Consideration of Particle Swarm Optimization combined with tabu search

AbstractThis 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 ofpbestin a tabu list. When a particle has a reduced searching ability, it selects apbestof 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