Simulated annealing based artificial bee colony algorithm for global numerical optimization

Chen, Shiming; Sarosh, Ali; Dong, Yun-Feng

doi:10.1016/j.amc.2012.09.052

Cited by 62 publications

(23 citation statements)

References 27 publications

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“…then, a fine-tuning mechanism is introduced and different perturbation factors is discussed. The improved algorithm has better performance in search ability and accuracy [12].Huang Lingling analysed characteristics of ABC and differential evolution algorithm, and proposed a integrated the advantages hybrid algorithm [13].Chen etc introduced simulated annealing algorithm in the process of hire bees search, improved ABC algorithm's ability to develop solutions [14].Yuan Xiaoyan introduced chaotic operator and reverse learning operator in initialized stage, then, in mining bee search equation introduced the best guide in the individual, and improved the search mechanism of scout bees, making the convergence have improved significantly [15].…”

Section: Improvement Of Algorithmmentioning

confidence: 99%

An Overview of Artificial Bee Colony Algorithm

Yang¹,

Jiang²

2015

Proceedings of the 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics

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Abstract.A swarm intelligence algorithm develops rapidly, which has solved many large scale complex problems these years. Artificial bee colony algorithm is a new swarm intelligence algorithm and gets wide attention for its superior performance, such as, strong global convergence, greedy heuristic search feature and quickly problem solution. The biological background is introduced briefly; By comparing bees foraging behavior with problems solution, modeling thought is given; Algorithm model is introduced in detail. Then, research status quo is discussed from two aspects, improvement and application of the algorithm. Also, conclusions are given about artificial bee colony algorithm, and improvement direction and application field are put forward from the weakness analysis of algorithm.

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Section: Improvement Of Algorithmmentioning

confidence: 99%

An Overview of Artificial Bee Colony Algorithm

Yang¹,

Jiang²

2015

Proceedings of the 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics

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“…"−", "+", and "≈" denote that the performance of the TLBO algorithm is worse than, better than, and similar to that of SGO, respectively bee colony algorithm) [36], IABC [38], ABC/Best1 [39], SAABC(simulated annealing-based artificial bee colony) [40], VABC(velocity-based artificial bee colony algorithm)…”

Section: Experiments 7: Sgo Vs Saabc Gabc Iabc Abc/best1 Gpso Dbmentioning

confidence: 99%

Social group optimization (SGO): a new population evolutionary optimization technique

Satapathy

Naik

2016

Complex Intell. Syst.

160

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Social group optimization (SGO), a populationbased optimization technique is proposed in this paper. It is inspired from the concept of social behavior of human toward solving a complex problem. The concept and the mathematical formulation of SGO algorithm is explained in this paper with a flowchart. To judge the effectiveness of SGO, extensive experiments have been conducted on number of different unconstrained benchmark functions as well as standard numerical benchmark functions taken from the IEEE Congress on Evolutionary Computation 2005 competition. Performance comparisons are made between state-of-theart optimization techniques, like GA, PSO, DE, ABC and its variants, and the recently developed TLBO. The investigational outcomes show that the proposed social group optimization outperforms all the investigated optimization techniques in computational costs and also provides optimal solutions for most of the functions considered in our work. The proposed technique is found to be very simple and straightforward to implement as well. It is believed that SGO will supplement the group of effective and efficient optimization techniques in the population-based category and give researchers wide scope to choose this in their respective applications.

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“…Hybrid PSO optimization has also been widely applied (Shieh et al, 2011;Valdez et al, 2011;Fan and Zahara, 2007;Ahandania et al, 2012;Li et al, 2009;Wang et al, 2013) demonstrating faster convergence rates. Likewise, ACO was improved by incorporating a hybridization strategy (Chen et al, 2012;Koide et al, 2013). This paper describes a new hybrid algorithm applied to two objective functions, namely the embedded CO 2 emissions and the economic cost.…”

Section: Introductionmentioning

confidence: 99%