GPU-based parallel particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Effect of the block occupancy in GPGPU over the performance of particle swarm algorithm
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
GPU-based asynchronous particle swarm optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Hybrid particle swarm optimization for vehicle routing problem with time windows
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
Software framework for vehicle routing problem with hybrid metaheuristic algorithms
ACC'11/MMACTEE'11 Proceedings of the 13th IASME/WSEAS international conference on Mathematical Methods and Computational Techniques in Electrical Engineering conference on Applied Computing
GPU-Based evaluation to accelerate particle swarm algorithm
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Time-Varying mutation in particle swarm optimization
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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A novel particle swarm optimization with triggered mutation (PSO-TM) is presented in this paper for better performance. First, a technique is designed to evaluate the "health" of swarm. When the swarm is successively "unhealthy" for a certain number of iterations, uniform mutation is applied to the position of each particle in a probabilistic way. If the mutations produce worse particles, the memorized previous positions are retrieved as current positions of these particles, hence the normal evolution process of the swarm will not be fiercely interrupted by such bad mutations. Experiments are conducted on 29 benchmark test functions to show the promising performance of our proposed PSOTM. The results show that the PSO-TM performs much better than the standard PSO on almost all of the 29 test functions, especially those multimodal, complex ones of hybrid composition. Besides, PSO-TM adds little computation complexity to the standard PSO, and runs almost equally fast. Furthermore, we have implemented PSO-TM based on Graphic Processing Unit(GPU) in parallel. Compared with the CPU-based standard PSO, the proposed PSO-TM can reach a speedup of 25×, as well as an improved optimizing performance.