A particle swarm optimization-threshold accepting hybrid algorithm for unconstrained optimization

Title:

A particle swarm optimization-threshold accepting hybrid algorithm for unconstrained optimization

Creator:

Maheshkumar , Y., Ravi, V., and Abraham , Ajith

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:c95b79cd-234b-4719-ae67-66676a95b53a
uuid:c95b79cd-234b-4719-ae67-66676a95b53a
doi:10.14311/NNW.2013.23.013

Subject:

Particle swatmk optimization, threshold accepting algorithm, hybrid metaheuristics, and unconstrained optimization

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this paper, we propose a novel hybrid metaheuristic algorithm, which integrates a Threshold Accepting algorithm (TA) with a traditional Particle Swarm Optimization (PSO) algorithm. We used the TA as a catalyst in speeding up convergence of PSO towards the optimal solution. In this hybrid, at the end of every iteration of PSO, the TA is invoked probabilistically to refine the worst particle that lags in the race of finding the solution for that iteration. Consequently the worst particle will be refined in the next iteration. The robustness of the proposed approach has been tested on 34 unconstrained optimization problems taken from the literature. The proposed hybrid demonstrates superior preference in terms of functional evaluations and success rate for 30 simulations conducted.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2013 Volume:23 | Number:3

Harvested from:

CDK

Metadata only:

false