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A unified framework for population-based metaheuristics

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Abstract

Based on the analysis of the basic schemes of a variety of population-based metaheuristics (PBMH), the main components of PBMH are described with functional relationships in this paper and a unified framework (UF) is proposed for PBMH to provide a comprehensive way of viewing PBMH and to help understand the essential philosophy of PBMH from a systematic standpoint. The relevance of the proposed UF and some typical PBMH methods is illustrated, including particle swarm optimization, differential evolution, scatter search, ant colony optimization, genetic algorithm, evolutionary programming, and evolution strategies, which can be viewed as the instances of the UF. In addition, as a platform to develop the new population-based metaheuristics, the UF is further explained to provide some designing issues for effective/efficient PBMH algorithms. Subsequently, the UF is extended, namely UFmeme to describe the Memetic Algorithms (MAs) by adding local search (memetic component) to the framework as an extra-feature. Finally, we theoretically analyze the asymptotic convergence properties of PBMH described by the UF and MAs by the UFmeme.

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Authors and Affiliations

  1. Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Bo Liu & Shouyang Wang

  2. Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, 100084, China

    Bo Liu & Ling Wang

  3. School of Economics and Management, Beihang University, Beijing, 100191, China

    Bo Liu & Ying Liu

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  1. Bo Liu
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Liu, B., Wang, L., Liu, Y. et al. A unified framework for population-based metaheuristics. Ann Oper Res 186, 231–262 (2011). https://doi.org/10.1007/s10479-011-0894-3

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