A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation

Cui, Laizhong; Li, Genghui; Lin, Qiuzhen; Du, Zhihua; Gao, Weifeng; Lu, Nan

doi:10.1016/j.ins.2016.07.022

Cited by 118 publications

(63 citation statements)

References 45 publications

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“…The following nine typical unconstrained optimization problems [16]are used to test the performance of the proposed SABC algorithm by comparing among ABC algorithm, GABC algorithm [17]and SABC algorithm. The objective function of these test problems are shown in Table 1.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

The Artificial Bee Colony Algorithm Improved with Simplex Method

Xufang¹,

Liang²,

Wen³

2018

JSW

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Artificial bee colony (ABC) is one of very effective intelligence optimization algorithms, which has good performance in solving global optimization problems(GOPs). However, the ABC algorithm performs relatively poorly in complex GOPs for some weaknesses, such as slow convergence and poor exploitation. An improved artificial bee colony algorithm named SABC algorithm is proposed, which combines the ABC algorithm and the simplex method for GOPs. In SABC algorithm, the simplex method is employed to update the food sources which cannot be improved after limit updates in the ABC algorithm. The proposed SABC algorithm takes not only the advantages of directness, simpleness and small calculation amount of simplex method, but also the strong global search ability of ABC algorithm. The accurate and rapid local research ability of the simplex method can guarantee to avoid some blind meaningless iteration in the ABC algorithm. The validity has been verified by the numerical experiments on 9 benchmark GOPs. The numerical result show that the proposed SABC algorithm has faster convergence speed and better convergence accuracy compared to the basic ABC algorithm.

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Section: Experimental Results and Analysismentioning

confidence: 99%

The Artificial Bee Colony Algorithm Improved with Simplex Method

Xufang¹,

Liang²,

Wen³

2018

JSW

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“…Song et al [43] developed a novel search equation by using the information of objective function value, and the new search equation can efficiently adjust the step-size adaptively. Cui et al [28] proposed a depth-first search ABC with elite-guided search equation (DFSABC elite), by incorporating the information of elite solutions into solution search equations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The reason is that the search strategy used in ABC only updates one variable at a time, which results in slow evolution [27]. To address this issue, improvements to ABC algorithm have been introduced by, e.g., modifying search equations [28,29,30], hybridizing with other meta-heuristic algorithms [31,32,24] and employing multiple search strategies [33,34,35]. While the convergence of these ABC variants have been prominently increased, most of these improved ABC algorithms are still confined to updating one variable at a time.…”

Section: Introductionmentioning

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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“…As F05 and F19 are Rosenbrock and shifed Rosenbrock functions and their global optimum is inside a long, narrow, parabolic shaped flat valley, the variables are strongly dependent, and the gradients do not generally point towards the optimum. If the population is guided by the global best solution or some other good solutions, the search will fall into some unpromising areas [14]. Therefore, EABC-BB is beaten by MGABC at these two functions.…”

Section: Comparison With Other Abcsmentioning

confidence: 99%

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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A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation

Cited by 118 publications

References 45 publications

The Artificial Bee Colony Algorithm Improved with Simplex Method

The Artificial Bee Colony Algorithm Improved with Simplex Method

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

Enhanced artificial bee colony with novel search strategy and dynamic parameter

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