A unified framework for population-based metaheuristics

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

An overview of population-based algorithms for multi-objective optimisation

“…Another somewhat not fully developed aspect is notation, especially in regard to Pareto dominance relations, where there is incoherence in this field. Finally a unified mathematical framework seems possible and its development would be highly beneficial; some steps toward this direction can are seen in Laumanns et al (2000), Liu et al (2011).…”

“…Other recent works that review population-based optimization methods are due to Liu et al (2011) and Zhou et al (2011). Although the work of Liu et al (2011) is not a survey, the authors present an interesting approach in unifying concepts employed in PBOTs, and as such contains useful reference material about previous attempts.…”

“…Although the work of Liu et al (2011) is not a survey, the authors present an interesting approach in unifying concepts employed in PBOTs, and as such contains useful reference material about previous attempts. Zhou et al (2011) present a more thematic overview of MOEAs and also provide an extensive list of applications of EAs in realworld problems.…”

“…The purpose of this work is to provide a general overview of the most prominent populationbased multi-objective optimization methodologies, serving as a map or a starting point in the search for a suitable technique, thus serving as a complement to existing works (Zlochin et al 2004, Dorigo and Blum 2005, Jin and Branke 2005, Reyes-Sierra and Coello 2006, Tapia and Coello 2007, AlRashidi and El-Hawary 2009, Das and Suganthan 2010, Liu et al 2011, Zhou et al 2011. As in cartography, maps are meant to contain important landmarks but in order to enhance legibility a vast amount of detail has been abstracted or omitted.…”

Adjustment of basal insulin infusion rate in T1DM by hybrid PSO

“…PSO has the advantages of fast convergence, easily realizing, and only having a few parameters to adjust. Besides, it also has a profound intelligence background which makes it suitable for both scientific research and engineering applications, such as neural network, fuzzy control system, scheduling, etc (Clerc and Kennedy 2002;Liu et al 2005Liu et al , 2011Rana et al 2011;Zhan et al 2009). However, standard PSO algorithm can easily get trapped in the local optima when solving complex multimodal problems, and hence various attempts have been made to improve the performance of standard version (Deng et al 2012;Li et al 2012;Liu et al 2010;Xu et al 2013;Zhan et al 2009).…”