TY - GEN

T1 - Fitness-distance-ratio based particle swarm optimization

AU - Peram, T.

AU - Veeramachaneni, K.

AU - Mohan, C. K.

N1 - Publisher Copyright:
© 2003 IEEE.

PY - 2003

Y1 - 2003

N2 - This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to combat the problem of premature convergence observed in many applications of PSO. The proposed new algorithm moves particles towards nearby particles of higher fitness, instead of attracting each particle towards just the best position discovered so far by any particle. This is accomplished by using the ratio of the relative fitness and the distance of other particles to determine the direction in which each component of the particle position needs to be changed. The resulting algorithm (FDR-PSO) is shown to perform significantly better than the original PSO algorithm and some of its variants, on many different benchmark optimization problems. Empirical examination of the evolution of the particles demonstrates that the convergence of the algorithm does not occur at an early phase of particle evolution, unlike PSO. Avoiding premature convergence allows FDR-PSO to continue search for global optima in difficult multimodal optimization problems.

AB - This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to combat the problem of premature convergence observed in many applications of PSO. The proposed new algorithm moves particles towards nearby particles of higher fitness, instead of attracting each particle towards just the best position discovered so far by any particle. This is accomplished by using the ratio of the relative fitness and the distance of other particles to determine the direction in which each component of the particle position needs to be changed. The resulting algorithm (FDR-PSO) is shown to perform significantly better than the original PSO algorithm and some of its variants, on many different benchmark optimization problems. Empirical examination of the evolution of the particles demonstrates that the convergence of the algorithm does not occur at an early phase of particle evolution, unlike PSO. Avoiding premature convergence allows FDR-PSO to continue search for global optima in difficult multimodal optimization problems.

KW - Animals

KW - Application software

KW - Cognition

KW - Computer science

KW - Convergence

KW - Evolutionary computation

KW - Particle swarm optimization

KW - Performance analysis

KW - Power engineering and energy

KW - Problem-solving

UR - http://www.scopus.com/inward/record.url?scp=84942155292&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84942155292&partnerID=8YFLogxK

U2 - 10.1109/SIS.2003.1202264

DO - 10.1109/SIS.2003.1202264

M3 - Conference contribution

AN - SCOPUS:84942155292

T3 - 2003 IEEE Swarm Intelligence Symposium, SIS 2003 - Proceedings

SP - 174

EP - 181

BT - 2003 IEEE Swarm Intelligence Symposium, SIS 2003 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2003 IEEE Swarm Intelligence Symposium, SIS 2003

Y2 - 24 April 2003 through 26 April 2003

ER -