Pso Paralellization
Noname manuscript No. (will be inserted by the editor)
PSO Algorithm for the Traveling Salesman and Other Optimization Problems.
An OpenMp Implementation
Santiago Garcia Carbajal · Fiona Reid · David Corne
Received: date / Accepted: date
Abstract Particle Swarm Optimization is a general purpose optimization method due to Kennedy, Eberhart and Shi. It works by maintaining a swarm of particles moving around the search space influenced by the observed improvements of the other particles. The main advantage of the method is that it does not use the gradient of the function to be optimized, what makes it suitable for problems where the gradient is impossible to derive or expensive in terms of CPU requirement. The performance of the proposed algorithm is tested on a standard problem. Keywords First keyword · Second keyword · More
S. Garcia Computer Science Department University of Oviedo Campus de Viesques Office 1.b.15 33206, Gijon Asturias Spain Tel.: +34-985-182487 Fax: +34-985-182156 E-mail: sgarcia@uniovi.es F. Reid Edinburgh Parallel Computing Centre Edinburgh Sotland Tel.: +44(0) 131-451-3410 Fax: +44(0)-131-650-6555 E-mail: fiona@epcc.ed.ac.uk D. Corne Heriot Watt University Edinburgh Sotland E-mail: dwcorne@macs.hw.ac.uk
2
Santiago Garcia Carbajal et al.
1 Introduction. PSO Kennedy & Eberhart (1995) proposed this bio-inspired PSO approach, which can be seen as a population-based optimization algorithm that performs a parallel search on a space of solutions. As an optimization algorithm, the purpose of the individuals in PSO is to find out the best position when they move through the problem space. These individuals in PSO, called particles, are initialized by a randomized velocity and position at the beginning of optimization, and then change their velocities and positions under the companions influence. In the optimization context, several solutions of a given problem constitute a population, called The Swarm. Each solution is seen as a social
PSO Algorithm for the Traveling Salesman and Other Optimization Problems.
An OpenMp Implementation
Santiago Garcia Carbajal · Fiona Reid · David Corne
Received: date / Accepted: date
Abstract Particle Swarm Optimization is a general purpose optimization method due to Kennedy, Eberhart and Shi. It works by maintaining a swarm of particles moving around the search space influenced by the observed improvements of the other particles. The main advantage of the method is that it does not use the gradient of the function to be optimized, what makes it suitable for problems where the gradient is impossible to derive or expensive in terms of CPU requirement. The performance of the proposed algorithm is tested on a standard problem. Keywords First keyword · Second keyword · More
S. Garcia Computer Science Department University of Oviedo Campus de Viesques Office 1.b.15 33206, Gijon Asturias Spain Tel.: +34-985-182487 Fax: +34-985-182156 E-mail: sgarcia@uniovi.es F. Reid Edinburgh Parallel Computing Centre Edinburgh Sotland Tel.: +44(0) 131-451-3410 Fax: +44(0)-131-650-6555 E-mail: fiona@epcc.ed.ac.uk D. Corne Heriot Watt University Edinburgh Sotland E-mail: dwcorne@macs.hw.ac.uk
2
Santiago Garcia Carbajal et al.
1 Introduction. PSO Kennedy & Eberhart (1995) proposed this bio-inspired PSO approach, which can be seen as a population-based optimization algorithm that performs a parallel search on a space of solutions. As an optimization algorithm, the purpose of the individuals in PSO is to find out the best position when they move through the problem space. These individuals in PSO, called particles, are initialized by a randomized velocity and position at the beginning of optimization, and then change their velocities and positions under the companions influence. In the optimization context, several solutions of a given problem constitute a population, called The Swarm. Each solution is seen as a social