A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization

Xiang, Yi; Peng, Yonggang; Zhong, Yong; Chen, Zhenyu; Lu, Xuwen; Zhong, Xue-jun

doi:10.1007/s10589-013-9591-2

Cited by 54 publications

(28 citation statements)

References 30 publications

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“…Otherwise, the original food source is kept in the memory. If X i can not be improved, then its counter holding the number of trials will increase by 1, otherwise, the counter is reset to 0 [41].…”

Section: The Artificial Bee Colony Algorithmmentioning

confidence: 99%

A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization

Xiang

2015

Applied Soft Computing

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Section: The Artificial Bee Colony Algorithmmentioning

confidence: 99%

A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization

Xiang

2015

Applied Soft Computing

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“…The limit value is 200, and this paper draws on the proposed improved algorithm of ABC in recent years. The parameter limit is set as 200 for most of the algorithms {such as PS-MEABC (Xiang et al 2014a), EABC (Gao et al 2014a), I-ABC, and I-ABC greedy (Sharma and Pant 2013)}. A comparison between different algorithms is convenient because of the similar limit.…”

Section: Benchmark Functions and Simulation Environmentmentioning

confidence: 99%

“…Other experimental parameters include average value (mean), best value (best), worst value (worst), standard deviation (SD), and average of convergence function evaluations (expected running time, ERT). The ERT (Xiang et al 2014a) covers all trials on each dimension, which indicates the average convergence speed of the algorithm. The ERT is calculated by repeating a trial 30 times under the condition that error of convergence accuracy is less than threshold values err (1.0e−6).…”

Section: Functionmentioning

confidence: 99%

“…• PS-MEABC (Xiang et al 2014a). N P = D, S N = D/2, limit = 200 • MEABC (Wang et al 2014): N P = 100, S N = 50, limit = 100, C = 1.5 • EABC (Gao et al 2014a Tables 13 and 14, SABC clearly works better in almost all of the cases and achieves better performance than state-of-the-art ABC algorithms.…”

Section: Comparison Against Popular Abc Algorithmsmentioning

confidence: 99%

“…A particle swarm-inspired multi-elitist ABC algorithm (PS-MEABC) (Xiang et al 2014a) employs the global best solution and multi-elitist preservation strategy based on the ABC algorithm to guide the modification of food sources. A novel multi-strategy ensemble ABC algorithm (MEABC) (Wang et al 2014), which utilizes different characteristics of the solution search strategies, is appropriate for balancing exploration and exploitation during different stages of the search process.…”

Section: Introductionmentioning

confidence: 99%

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An improved artificial bee colony algorithm based on the strategy of global reconnaissance

Sun

et al. 2015

Soft Comput

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The artificial bee colony (ABC) algorithm is a recently introduced swarm intelligence optimization algorithm based on the foraging behavior of a honeybee colony. However, many problems are encountered in the ABC algorithm, such as premature convergence and low solution precision. Moreover, it can easily become stuck at local optima. The scout bees start to search for food sources randomly and then they share nectar information with other bees. Thus, this paper proposes a global reconnaissance foraging swarm optimization algorithm that mimics the intelligent foraging behavior of scouts in nature. First, under the new scouting search strategies, the scouts conduct global reconnaissance around the assigned subspace, which is effective to avoid premature convergence and local optima. Second, the scouts guide other bees to search in the neighborhood by applying heuristic information about global reconnaissance. The cooperation between the honeybees will contribute to the improvement of optimization performance and solution precision. Finally, the prediction and selection mechanism is adopted to further modify the search strategies of the employed bees and onlookers. Therefore, the search performance in the neighborhood of the local optimal solution is enhanced. The experimental results conducted on 52 typical test functions show that the proposed algorithm is more effective in avoiding premature convergence and Communicated by improving solution precision compared with some other ABCs and several state-of-the-art algorithms. Moreover, this algorithm is suitable for optimizing high-dimensional space optimization problems, with very satisfactory outcomes.

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Particle Swarm Optimization

Rana

Sarwar

Siddiqui

et al. 2023

Optimization Techniques in Engineering

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A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization

Cited by 54 publications

References 30 publications

A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization

A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization

An improved artificial bee colony algorithm based on the strategy of global reconnaissance

Particle Swarm Optimization

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