A novel artificial bee colony algorithm with diversity strategy

Lee, Wei-Ping; Cai, Wanting

doi:10.1109/icnc.2011.6022505

Cited by 23 publications

(26 citation statements)

References 10 publications

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“…The next comparison is made between ABC-AX 2 and DABC [17]. DABC tries to maintain sufficient amount of diversity among the candidate solutions to allow more search space explorations.…”

Section: First Abc-axmentioning

confidence: 99%

“…In this section ABC-AX 2 is compared with some other recent variants of ABC, such as the cooperative ABC (CABC) [16], ABC with diversity strategy [17], chaotic ABC (CHABC) [18], gbest-guided ABC (GABC) [20] and Hooke Jeeves ABC (HJABC) [21]. The first three variants (e.g., [16]- [18]) increase the degree of explorations, while the last two variants (e.g., [20]- [21]) increase the intensity of exploitations.…”

Section: Comparison With Other Abc-variantsmentioning

confidence: 99%

“…Thus, most existing ABC-variants try to improve either the exploitative (e.g., [20]- [24]) or the explorative ( [15]- [19]) characteristics of the basic ABC algorithm. The exploitative improvements are usually based on intensifying the search around the best solution(s) found so far (e.g., [20], [21], [24]) and hybridizing efficient local search operators with ABC (e.g., [21], [23], [24]), while the explorative improvements can be based on more population diversity (e.g., [16], [17]) and more explorative selection and/or perturbation operations ( [15], [18], [19]). But none of these algorithms considers the individual explorative/exploitative requirements of each candidate solution separately; rather they employ some population-wide global strategy, identically for all candidate solutions, which is significantly improved in the proposed algorithm -ABC-AX 2 , as described in the following section.…”

Section: Existing Variants Of Abc Algorithmmentioning

confidence: 99%

“…The cooperative ABC (CABC) [16] tries to enforce more explorations by decomposing the search space into multiple subspaces and by employing multiple bee colonies to explore through different subspaces. ABC with diversity strategy (DABC) [17] tries to maintain sufficient level of population diversity for conducting more explorations by alternating between two different perturbation schemes. Chaotic ABC (ChABC) [18] tries to improve the explorative characteristics of ABC by employing chaotic dynamics instead of random number generators.…”

Section: Existing Variants Of Abc Algorithmmentioning

confidence: 99%

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Artificial Bee Colony Algorithm with Adaptive Explorations and Exploitations: A Novel Approach for Continuous Optimization

ShafiulAlam¹,

Islam²,

Murase³

2015

IJAIS

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A proper balance between global explorations and local exploitations is often considered necessary for complex, high dimensional optimization problems to avoid local optima and to find a good near optimum solution with sufficient convergence speed. This paper introduces Artificial Bee Colony algorithm with Adaptive eXplorations and eXploitations (ABC-AX 2 ), a novel algorithm that improves over the basic Artificial Bee Colony (ABC) algorithm. ABC-AX 2 augments each candidate solution with three control parameters that control the perturbation rate, magnitude of perturbations and proportion of explorative and exploitative perturbations. Together, all the control parameters try to adapt the degree of global explorations and local exploitations around each candidate solution by affecting how new trial solutions are produced from the existing ones. The control parameters are automatically adapted at the individual solution level, separately for each candidate solution. ABC-AX 2 is tested on a number of benchmark problems of continuous optimization and compared with the basic ABC algorithm and several other recent variants of ABC algorithm. Results show that the performance of ABC-AX 2 is often better than most other algorithms in comparison, in terms of both convergence speed and final solution quality.

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“…The next comparison is made between ABC-AX 2 and DABC [17]. DABC tries to maintain sufficient amount of diversity among the candidate solutions to allow more search space explorations.…”

Section: First Abc-axmentioning

confidence: 99%

Section: Comparison With Other Abc-variantsmentioning

confidence: 99%

Section: Existing Variants Of Abc Algorithmmentioning

confidence: 99%

Section: Existing Variants Of Abc Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Bee Colony Algorithm with Adaptive Explorations and Exploitations: A Novel Approach for Continuous Optimization

ShafiulAlam¹,

Islam²,

Murase³

2015

IJAIS

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show abstract

“…Global ABC algorihm, which was introduced by Guo et al [2011], proposed three modified search equation by using global and individual best positions. To balance between exploring and exploiting, a diversity stategy have used by Lee and Cai [2011].…”

