Highly Convergent Particle Swarm Optimization
نویسنده
چکیده
Abstract— Particle Swarm Optimization (PSO) algorithm is swarm intelligence based algorithm which is used for solving optimization problem. PSO simulates the intelligent foraging behavior of a flock of birds. This paper presents a modification in PSO and develops an algorithm called Highly Convergent Particle Swarm Optimization (HCPSO) algorithm, in which the velocity of particle is made to be dependent only on the global best position. HCPSO improves the exploitation capability of PSO algorithm. The modification of the algorithm is presented and a set of ten complex benchmark functions is utilized for performing the experiments for checking its validity. Results show that the proposed HCPSO outperforms the PSO algorithm on most functions.
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