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Consideration of Particle Swarm Optimization combined with tabu search
Author(s) -
Nakano Shinichi,
Ishigame Atsushi,
Yasuda Keiichiro
Publication year - 2010
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.20966
Subject(s) - tabu search , particle swarm optimization , benchmark (surveying) , mathematical optimization , metaheuristic , computer science , multi swarm optimization , swarm behaviour , guided local search , algorithm , mathematics , geography , geodesy
This paper presents a new form of Particle Swarm Optimization (PSO) based on the concept of tabu search (TS). In PSO, when a particle finds a local optimal solution, all of the particles gather around that one, and cannot escape from it. On the other hand, TS can escape from the local optimal solution by moving away from the best present solution. The proposed Tabu List PSO (TL‐PSO) is a method for combining the strong points of PSO and TS. This method stores the history of pbest in a tabu list. When a particle has a reduced searching ability, it selects a pbest of the past from the historical values, which is used for the update. This makes each particle active, and the searching ability of the swarm makes progress. The proposed method was validated by numerical simulations with several functions that are well known as optimization benchmark problems for comparison to the conventional PSO method. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 172(4): 31–37, 2010; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20966