Abstract:
This paper presents a novel particle swarm optimization (PSO) algorithm that combines the strengths of several PSO variants into a single competitive algorithm. This nove...Show MoreMetadata
Abstract:
This paper presents a novel particle swarm optimization (PSO) algorithm that combines the strengths of several PSO variants into a single competitive algorithm. This novel algorithm, named Time Adaptive Dual Particle Swarm Optimization (TAD-PSO), is comprised of two specialized populations, with one focusing on exploration of the search space and the other on exploitation. The main population, specialized in exploration, uses orthogonal learning to create information-rich exemplars which intelligently guide particle movement throughout the search space. The auxiliary population uses a PSO variant known for its very fast convergence speed, and thus very high performance on unimodal problems. This population is specialized in exploitation of the interesting local minima. The main population size decays linearly, to foster exploration early and convergence in the later stages of the optimization procedure. Additionally, TAD-PSO does not have the topological structure of the swarm as an algorithm hyper-parameter, making it a fast and simple algorithm to apply to new problems. TAD-PSO was tested extensively and compared to 6 widely used PSO variants on 19 benchmark problems, for 10, 30 and 100 dimensions. TAD-PSO consistently ranked first in each dimensional space, making it a competitive optimization algorithm on both unimodal and multimodal problems.
Published in: 2017 IEEE Congress on Evolutionary Computation (CEC)
Date of Conference: 05-08 June 2017
Date Added to IEEE Xplore: 07 July 2017
ISBN Information: