Particle Swarm Optimization - A Survey <s> Parallel Implementation <s> This paper focuses on a parallel version of particle swarm optimization (PSO) algorithm which can significantly reduces execution time for solving complex large-scale optimization problems. This paper gives an overview of PSO algorithm, and then proposes a design and an implementation of parallel PSO. The proposed algorithm eliminates redundant synchronizations and optimizes message transfer to overlap communication with computation. The experimental results showed that 13.2 times speedup was obtained by the proposed parallel PSO algorithm with 14 processors. <s> BIB001 </s> Particle Swarm Optimization - A Survey <s> Parallel Implementation <s> In optimization problems involving large amounts of data, such as web content, commercial transaction information, or bioinformatics data, individual function evaluations may take minutes or even hours. particle swarm optimization (PSO) must be parallelized for such functions. However, large-scale parallel programs must communicate efficiently, balance work across all processors, and address problems such as failed nodes. We present mapreduce particle swarm optimization (MRPSO), a PSO implementation based on the mapreduce parallel programming model. We describe MapReduce and show how PSO can be naturally expressed in this model, without explicitly addressing any of the details of parallelization. We present a benchmark function for evaluating MRPSO and note that MRPSO is not appropriate for optimizing easily evaluated functions. We demonstrate that MRPSO scales to 256 processors on moderately difficult problems and tolerates node failures. <s> BIB002
As the problem scale grows, increasing the number of particles will certainly improve the chance of finding a good solution. Several authors have pursued parallel implementation of PSO. Schutte et al.  implemented PSO in parallel by dividing the swarm population to processor nodes. In their setup, only the evaluation of the objective function f was done in parallel in a synchronized manner. Experimental results using a cluster computer up to 32 nodes showed good scalability provided that objective functions require nearidentical computational costs for evaluation. Li and Wada BIB001 also assigned a subswarm to each processor node running in parallel. In order to solve the bottleneck for speed up in exchanging information between the nodes, they proposed the delayed exchange scheduling which can omit global synchronization. It is reported that, by this improvement, an improved scalability was achieved in a PC cluster. In McNabb et al. BIB002 , PSO was coded on a parallel computing framework called MapReduce, and was evaluated using a cluster with max. 128 nodes. Similar scalability as former works were observed up to a certain number of nodes. However, using more nodes, it is reported that implementation and communication overheads dominated and hindered further improvements.