An Improved Parallel Particle Swarm Optimization

Abstract

AbstractIn the area of global optimization, a variety of techniques have been developed to find the global minimum. These techniques, in most cases, require a significant amount of computational resources and time to complete and therefore there is a need to develop parallel techniques. In addition, the wide spread of parallel architectures in recent years greatly facilitates the implementation of such techniques. Among the most widely used global optimization techniques is the particle swarm optimization technique. In this work, a series of modifications are proposed in the direction of efficient parallelization for particle swarm optimization. These modifications include an innovative velocity calculation mechanism that has also been successfully used in the serial version of the method, mechanisms for propagating the best particles between parallel computing units, but also a process termination mechanism, which has been properly configured for efficient execution in parallel computing environments. The proposed technique was applied to a multitude of computational problems from the relevant literature and the results were more than promising, since it was found that increasing the computational threads can significantly reduce the required number of function calls to find the global minimum. The proposed technique is at rate of 50–70% of the required number of function calls compared to other optimization techniques. This reduction is visible even if one to two parallel processing units are used. In addition, with the increase in parallel processing units, a drastic reduction in the number of calls is observed and therefore a reduction in the required computing time, which can reach up to 70%.