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The particle swarm optimization (PSO) is a stochastic optimization algorithm imitating animal behavior, which shows a bad performance when optimizing the multimodal and high dimensional functions. Each particle uses own experience and otherpsilas to make decision, it is easy to trap into premature convergence, but group decision making with all the individuals to make decisions uses various experiences...
Premature convergence is a major problem of Particle Swarm Optimization (PSO).Although many strategies have been proposed, there is still some work needed to do in high-dimensional cases. To overcome this shortcoming, a diffused velocity update equation is designed aiming to improve the population diversity with a c. Simulation results show the performance of this new variant is superior to other...
Artificial bee colony (ABC) algorithm is a new global stochastic optimization algorithm based on the particular intelligent behavior of honeybee swarms, in which there exists many issues to be improved and solved. When onlooker bees exploit in ABC algorithm, they choose food source depending on the strategy of proportional selection that can result in the premature of the evolutionary process. In...
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