Group Discussion Mechanism Based Particle Swarm Optimization
Inspired by the group discussion behavior of students in class, a new group topology is designed and incorporated into original particle swarm optimization (PSO). And thus, a novel modified PSO, called group discussion mechanism based particle swarm optim
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Department of Business Management, Shenzhen Institute of Information Technology, Shenzhen 518172, China 2 College of Management, Shenzhen University, Shenzhen, China [email protected], [email protected]
Abstract. Inspired by the group discussion behavior of students in class, a new group topology is designed and incorporated into original particle swarm optimization (PSO). And thus, a novel modified PSO, called group discussion mechanism based particle swarm optimization (GDPSO), is proposed. Using a group discussion mechanism, GDPSO divides a swarm into several groups for local search, in which some smaller teams with a dynamic change topology are included. Particles with the best fitness value in each group will be selected to learn from each other for global search. To evaluate the performance of GDPSO, four benchmark functions are selected as test functions. In the simulation studies, the performance of GDPSO is compared with some variants of PSOs, including the standard PSO (SPSO), PSO-Ring and PSO-Square. The results confirm the effectiveness of GDPSO in some of the benchmarks. Keywords: Group discussion
Topology GDPSO
1 Introduction Inspired by a swarm behavior of bird flock and fish school, particle swarm optimization (PSO) was originally proposed by Kenney and Eberhart [1, 2]. Since its inception, numerous scholars have been increasingly interested in the work of employing PSO to solve various complicated optimization problems and putting forward a series of methods to improve the performance of PSO in case of trapping in local optimum. The analysis and improvement of PSO can be mainly summarized into three categories: parameters adjustment [3, 4], new population topology design [5–9] and hybrid strategies [10, 11]. In PSO, each particle searches for a better position in accordance with its own experience and the best experience of its neighbors [12]. Accordingly, a variety of researches have been dedicated to modifying the information exchange mechanisms between neighbors (learning exemplars) with various population topological structures. Kennedy proposed a Ring topology, with which each particle is only connected to its immediate neighbors [6]. Mendes presented three other topologies, i.e., four clusters, Pyramid and Square to guarantee every individual fully informed [7]. Jiang proposed a novel age-based PSO with age-group topology [8]. Lim proposed a new variant of PSO with increasing topology connectivity that increases the particle’s topology connectivity with time as well as performs the shuffling mechanism [9]. © Springer International Publishing Switzerland 2016 D.-S. Huang et al. (Eds.): ICIC 2016, Part III, LNAI 9773, pp. 88–95, 2016. DOI: 10.1007/978-3-319-42297-8_9
Mechanism Based Particle Swarm Optimization
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By mimicking the group discussion behavior of students in class, a new topology is designed. And thus an improved PSO, named GDPSO is proposed. Through some of the benchmarks, the experimental results showed that the proposed GDPSO algorithm adjusts the balance between the lo
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