A framework for multi-objective optimisation based on a new self-adaptive particle swarm optimisation algorithm

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dc.contributor.authorTang, Biwei
dc.contributor.authorZhu, Zhanxia
dc.contributor.authorShin, Hyo-Sang
dc.contributor.authorTsourdos, Antonios
dc.contributor.authorLuo, Jianjun
dc.date.accessioned2017-10-10T13:38:29Z
dc.date.available2017-10-10T13:38:29Z
dc.date.issued2017-08-24
dc.description.abstractThis paper develops a particle swarm optimisation (PSO) based framework for multi-objective optimisation (MOO). As a part of development, a new PSO method, named self-adaptive PSO (SAPSO), is first proposed. Since the convergence of SAPSO determines the quality of the obtained Pareto front, this paper analytically investigates the convergence of SAPSO and provides a parameter selection principle that guarantees the convergence. Leveraging the proposed SAPSO, this paper then designs a SAPSO-based MOO framework, named SAMOPSO. To gain a well-distributed Pareto front, we also design an external repository that keeps the non-dominated solutions. Next, a circular sorting method, which is integrated with the elitist-preserving approach, is designed to update the external repository in the developed MOO framework. The performance of the SAMOPSO framework is validated through 12 benchmark test functions and a real-word MOO problem. For rigorous validation, the performance of the proposed framework is compared with those of four well-known MOO algorithms. The simulation results confirm that the proposed SAMOPSO outperforms its contenders with respect to the quality of the Pareto front over the majority of the studied cases. The non-parametric comparison results reveal that the proposed method is significantly better than the four algorithms compared at the confidence level of 90% over the 12 test functions.en_UK
dc.identifier.citationTang B, Zhua Z, Shin H-S, Tsourdos A, Luo J, A framework for multi-objective optimisation based on a new self-adaptive particle swarm optimisation algorithm, Information Services, Vol. 420, December 2017, pp. 364-385en_UK
dc.identifier.cris18348389
dc.identifier.issn0020-0255
dc.identifier.urihttp://dx.doi.org/10.1016/j.ins.2017.08.076
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/12608
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMulti-objective optimisationen_UK
dc.subjectSelf-adaptive particle swarm optimisationen_UK
dc.subjectConvergence of particle swarm optimisationen_UK
dc.subjectCircular sorting methoden_UK
dc.titleA framework for multi-objective optimisation based on a new self-adaptive particle swarm optimisation algorithmen_UK
dc.typeArticleen_UK

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