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Particle swarm optimization (PSO) algorithm has attracted great attention as a stochastic optimizing method due to its simplicity and power strength in optimization fields. However, two issues are still to be improved, especially, for complex multimodal problems. One is the premature convergence for multimodal problems. The other is the low efficiency for complex problems. To address these two issues,...
Based on particle swarm optimization (PSO) and artificial bee colony (ABC), a novel hybrid swarm intelligent algorithm is developed in this paper. Two information exchanging processes are introduced to share valuable information mutually between particle swarm and bee colony. Numerical results show that the proposed method is effective and performs better than both PSO and ABC.
Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems, e.g., aerodynamic design optimization, qualitative model learning in bioinformatics. To address this problem, we present a combination between genetic algorithms and clustering methods. Specifically, the clustering method used in this paper is affinity propagation. The numerical experiments...
An improved quantum-inspired evolutionary algorithm is presented in this paper. Quantum angle is adopted to present the quantum bit in the proposed algorithm. A novel quantum rotation gate strategy is adopted to adjust the direction of the quantum gate which is used to update the quantum population. The step size is adaptively adjusted rather than a fixed angle. Furthermore, the particle swarm optimization...
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