Gaussian bare-bones artificial bee colony algorithm

Zhou, Xinyu; Wu, Zhijian; Wang, Hui; Rahnamayan, Shahryar

doi:10.1007/s00500-014-1549-5

Cited by 68 publications

(47 citation statements)

References 56 publications

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“…Gao et al [30] applied the orthogonal experimental design to form an orthogonal learning strategy for ABC, and proposed a new ABC algorithm based on modified search equation and orthogonal learning (OCABC). Zhou et al [41] adopted Gaussian bare-bones search equation in the onlooker bee phase, and proposed Gaussian bare-bones ABC (GBABC). Cui et al [18] developed a ranking-based adaptive ABC (ARABC), in which DE mutation operator "rand/1" as the search equation, and the parent food sources used in the search equation are chosen based on their rankings.…”

Section: Literature Reviewmentioning

confidence: 99%

Self-adaptive differential artificial bee colony algorithm for global optimization problems

Tianfield

2019

Swarm and Evolutionary Computation

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Artificial bee colony algorithm (ABC) has attracted wide attention in the recent decade. Although ABC algorithms can achieve good performance on separable problems by optimizing each variable independently, their performances on complex non-separable problems are still unsatisfactory. In this paper, through incorporating multiple differential search strategies and a self-adaptive mechanism within the framework of ABC, we propose a new ABC algorithm, called self-adaptive differential artificial bee colony (sdABC) algorithm. By means of differential search strategies, more variables will be updated each time based on the combination of mutation and crossover. Thus, sdABC has much enhanced ability for solving complex non-separable problems. Our proposed sdABC algorithm is evaluated on 28 benchmarks functions, including both common separable problems and complex non-separable CEC2015 functions. The experimental results show that sdABC can achieve much more desirable results compared with previous ABC algorithms on both separable and non-separable functions. Furthermore, the performance of our sdABC is also very competitive compared with well-established differential evolution and other meta-heuristic algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Self-adaptive differential artificial bee colony algorithm for global optimization problems

Tianfield

2019

Swarm and Evolutionary Computation

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“…Therefore, in order to address these issues in Eq. (5), Zhou et al [48] designed a Gaussian bare-bones search equation inspired by the concept of BBPSO, shown in Eq. (6).…”

Section: Improved Abcsmentioning

confidence: 99%

“…(6) in the onlooker bee phase, the newly proposed algorithm is named ABC-BB. Moreover, Zhou [48] also proposed a general opposition learning strategy (GOBL) in the scout bee phase. By combining the ABC-BB and GOBL, they [48] proposed a new ensemble algorithm (GBABC).…”

Section: Improved Abcsmentioning

confidence: 99%

“…Moreover, Zhou [48] also proposed a general opposition learning strategy (GOBL) in the scout bee phase. By combining the ABC-BB and GOBL, they [48] proposed a new ensemble algorithm (GBABC). Similarly, Cui et al [15] also proposed a novel search equation to avoid the oscillation phenomenon of GABC, shown in Eq.…”

Section: Improved Abcsmentioning

confidence: 99%

“…However, as claimed in [25], the search equation of GABC may cause an oscillation phenomenon and thus may also degrade convergence since the guidance of the last two terms may be in opposite directions. Afterwards, Zhou [48] proposed an improved ABC with Gaussian bare-bones search equation (ABC-BB for short) algorithm based on the utilization of the global best solution to make up the defect of GABC. Moreover, they proposed an ensemble algorithm composed of ABC-BB and general opposition learning initialization strategy (GOBL), named GBABC.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced artificial bee colony with novel search strategy and dynamic parameter

Chen

2019

COMSIS J

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There is only one guiding solution in the search equation of Gaussian bare-bones artificial bee colony algorithm (ABC-BB), which is easy to result in the problem of premature convergence and trapping into the local minimum. In order to enhance the capability of escaping from local minimum without loss of the exploitation ability of ABC-BB, a new triangle search strategy is proposed. The candidate solution is generated among the triangle area formed by current solution, global best solution and any randomly selected elite solution to avoid the premature convergence problem. Moreover, the probability of crossover is controlled dynamically according to the successful search experience, which can enable ABC-BB to adapt all kinds of optimization problems with different landscapes. The experimental results on a set of 23 benchmark functions and two classic real-world engineering optimization problems show that the proposed algorithm is significantly better than ABC-BB as well as several recently-developed state-of-the-art evolution algorithms.

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Group‐guided artificial bee colony algorithm with elastic adjustment strategy

Wang

Jie

Jiang

2022

Concurrency and Computation

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Artificial bee colony algorithm (ABC) is a new intelligent optimization method based on swarm intelligence. It is an algorithm that simulates the social behavior of bees in nature and has a performance of good search in all kinds of optimization problems.Over the past few years, ABC algorithm has become popular because of its stable performance and good structure. ABC algorithm performance, however, is often influenced by both exploitation and exploration abilities, and balancing exploitation and exploration is particularly important. This article proposes a novel variant of ABC, which is called elastic adjusted group guided artificial bee colony algorithm (EGABC). In EGABC, we propose a group guidance strategy with flexible ability to adjust. The main purpose is to effectively use the known information, speed up convergence, and play a balance between search exploitation and exploration ability. To verify the validity of EGABC, we conducted experiments on 13 classic benchmark problems and CEC2013 test suite. Based on experimental results, the EGABC strategy is highly competitive and can improve the ABC algorithm's performance significantly.

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Gaussian bare-bones artificial bee colony algorithm

Cited by 68 publications

References 56 publications

Self-adaptive differential artificial bee colony algorithm for global optimization problems

Self-adaptive differential artificial bee colony algorithm for global optimization problems

Enhanced artificial bee colony with novel search strategy and dynamic parameter

Group‐guided artificial bee colony algorithm with elastic adjustment strategy

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