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There exists the disadvantages such as prematurity in particle swarm optimization because of the decrease of swarm diversity. In order to solve this problem an immune particle swarm optimization(Immune-PSO)algorithm is proposed which is combined with immune clone selection algorithm, Clone copy operator, clone hyper-mutation operator and clone selection operator are performed during the evolutionary...
Particle swarm optimization has poor diversity, slow convergence speed and is easy to trap into local optimum during the course of searching. Clonal selection mechanism and idiotypic immune network theory exhibited in biological immune system are introduced into particle swarm optimization algorithm, and a particle swarm optimization algorithm based on immune mechanism is proposed. The new algorithm...
A novel algorithm, called quantum-inspired immune evolutionary algorithm (QIEA), is proposed to avoid premature convergence. Based on the combining of the quantum evolutionary algorithm with the main mechanisms of immune, QIEA can adopt the high-effective parallelism of QEA and using a lot of operators such as mutation and selection to improve the traditional quantum evolutionary algorithm (QEA),...
Engineering design studies can often be cast in terms of optimization problems. However, for such an approach to be worthwhile, designers must be content that the optimization approaches employed is fast convergence. Usefulness of heuristic algorithm as the search method for diverse optimization problems is examined. Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural...
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