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A modified ABC algorithm based on improved-global-best-guided approach and adaptive-limit strategy for global optimization

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APPLIED SOFT COMPUTING 2016年 46卷 469-486页

作者： Zhong, Fuli Li, Hui Zhong, Shouming Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Math Sci Chengdu 611731 Peoples R China

Artificial bee colony (ABC) is a novel biological-inspired optimization algorithm which has been shown to be more effective for global optimization of multimodal and multidimensional optimization problems, than some other conventional biological-inspired optimization algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO), for its good exploration capability and the efficient balance between the local search and the global search processes. It has drawn widely attentions from scholars and was applied to various fields for its advantages of excellent global optimization ability and it is easy to implement. However, the basic ABC has some drawbacks such as poor exploitation, slow to converge and hard to find the best solution from all feasible solutions in some cases. In this paper, a modified ABC algorithm based on improved-global-best-guided approach and adaptive-limit strategy for global optimization problems called IGAL-ABC algorithm is proposed. An improved-global-best-guided term with a nonlinear adjusting factor is employed in the update equation. Two nonlinear adjusting factors are applied to control the convergence speed and balance the exploration and exploitation abilities. Multiple dimensions of solution are perturbed each time for generating new candidate food sources. In addition, an adaptive-limit strategy is applied to adjust the limit which controls the frequency that the employed bee abandons its food source, to improve the performance of the algorithm further. Results of experiments tested on multiple benchmark functions show that the proposed method is effective and has good performance. The comparison experiments illustrate that the proposed algorithm has better solution quality and convergence characteristics. (C) 2016 Elsevier B.V. All rights reserved.

关键词： biological-inspired optimization algorithm Swarm intelligence Artificial bee colony optimization

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An improved artificial bee colony algorithm with modified-neighborhood-based update operator and independent-inheriting-search strategy for global optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 58卷 134-156页

Artificial bee colony (ABC) is a novel swarm intelligence optimization algorithm that has been shown to be effective in solving high dimensional global optimization problem with good performance for its excellent exploration capability. It has received a great deal of attentions of researchers since it was proposed, and was employed to many application fields for its advantages of excellent global optimization ability and easy to implement. However, the basic ABC has some drawbacks like poor exploitation and slow convergence. In this paper, an improved artificial bee colony algorithm based on modified-neighborhood-based update operator and independent-inheriting-search strategy for global optimization called MNIIABC algorithm is proposed. In the proposed algorithm, a modified-neighborhood-based update operator, which contains a global-best term and a subset-best guided term, is applied in the employed bee stage to balance the exploration and exploitation. Aiming to improve the solution diversity, a subset partition method for producing perturbation term is considered. In order to enhance the exploitation of the algorithm, an independent-inheriting-search strategy is used in the onlooker stage. Experiment results tested on multiple benchmark functions show that the proposed method is effective, and has good performance. The comparison experimental results illustrate that the proposed algorithm has good solution quality and convergence characteristics.

关键词： biological-inspired optimization algorithm Swarm intelligence Artificial bee colony optimization

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Gbest-guided artificial bee colony algorithm for numerical function optimization

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APPLIED MATHEMATICS AND COMPUTATION 2010年第7期217卷 3166-3173页

作者： Zhu, Guopu Kwong, Sam City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

Artificial bee colony (ABC) algorithm invented recently by Karaboga is a biological-inspired optimization algorithm, which has been shown to be competitive with some conventional biological-inspired algorithms, such as genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO). However, there is still an insufficiency in ABC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. inspired by PSO, we propose an improved ABC algorithm called gbest-guided ABC (GABC) algorithm by incorporating the information of global best (gbest) solution into the solution search equation to improve the exploitation. The experimental results tested on a set of numerical benchmark functions show that GABC algorithm can outperform ABC algorithm in most of the experiments. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Artificial bee colony algorithm Genetic algorithm Particle swarm optimization Differential evolution biological-inspired optimization algorithm Numerical function optimization

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Linear Weighted Gbest-guided Artificial Bee Colony algorithm

Linear Weighted Gbest-guided Artificial Bee Colony Algorithm

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5th International Symposium on Computational Intelligence and Design (ISCID)

作者： Zhang, Yanyu Zeng, Peng Wang, Yang Zhu, Baohui Kuang, Fangjun Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China

ISBN: (纸本)9780769548111

Artificial bee colony (ABC) algorithm invented recently by Karaboga is a competitive stochastic population-based optimization algorithm. However, solution search equation used in the original ABC algorithm is good at exploration but poor at exploitation. An improved ABC algorithm called Gbest-guided ABC (GABC) was introduced by researchers to improve the exploitation of ABC algorithm. In order to improve the GABC algorithm further, we propose an improved GABC algorithm with a linear weight called WGABC, and introduce a novel solution search equation used at scout bee stage of WGABC algorithm. Experimental results tested on a set of numerical benchmark functions show that WGABC can outperform ABC and GABC algorithms in most of the experiments.

关键词： Artificial bee colony algorithm biological-inspired optimization algorithm Numerical function optimization Swarm intelligence

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Linear Weighted Gbest-guided Artificial Bee Colony algorithm

Linear Weighted Gbest-guided Artificial Bee Colony Algorithm

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2012 Fifth International Symposium on Computational Intelligence and Design [第五届计算智能与设计国际会议 ISCID 2012]

作者： Yanyu Zhang Peng Zeng Yang Wang Fangjun Kuang Baohui Zhu Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Computer and Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Graduate School of Chi

Artificial bee colony (ABC) algorithm invented recently by Karaboga is a competitive stochastic population-based optimization algorithm. However, solution search equation used in the original ABC algorithm is good at exploration but poor at exploitation. An improved ABC algorithm called Gbestguided ABC (GABC) was introduced by researchers to improve the exploitation of ABC algorithm. In order to improve the GABC algorithm further, we propose an improved GABC algorithm with a linear weight called WGABC, and introduce a novel solution search equation used at scout bee stage of WGABC algorithm. Experimental results tested on a set of numerical benchmark functions show that WGABC can outperform ABC and GABC algorithms in most of the experiments.

关键词： Artificial bee colony algorithm biological-inspired optimization algorithm Numerical function optimization Swarm intelligence

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