An artificial bee colony algorithm search guided by scale-free networks

Ji, Junkai; Song, Shuangbao; Tang, Cheng; Gao, Shangce; Tang, Zheng; Todo, Yuki

doi:10.1016/j.ins.2018.09.034

Cited by 56 publications

(21 citation statements)

References 30 publications

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“…The results from different papers included and presented in this paper in order to compare WOA-BAT with other well-known evolutionary algorithms, for example, GA, DE, ABC, and BSO. The results of these algorithms are obtained from CEC2005, which includes 25 benchmark functions [59–61]. The results for CEC2005 as shown in Table 12 indicate that WOA-BAT has the first rank because it outperforms well in 13 functions.…”

Section: Implementation and Resultsmentioning

confidence: 99%

A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm

Mohammed

Umar

Rashid

2019

Computational Intelligence and Neuroscience

178

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The whale optimization algorithm (WOA) is a nature-inspired metaheuristic optimization algorithm, which was proposed by Mirjalili and Lewis in 2016. This algorithm has shown its ability to solve many problems. Comprehensive surveys have been conducted about some other nature-inspired algorithms, such as ABC and PSO. Nonetheless, no survey search work has been conducted on WOA. Therefore, in this paper, a systematic and meta-analysis survey of WOA is conducted to help researchers to use it in different areas or hybridize it with other common algorithms. Thus, WOA is presented in depth in terms of algorithmic backgrounds, its characteristics, limitations, modifications, hybridizations, and applications. Next, WOA performances are presented to solve different problems. Then, the statistical results of WOA modifications and hybridizations are established and compared with the most common optimization algorithms and WOA. The survey’s results indicate that WOA performs better than other common algorithms in terms of convergence speed and balancing between exploration and exploitation. WOA modifications and hybridizations also perform well compared to WOA. In addition, our investigation paves a way to present a new technique by hybridizing both WOA and BAT algorithms. The BAT algorithm is used for the exploration phase, whereas the WOA algorithm is used for the exploitation phase. Finally, statistical results obtained from WOA-BAT are very competitive and better than WOA in 16 benchmarks functions. WOA-BAT also outperforms well in 13 functions from CEC2005 and 7 functions from CEC2019.

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Section: Implementation and Resultsmentioning

confidence: 99%

A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm

Mohammed

Umar

Rashid

2019

Computational Intelligence and Neuroscience

178

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“…As we mentioned that the proposed MDBSO is superior than BSO in solving such kinds of problems [53]. Using ABC variants as contrasts, including GABC [54], SeABC [55], MABC [56], RABC [57] and SFABC [58], is a very suitable conduct to prove the promising search ability of MDBSO. Tables 6 and 7 exhibit the results obtained by MDBSO and ABC variants.…”

Section: ) Mdbso Vs Bso Variantsmentioning

confidence: 99%

A Multiple Diversity-Driven Brain Storm Optimization Algorithm With Adaptive Parameters

Gao

Wang

et al. 2019

IEEE Access

Self Cite

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Brain storm optimization (BSO) is a swarm intelligence optimization algorithm which is proven to have practical values in various fields. During these years, many modifications have been facilitated to effectively improve BSO's search performance. So far, these modifications focus on improving the solution quality by applying different clustering methods and learning strategies, in which the population diversity is often neglected. However, in recent studies, population diversity plays a more significant role in designing optimization algorithm. A population that maintains its diversity in a high level can easily obtain better solutions than the one with low level of diversity. Therefore, this paper proposes a control method that evaluates the population diversity of BSO to improve its performance. Two diversity measures, which are known as distance-based diversity and fitness-based diversity, are implemented to realize the adaptation of algorithm parameters. The new algorithm is called multiple diversity-driven BSO (MDBSO). Its performance is verified by CEC2017 benchmark function suit and a neuron model training task. The results demonstrate the effectiveness and efficiency of MDBSO.

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“…Experimental results show those learning strategies have excellent performance in convergence speed. Ji et al [33] proposed a scale-free ABC (SFABC). Under the guidance of a scalefree network, poor food sources can obtain more information from neighbors' good sources, and exchange information in good food sources.…”

Section: B Other Abc Variantsmentioning

confidence: 99%

A New Artificial Bee Colony Based on Multiple Search Strategies and Dimension Selection

Xiao

Wang

et al. 2019

IEEE Access

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Artificial bee colony (ABC) algorithm is a popular optimization technique with strong search ability. Although ABC has the ability to handle complex optimization problems, it suffers from weak exploitation and slow convergence. In order to tackle this issue, a new ABC variant based on multiple search strategies and dimension selection (ABC-MSDS) is proposed in this paper. Firstly, multiple search strategies based on dual strategy pool are designed. Compared to other existing ABC with multiple search strategies, our approach constructs two strategy pools for employed and onlooker bees, respectively. Secondly, a new dimension selection method is used to replace the random dimension selection in the standard ABC. In the search process, each dimension is chosen one by one in terms of the quality of offspring. Finally, a modified scout bee phase is employed to accelerate the search. Experimental study is conducted on classical benchmark problems and CEC 2013 shifted and rotated problems. The performance of ABC-MSDS is compared with several recently published ABC variants. Computational results demonstrate the effectiveness of our approach. INDEX TERMS Artificial bee colony (ABC), multiple search strategies, strategy pool, dimensional selection, oppositionbased learning.

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An artificial bee colony algorithm search guided by scale-free networks

Cited by 56 publications

References 30 publications

A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm

A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm

A Multiple Diversity-Driven Brain Storm Optimization Algorithm With Adaptive Parameters

A New Artificial Bee Colony Based on Multiple Search Strategies and Dimension Selection

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