Improved artificial bee colony algorithm with mutual learning

Liu, Yu; Ling, Xiaoxi; Yu, Liang; Liu, Guanghao

doi:10.1109/jsee.2012.00034

Cited by 35 publications

(10 citation statements)

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“…(6) where the definition of the weight value W parameters is the same as in the PSO proposed in Reference [21]. In terms of learning factors, cognition learning factor (C1) is regarded as each individual bee, which, combined with ABC [22], is modified using Equation (7). During the tracking period, particles constantly calculate if a new solution is superior to the existing one.…”

Section: Combining Artificial Bee Colony Algorithmmentioning

confidence: 99%

Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm

Chao

Hsieh

2019

Electronics

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In this study, the output characteristics of partial modules in a photovoltaic module array when subject to shading were first explored. Then, an improved particle swarm optimization (PSO) algorithm was applied to track the global maximum power point (MPP), with a multi-peak characteristic curve. The improved particle swarm optimization algorithm proposed, combined with the artificial bee colony (ABC) algorithm, was used to adjust the weighting, cognition learning factor, and social learning factor, and change the number of iterations to enhance the tracking performance of the MPP tracker. Finally, MATLAB software was used to carry out a simulation and prove the improved that the PSO algorithm successfully tracked the MPP in the photovoltaic array output curve with multiple peaks. Its tracking performance is far superior to the existing PSO algorithm.

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Section: Combining Artificial Bee Colony Algorithmmentioning

confidence: 99%

Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm

Chao

Hsieh

2019

Electronics

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“…The literature [8] proposed a new Energy-efficient Distributed Clustering Algorithm (BPEC) by taking the ratio of average residual energy of the neighboring nodes to the residual energy of the node itself as the main parameter to compete for the cluster head and the "degree" of node as the aided parameter for the cluster head competition to achieve the balanced load and prolong the network life cycle. The literature [9] transformed the clusters as the cluster partition with the approximate optimization goal as well as the cluster head selection issue and then adopted a heuristic clustering control algorithm. This paper put forward an efficient and reliable routing algorithm on wireless sensor network based on the quantum artificial bee colony algorithm by summarizing the research results of the above researchers and combing the emerging swarm intelligence algorithm to enhance the network clustering efficiency and improve the network efficiency and reliability.…”

Section: The Related Workmentioning

confidence: 99%

An Efficient and Reliable Routing Protocol for WSNs Based on Quantum Artificial Bee Colony Algorithm

Wang¹,

Dayu²

2017

IJCA

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“…To improve search precision, a modified ABC algorithm for solving constrained numerical optimization problems was presented in [18]. Using mutual learning, an improved ABC algorithm that adjusts population diversity, and has higher convergence speed was proposed in [19]. By combining a pattern search rule, a hybridized ABC algorithm for global optimization was presented in [20].…”

Section: Introductionmentioning

confidence: 99%

Artificial Bee Colony Algorithm Combined With Previous Successful Search Experience

2019

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The neighboring solutions-based technique employed by the artificial bee colony algorithm (ABC) is good at exploration but poor at exploitation. The main reason is the blindness of search behavior which leads to the employed bees not generating promising candidate solutions. To address this issue, we propose an improved ABC algorithm (ABCPSE) combined with the previous successful search experience. The proposed algorithm has the following innovative advantages: First, the previous successful search experience with good performances in convergence and distribution are employed in real time to generate offspring. This rule can increase convergence speed and exploitation ability of ABCPSE algorithm. Second, local search is performed near the superior individuals produced in a different generation. Hence, a set of solutions with excellent diversity and convergence is obtained. To assess the performance of ABCPSE algorithm, experiments are conducted on a set of 18 benchmark functions. The results demonstrate that the proposed algorithm can produce higher quality solutions with faster convergence than some current stateof-the-art ABC-based algorithms.

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Improved artificial bee colony algorithm with mutual learning

Cited by 35 publications

References 0 publications

Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm

Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm

An Efficient and Reliable Routing Protocol for WSNs Based on Quantum Artificial Bee Colony Algorithm

Artificial Bee Colony Algorithm Combined With Previous Successful Search Experience

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