An improved artificial bee colony algorithm based on elite group guidance and combined breadth-depth search strategy

Kong, Depeng; Chang, Tianqing; Dai, Wenjun; Wang, Quandong; Sun, Huiyong

doi:10.1016/j.ins.2018.02.025

Cited by 49 publications

(21 citation statements)

References 46 publications

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“…As shown in formulas (20), (21) and (22), in the early stage of the algorithm, because the relative distance between individuals is large and the search step size is large, the algorithm focuses on exploration. As the iteration progresses, the relative distance between individual's decreases, the search step size also decreases, and the algorithm adaptively changes to the development stage.…”

Section: B Adaptive Step Size Strategymentioning

confidence: 99%

“…Recently, researchers combined the high-speed parallel computing ability of GPUs to improve the convergence speed of the ABC algorithm and achieved excellent results [18]- [20]. The artificial bee colony algorithm simulates the intelligent foraging behavior of a honeybee colony [21]. Owing to its outstanding global search capability, ABC has been successfully utilized in many applications, i.e., parameter estimation [22] and wireless sensor networks [23].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Squirrel Search Algorithm With Reproductive Behavior

et al. 2020

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The squirrel search algorithm (SSA) is a recently proposed nature-inspired algorithm based on the dynamic foraging and gliding behavior of squirrels. Because of its simplicity and stability, the squirrel algorithm has attracted increasing research interest. However, the lack of exploration ability of the SSA may lead to premature convergence to the local optimum. To overcome this disadvantage, an improved SSA with reproductive behavior (RSSA) is proposed to solve the numerical optimization problem. First, the reproductive behavior of the invasive weed algorithm (IWO) is introduced to the conventional SSA to generate offspring individuals, and these offspring individuals are scattered into the search space by Gaussian distribution to complete the location update. This method makes it possible for individuals with poor fitness to enter the next generation search, improving the exploration ability of the SSA. Second, an adaptive step strategy is proposed to adaptively adjust the search step of squirrels according to the distance between each squirrel and other family members. This strategy effectively balances the exploration and exploitation of the algorithm. Finally, the performance of the proposed RSSA algorithm is evaluated using Wilcoxon's test on unimodal, multimodal, fixed-dimensional multimodal and CEC 2014 benchmark functions. Experimental results and statistical tests show that RSSA has better performance in terms of convergence, accuracy, and search capability compared with other state-of-the-art algorithms. INDEX TERMS Nature-inspired, squirrel search algorithm, exploration and exploitation, Wilcoxon's test, CEC 2014 benchmark functions.

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Section: B Adaptive Step Size Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Squirrel Search Algorithm With Reproductive Behavior

et al. 2020

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“…EABC algorithm is an improved version of ABC algorithm and they are similar in many aspects. But in searching process, EABC algorithm will use the most elite source found so far to contribute in searching.…”

Section: Proposed Approachmentioning

confidence: 99%

Elite artificial bees' colony algorithm to solve robot's fuzzy constrained routing problem

Sori

Ebrahimnejad

Motameni

2019

Computational Intelligence

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One of the fundamental challenges of the robotics field is robot's movement. That is, why route planning is an eminent issue of robotics research and it is used to enhance autonomy of moving robots in complex environments. The objective of route planning problem is to find the shortest route without collide from initiation point to destination point so that the amount of energy consumption by robot would not exceed a predefined amount. Because neither the amount of energy consumption nor the robot's passed distance index cannot be measured precisely due to environmental conditions, and fuzzy data is used for modeling the problem and the problem would be called “Robot Fuzzy Constrained shortest Route” problem. The main contributions of this study are fivefold: (i) The mathematical model of fuzzy constrained shortest route problem (FCSRP) is formulated; (ii) An elite artificial bees' colony (EABC) algorithm is used to solve the robot's FSCRP; (iii) The proposed EABC algorithm is simulated with two fuzzy networks; (iv) The performance of the proposed approach is compared with the performance of genetic algorithm and particle swarm optimization algorithm; and (v) The results show the convergence speed of the EABC algorithm is higher than the existing algorithms.

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“…Note that the identification of fuzzy measure aiming to obtain more accurate solutions is a difficult point. 34 In this work, artificial bee colony (ABC) algorithm 35,36 is first introduced to identify the l-fuzzy measure and achieve superior results. Fuzzy integral mainly includes Sugeno fuzzy integral, Choquet fuzzy integral, and Zhenyuan fuzzy integral.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of equipment maintenance quality: A hybrid multi-criteria decision-making approach

Zhang

Shan

2019

Advances in Mechanical Engineering

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Maintenance plays a crucial role in the entire life cycle of equipment. With the acceleration of industrialization, the evaluation of equipment maintenance quality has undoubtedly become more challenging due to the complex mechanical structure, various maintenance modes, and so on. In order to make decisions scientifically, a hybrid multi-criteria decision-making approach integrating triangle fuzzy number, l-fuzzy measure, TOPSIS, and Choquet fuzzy integral is proposed in this article. First, the interaction among criteria can be handled reasonably by fuzzy integral based on l-fuzzymeasure. Second, fuzzy numbers which are given by experts are applied to deal with fuzzy linguistic value. In addition, artificial bee colony algorithm is first introduced to identify l-fuzzy-measure. The comparison results of three optimization algorithms which include artificial bee colony algorithm, genetic algorithm, and particle swarm optimization prove artificial bee colony algorithm is more effective than genetic algorithm and particle swarm optimization. A case study which contains six maintenance alternatives is practiced to prove the effectiveness of the proposed hybrid multi-criteria decision-making approach. Finally, the comparison is made between the proposed method and two classical multi-criteria decision-making approaches which refer to TOPSIS and gray correlation, and the results demonstrate the proposed method is suitable to solve maintenance quality evaluation problem.

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An improved artificial bee colony algorithm based on elite group guidance and combined breadth-depth search strategy

Cited by 49 publications

References 46 publications

An Improved Squirrel Search Algorithm With Reproductive Behavior

An Improved Squirrel Search Algorithm With Reproductive Behavior

Elite artificial bees' colony algorithm to solve robot's fuzzy constrained routing problem

Evaluation of equipment maintenance quality: A hybrid multi-criteria decision-making approach

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