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Optimization Using Particle Swarms with Near Neighbor Interactions

Veeramachaneni, Kalyan ; Peram, Thanmaya ; Mohan, Chilukuri ; Osadciw, Lisa Ann

Genetic and Evolutionary Computation — GECCO 2003, p.110-121 [Periódico revisado por pares]

Berlin, Heidelberg: Springer Berlin Heidelberg

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  • Título:
    Optimization Using Particle Swarms with Near Neighbor Interactions
  • Autor: Veeramachaneni, Kalyan ; Peram, Thanmaya ; Mohan, Chilukuri ; Osadciw, Lisa Ann
  • Assuntos: Benchmark Problem ; Particle Swarm ; Particle Swarm Optimization ; Particle Swarm Optimization Algorithm ; Premature Convergence
  • É parte de: Genetic and Evolutionary Computation — GECCO 2003, p.110-121
  • Descrição: 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. In the new algorithm, each particle is attracted towards the best previous positions visited by its neighbors, in addition to the other aspects of particle dynamics in PSO. 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, known as Fitness-Distance-Ratio based PSO (FDR-PSO), is shown to perform significantly better than the original PSO algorithm and several of its variants, on many different benchmark optimization problems. Avoiding premature convergence allows FDR-PSO to continue search for global optima in difficult multimodal optimization problems, reaching better solutions than PSO and several of its variants.
  • Títulos relacionados: Lecture Notes in Computer Science
  • Editor: Berlin, Heidelberg: Springer Berlin Heidelberg
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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