EPSO - best-of-two-worlds meta-heuristic applied to power system problems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Swarm directions embedded in fast evolutionary programming
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Self-adaptive mutations may lead to premature convergence
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow
Computers and Operations Research
Swarm algorithms with chaotic jumps applied to noisy optimization problems
Information Sciences: an International Journal
An improved probability particle swarm optimization algorithm
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
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Bare Bones Particle Swarm Optimization (BBPSO) is a powerful algorithm, which has shown potential to solving multimodal optimization problems. Unfortunately, BBPSO may also get stuck into local optima when optimizing functions with many local optima in high dimensional search space. In previous attempts an approach was developed which consists of a jump strategy combined with PSO in order to escape from local optima and promising results have been obtained. In this paper, we combine BBPSO with a jump strategy when no fitness improvement is observed. The jump strategy is implemented based on the Gaussian or the Cauchy probability distribution. The algorithm was tested on a suite of well-known benchmark multimodal functions and the results were compared with those obtained by the standard BBPSO algorithm and with BBPSO with re-initialization. Simulation results show that the BBPSO with the jump strategy performs well in all functions investigated. We also notice that the improved performance is due to a successful number of Gaussian or Cauchy jumps.