%0 Journal Article %1 Taherkhani2016281 %A Taherkhani, Mojtaba %A Safabakhsh, Reza %D 2016 %J Applied Soft Computing %K (PSO) Adaptive Particle Radar Stability analysis design inertia optimization swarm system weight %P 281 - 295 %R http://dx.doi.org/10.1016/j.asoc.2015.10.004 %T A novel stability-based adaptive inertia weight for particle swarm optimization %U http://www.sciencedirect.com/science/article/pii/S1568494615006195 %V 38 %X Abstract Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by intelligent collective behavior of birds. The performance of the \PSO\ algorithm highly depends on choosing appropriate parameters. Inertia weight is a parameter of this algorithm which was first proposed by Shi and Eberhart to bring about a balance between the exploration and exploitation characteristics of PSO. This paper presents an adaptive approach which determines the inertia weight in different dimensions for each particle, based on its performance and distance from its best position. Each particle will then have different roles in different dimensions of the search environment. By considering the stability condition and an adaptive inertia weight, the acceleration parameters of \PSO\ are adaptively determined. The corresponding approach is called stability-based adaptive inertia weight (SAIW). The proposed method and some other models for adjusting the inertia weight are evaluated and compared. The efficiency of \SAIW\ is validated on 22 static test problems, moving peaks benchmarks (MPB) and a real-world problem for a radar system design. Experimental results indicate that the proposed model greatly improves the \PSO\ performance in terms of the solution quality as well as convergence speed in static and dynamic environments.

@article{Taherkhani2016281, abstract = {Abstract Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by intelligent collective behavior of birds. The performance of the \{PSO\} algorithm highly depends on choosing appropriate parameters. Inertia weight is a parameter of this algorithm which was first proposed by Shi and Eberhart to bring about a balance between the exploration and exploitation characteristics of PSO. This paper presents an adaptive approach which determines the inertia weight in different dimensions for each particle, based on its performance and distance from its best position. Each particle will then have different roles in different dimensions of the search environment. By considering the stability condition and an adaptive inertia weight, the acceleration parameters of \{PSO\} are adaptively determined. The corresponding approach is called stability-based adaptive inertia weight (SAIW). The proposed method and some other models for adjusting the inertia weight are evaluated and compared. The efficiency of \{SAIW\} is validated on 22 static test problems, moving peaks benchmarks (MPB) and a real-world problem for a radar system design. Experimental results indicate that the proposed model greatly improves the \{PSO\} performance in terms of the solution quality as well as convergence speed in static and dynamic environments. }, added-at = {2015-11-23T13:20:43.000+0100}, author = {Taherkhani, Mojtaba and Safabakhsh, Reza}, biburl = {https://www.bibsonomy.org/bibtex/24c2496bf61f412b65fdbbfef57c2aefa/mojtaba-taherkh}, doi = {http://dx.doi.org/10.1016/j.asoc.2015.10.004}, interhash = {d63a484bcbaab86ddd822699d05729ee}, intrahash = {4c2496bf61f412b65fdbbfef57c2aefa}, issn = {1568-4946}, journal = {Applied Soft Computing }, keywords = {(PSO) Adaptive Particle Radar Stability analysis design inertia optimization swarm system weight}, pages = {281 - 295}, timestamp = {2015-11-23T13:20:43.000+0100}, title = {A novel stability-based adaptive inertia weight for particle swarm optimization }, url = {http://www.sciencedirect.com/science/article/pii/S1568494615006195}, volume = 38, year = 2016 }