[1]QIN Quande,LI Li,CHENG Shi,et al.Interactive learning particle swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2012,7(6):547-553.
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Interactive learning particle swarm optimization algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 7 Number of periods: 2012 6 Page number: 547-553 Column: 学术论文—智能系统 Public date: 2012-12-25
Title:
Interactive learning particle swarm optimization algorithm
Author(s):
QIN Quande1;  LI Li1;  CHENG Shi2; 3;  LI Rongjun4
1. College of Management, Shenzhen University, Shenzhen 518060, China;
2. Department of Electrical Engineering and Electronics, Liverpool University, Liverpool L69 3GJ, UK;
3. Department of Electrical and Electronics Engineering, Xi’an JiaotongLiverpool University, Suzhou 215123, China;
4. School of Business Administration, South China University of Technology, Guangzhou 510640, China
Keywords:
particle swarm optimization algorithm;  interactive learning;  learning strategy;  learning behavior;  population diversity
CLC:
TP18
DOI:
-
Abstract:
Analyzing the drawbacks of learning mechanism in the basic particle swarm optimization (PSO), an interactive learning particle swarm optimization (ILPSO) is presented, which is inspired by the phenomenon in human society that individuals in different groups can learn each other. Particles are composed of two populations in ILPSO. When the best particle’s fitness value of two populations does not improve within a certain number of successive iterations, interactive learning strategies are implemented. According to the best particle′s fitness value of each population, a simulated annealing mechanism and roulette method are used to identify the learning population and the learned population. This paper proposes an empirical formula of sorting fitness value to calculate the probability of each particle in the learning population learning from the learned population. In order to escape selection pressure, a speed mutation method is used. The numerical experimental results of some benchmark functions show that ILPSO has good global search capability and is an effective method for solving complicated problems.
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