TY - JOUR
T1 - A unified framework for population-based metaheuristics
AU - Liu, Bo
AU - Wang, Ling
AU - Liu, Ying
AU - Wang, Shouyang
PY - 2011/6
Y1 - 2011/6
N2 - 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.
AB - 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.
KW - Convergence analysis
KW - Evolutionary computing
KW - Memetic Algorithms
KW - Metaheuristics
KW - Population-based metaheuristics
KW - Unified framework
UR - https://www.scopus.com/pages/publications/79958854038
U2 - 10.1007/s10479-011-0894-3
DO - 10.1007/s10479-011-0894-3
M3 - 文章
AN - SCOPUS:79958854038
SN - 0254-5330
VL - 186
SP - 231
EP - 262
JO - Annals of Operations Research
JF - Annals of Operations Research
IS - 1
ER -