Artificial bee colony algorithm with multiple search strategies

Gao, Weifeng; Huang, Lingling; Liu, Sanyang; Chan, Felix T. S.; Dai, Cai; Shan, Xian

doi:10.1016/j.amc.2015.09.019

Cited by 45 publications

(24 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address the advantage of ABCVNS, it is compared with ABC at the beginning. As far as the two contenders are concerned, the other parameters of each algorithm are given below:ABC: the population size is 40, namely, N p = 20 [18], and the control parameter limit is set to N p ∗ D [8, 21]ABCVNS: the population size is 40, namely, N p = 20 [18], and the control parameter limit is also set to N p ∗ D [8, 21]…”

Section: Experimental Study and Discussionmentioning

confidence: 99%

“…To simultaneously get better accuracies of solutions and a faster convergence performance, Gao et al [18] and Kiran et al [21] have proposed multiple search strategies to coordinate the exploitation and the exploration abilities of ABC from various perspectives, respectively. In addition, different neighbor search operators are also employed in other methods [34].…”

Section: A Multistrategy Artificial Bee Colony Algorithmmentioning

confidence: 99%

“…Frequently, the ABC was hybridized with other local approaches [13] to enhance its search ability. More recently, many researchers would like to utilize hybridization of multiple search strategies to balance the exploration and exploitation abilities of ABC [18, 21]. For instance, Gao et al [18] first constructed a strategy candidate pool composed of three search strategies with different search abilities and then proposed an adaptive selection mechanism to select a search strategy for each individual based on previous search experiences.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, many researchers would like to utilize hybridization of multiple search strategies to balance the exploration and exploitation abilities of ABC [18, 21]. For instance, Gao et al [18] first constructed a strategy candidate pool composed of three search strategies with different search abilities and then proposed an adaptive selection mechanism to select a search strategy for each individual based on previous search experiences. Moreover, the proposed algorithm, namely, MuABC, had achieved a better performance when compared with the standard ABC and other state-of-the-art algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Multistrategy Artificial Bee Colony Algorithm Enlightened by Variable Neighborhood Search

Xiang

et al. 2019

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Artificial bee colony (ABC) has a good exploration ability against its exploitation ability. For enhancing its comprehensive performance, we proposed a multistrategy artificial bee colony (ABCVNS for short) based on the variable neighborhood search method. First, a search strategy candidate pool composed of two search strategies, i.e., ABC/best/1 and ABC/rand/1, is proposed and employed in the employed bee phase and onlooker bee phase. Second, we present another search strategy candidate pool which consists of the original random search strategy and the opposition-based learning method. Then, it is used to further balance the exploration and exploitation abilities in the scout bee phase. Last but not least, motivated by the scheme of neighborhood change of variable neighborhood search, a simple yet efficient choice mechanism of search strategies is presented. Subsequently, the effectiveness of ABCVNS is carried out on two test suites composed of fifty-eight problems. Furthermore, comparisons among ABCVNS and several famous methods are also carried out. The related experimental results clearly demonstrate the effectiveness and the superiority of ABCVNS.

show abstract

Section: Experimental Study and Discussionmentioning

confidence: 99%

Section: A Multistrategy Artificial Bee Colony Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Multistrategy Artificial Bee Colony Algorithm Enlightened by Variable Neighborhood Search

Xiang

et al. 2019

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Wang et al [34] proposed a multi-strategy ensemble ABC (MEABC), in which a pool of strategies is constructed by three distinct search strategies to compete to produce offspring. Gao et al [49] developed an ABC with multiple search strategies (MuABC) . In MuABC, employed bee and onlooker bee each generate three candidate solutions by using three different search strategies, and the best solution is selected to compete with the old solution.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Tianfield

2019

Swarm and Evolutionary Computation

View full text Add to dashboard Cite

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.

show abstract

Multipopulation artificial bee colony algorithm based on a modified probability selection model

Wang

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

Artificial bee colony (ABC) performs excellently over many problems, but it has some shortcomings, such as weak exploitation as well as slow convergence. For the sake of dealing with these issues, a modified ABC known as MPABC is presented. Firstly, the entire population is partitioned into two different subpopulations at the stage of employed bees, and they use different search strategies. Then, a new probability selection strategy is designed on the basis of the principle of Soft Maximum function. Finally, a novel search method is constructed for improving the intensity of exploitation by gradually increasing the ratio of the current optimal solutions. In order to comprehensively validate the capability of MPABC, 12 benchmark problems are employed. Computational results clearly demonstrate MPABC surpasses the basic ABC and some other famous ABCs.

show abstract

Artificial bee colony algorithm with multiple search strategies

Cited by 45 publications

References 38 publications

A Multistrategy Artificial Bee Colony Algorithm Enlightened by Variable Neighborhood Search

A Multistrategy Artificial Bee Colony Algorithm Enlightened by Variable Neighborhood Search

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

Multipopulation artificial bee colony algorithm based on a modified probability selection model

Contact Info

Product

Resources

About