Cooperative Asynchronous Parallel Particle Swarm Optimization for Large Dimensional Problems

Abstract

Metaheuristics have been very successful to solve NP-hard optimization problems. However, some problems such as big optimization problems are too expensive to be solved using classical computing. Naturally, the increasing availability of high performance computing (HPC) is an appropriate alternative to solve such complex problems. In addition, the use of HPC can lead to more accurate metaheuristics if their internal mechanisms are enhanced. Particle swarm optimization (PSO) is one of the most know metaheuristics and yet does not have many parallel versions of PSO which take advantage of HPC via algorithmic modifications. Therefore, in this article, the authors propose a cooperative asynchronous parallel PSO algorithm (CAPPSO) with a new velocity calculation that utilizes a cooperative model of sub-swarms. The asynchronous communication among the sub-swarms makes CAPPSO faster than a parallel and more accurate than the master-slave PSO (MS-PSO) when the tested big problems.

Parallelization of Swarm Intelligence Algorithms: Literature Review

“…One way to profit from such powerful hardware is to run multiple swarms simultaneously. This kind of implementation can be seen in [24,47,[47][48][49][50][51][52][53][54][55][56]. SI algorithms have a non-deterministic behavior, therefore each run typically produces different results.…”

“…In a distributed environment with several nodes, for example, data transfers have a huge impact on the execution time. An asynchronous communication strategy was proposed by Bourennani et al in order to enable overlapping communications and computations [56]. In their proposed version of PSO, each particle uses its local best, the subswarm best and the best solution found so far among all subswarms to update its position.…”

“…These two frameworks can be combined. Examples of parallel SI algorithms using these frameworks can be found in [26,49,56,[64][65][66][67][68].…”

Parallel Differential Evolutionary Particle Filtering Algorithm Based on the CUDA Unfolding Cycle

“…Literature [6,7] proposed a GPU-based parallel optimization design and implementation of particle filtering to improve the computational speed of the tracking algo-rithm. Literature [8][9][10] designed and implemented a parallel particle swarm optimization algorithm based on CUDA, which uses a large number of GPU threads to accelerate the convergence speed of the whole particle swarm. Parallel statute algorithms are used in the abovementioned literature for parallel particle filtering algorithms to simplify thread operations.…”