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A brick-up model for recombining metaheuristic optimisation algorithm using analytic hierarchy process

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Abstract

Most swarm intelligence algorithms are stochastic metaheuristic algorithms in nature, and thus they may not solve all optimisation problems perfectly. Different algorithms may have different advantages, and the different real cases should be analysed independently. In this paper, a new brick-up re- building method for metaheuristic algorithms is proposed and discussed. This brick-up method creatively separates the metaheuristic algorithms into components (bricks) and generate a brick pool for further use. Then a new and best fitting algorithm will be generated custom-made to different problem and suggested to user as the best solution available in metaheuristic design. The main contributions for this research are the metaheuristic brick selection rules analysis and brick-up system model simulation. The proposed model has been tested on CEC 2015 benchmark function sets to verify its performance. The experimental results show that this recombination model can produce a metaheuristic algorithm that is as efficient as each individual candidate algorithm or better.

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Acknowledgements

The authors are thankful for the financial support from the research grants, MYRG2016-00069, entitled ’Nature-Inspired Computing and Metaheuristics Algorithms for Optimizing Data stream mining Performance’, EF003/FST-FSJ/2019/GSTIC, code no. 201907010001, FDCT/126/2014/A3, entitled ’A Scalable Data Stream Mining Methodology: Stream-based Holistic Analytics and Reasoning in Parallel’ offered by FDCT and RDAO/FST, the University of Macau and the Macau SAR government. We are thankful for the technical contribution by Mr. Shuang Liu who assisted in running the experiments.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology, College of Artificial Intelligence, Chongqing Technology and Business University, Chongqing, China

    Qun Song

  2. Department of Computer and Information Science, University of Macau, Macau, SAR, China

    Tengyue Li, Simon Fong & Shuang Liu

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  1. Qun Song
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  2. Tengyue Li
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  3. Simon Fong
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  4. Shuang Liu
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Correspondence to Qun Song.

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Song, Q., Li, T., Fong, S. et al. A brick-up model for recombining metaheuristic optimisation algorithm using analytic hierarchy process. Appl Intell 53, 3166–3182 (2023). https://doi.org/10.1007/s10489-022-03586-1

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