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With many advantages of computing with real number, few parameters to be adjusted, the Particle Swarm Optimizer (PSO) is applied in many fields. The major problem with PSO algorithm is premature convergence. Some optimization strategies were introduced to overcome it. In these former researches, the dimension of benchmarks in experiments was usually set to be a small value. But it can be seen that...
This paper presents a dynamic mutation particle swarm optimization (DMPSO). The particle swarm optimization (PSO) is a popular swarm algorithm, which has exhibited good performance on many optimization problems. However, similar to other swarm intelligence algorithms, PSO also suffers from premature convergence. Sub swarm and mutation are widely used strategies in the PSO algorithm to overcome the...
The ant colony optimization is a new meta-heuristic, it is a population based algorithm and is a good method for combination optimizations. Due to the random probabilistic search strategy, the slow convergence is the main problem of the ACO. In order to improve the convergence of the algorithm, the premium-penalty ant colony optimization (PPACO) is proposed. In this new algorithm, the good solutions...
As an effective global optimization method, genetic algorithm has been used in real practice very widely. When it is used in real practice, its slow convergence and poor stability have become the main problems. In order to overcome these problems, from the creation of the initial population, immune selection operation, improved genetic operators, et al, an improved fast immunized genetic algorithm...
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