Section: E22 Variants Of the Artificial Bee Colony Algorithmmentioning

confidence: 99%

Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems

Liao¹

2015

4OR-Q J Oper Res

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ii SummaryContinuous optimization problems are optimization problems where all variables have a domain that typically is a subset of the real numbers; mixed discretecontinuous optimization problems have additionally other types of variables, so that some variables are continuous and others are on an ordinal or categorical scale. Continuous and mixed discrete-continuous problems have a wide range of applications in disciplines such as computer science, mechanical or electrical engineering, economics and bioinformatics. These problems are also often hard to solve due to their inherent difficulties such as a large number of variables, many local optima or other factors making problems hard. Therefore, in this thesis our focus is on the design, engineering and configuration of high-performing heuristic optimization algorithms.We tackle continuous and mixed discrete-continuous optimization problems with two classes of population-based heuristic algorithms, ant colony optimization (ACO) algorithms and evolution strategies. In a nutshell, the main contributions of this thesis are that (i) we advance the design and engineering of ACO algorithms to algorithms that are competitive or superior to recent state-of-the-art algorithms for continuous and mixed discrete-continuous optimization problems, (ii) we improve upon a specific state-of-the-art evolution strategy, the covariance matrix adaptation evolution strategy (CMA-ES), and (iii) we extend CMA-ES to tackle mixed discrete-continuous optimization problems.More in detail, we propose a unified ant colony optimization (ACO) framework for continuous optimization (UACOR). This framework synthesizes algorithmic components of two ACO algorithms that have been proposed in the literature and an incremental ACO algorithm with local search for continuous optimization, which we have proposed during my doctoral research. The design of UACOR allows the usage of automatic algorithm configuration techniques to automatically derive new, high-performing ACO algorithms for continuous optimization. We also propose iCMAES-ILS, a hybrid algorithm that loosely couples IPOP-CMA-ES, a CMA-ES variant that uses a restart schema coupled with an increasing population iii size, and a new iterated local search (ILS) algorithm for continuous optimization. The hybrid algorithm consists of an initial competition phase, in which IPOP-CMA-ES and the ILS algorithm compete for further deployment during a second phase. A cooperative aspect of the hybrid algorithm is implemented in the form of some limited information exchange from IPOP-CMA-ES to the ILS algorithm during the initial phase. Experimental studies on recent benchmark functions suites show that UACOR and iCMAES-ILS are competitive or superior to other state-of-the-art algorithms.To tackle mixed discrete-continuous optimization problems, we extend ACO MV and propose CES MV , an ant colony optimization algorithm and a covariance matrix adaptation evolution strategy, respectively. In ACO MV and CES MV , the decision variables of an optimization prob...

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Artificial bee colony–based spectrum handoff algorithm in wireless cognitive radio networks

Bayrakdar

Çalhan

2017

Int J Communication

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In this work, we proposed a new artificial bee colony-based spectrum handoff algorithm for wireless cognitive radio systems. In our wireless cognitive radio system, primary users, secondary users, and related base stations exist in the same communication environment. For our artificial bee colony-based algorithm, secondary users that always struggle to discover an idle channel have a leading role. While honey bees work hard to find the best-quality nectar source for foraging, secondary users try to find idle channels for making communication. In this way, secondary users are organized for different missions such as sensing and handoff similar to honey bees to minimize spectrum handoff delay by working together. In the spectrum handoff stage, some secondary users must sense the spectrum so that the interrupted secondary user may perform the spectrum handoff process. In our developed spectrum handoff algorithm, the spectrum availability characteristic is observed on the basis of the missions of the bees in the artificial bee colony algorithm with the aim of minimizing the spectrum handoff delay and maximizing probability of finding an idle channel.With the help of the algorithm that is developed using the artificial bee colony, spectrum handoff delay of secondary users is considerably decreased for different number of users without reducing probability of finding an available channel.

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A novel artificial bee colony algorithm with diversity strategy

Cited by 23 publications

References 10 publications

Artificial Bee Colony Algorithm with Adaptive Explorations and Exploitations: A Novel Approach for Continuous Optimization

Artificial Bee Colony Algorithm with Adaptive Explorations and Exploitations: A Novel Approach for Continuous Optimization

Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems

Artificial bee colony–based spectrum handoff algorithm in wireless cognitive radio networks

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