] reported improvement of the convergence speed by introducing an exploitative search iteration operator based on the fixed point theorem of contractive mapping. Qingxian and Haijun [24] employed the Boltzmann selection scheme and introduced an improved initialization scheme to improve the convergence speed.…”

Section: Existing Variants Of the Abc Algorithmmentioning

confidence: 99%

On the Performance of Explorative Artificial Bee Colony Algorithm for Numeric Function Optimization

Ahmed¹,

Shahriar²,

Shafiul³

2015

IJAIS

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The Explorative Artificial Bee Colony (EABC) algorithm is a recently introduced swarm intelligence based algorithm that has been successfully tested to optimize only a limited number of multimodal functions. This paper evaluates EABC on a larger number of benchmark functions, including both unimodal and multimodal functions. EABC is an improved variant of the Artificial Bee Colony (ABC) algorithm. A major problem with the basic ABC algorithm is that it is more aligned towards exploitations, rather than explorations, which often leads to premature convergence and fitness stagnation. The improved variant -EABC tries to increase the degree of explorations of ABC by introducing more randomness during its perturbation operations. Besides, EABC customizes the degree of exploitations and explorations at the individual solution level, separately for each candidate solution of the bee population. EABC also introduces a crossover operation that assists the explorative perturbation operation of EABC. This paper extends the experimental studies on EABC by evaluating it on as many as 13 complex, high dimensional benchmark functions, including both unimodal and multimodal, separable and non-separable functions. The results are compared with the basic ABC algorithm. The comparison demonstrates that EABC often performs better optimization than the original ABC algorithm, which indicates the effectiveness of its more explorative operations.

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On the Analysis of Performance of the Improved Artificial-Bee-Colony Algorithm

Cited by 29 publications

References 2 publications

Active power loss minimization in electric power systems through chaotic artificial bee colony algorithm

Active power loss minimization in electric power systems through chaotic artificial bee colony algorithm

A review on Artificial Bee Colony algorithm

On the Performance of Explorative Artificial Bee Colony Algorithm for Numeric Function Optimization

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