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As the K-means algorithm is dependent on the initial clustering center, and the particle swarm optimization (PSO) converges prematurely and is easily trapped in local minima, a Gaussian kernel particle swarm optimization clustering algorithm is proposed in this paper. The algorithm adopts the theory of good point set to initialize population, which makes the initial clustering center more rational...
This paper presents an improved particle swarm optimization for solving interval nonlinear programming, and considers the nonlinear programming problem, which is based on immune algorithm. And can make the particles only follow the global extremum and have a definite evolution direction when they are renewed. This improved approach has been tested on some problems commonly used in the literature....
This paper presents a fast-stable population migration algorithm for multi-objective optimization to solve multi-objective optimization problems. Based on the concept of Pareto non-domination and guided by a global optimization experiments, this algorithm adopts dynamic mutation operator and the entire population migrating method to increase the algorithm convergence speed and population diversity...
In this paper, Pareto non-dominated ranking, crowding distance, tournament selection methods and mean particle swarm optimization were introduced, we using these concepts, a novel mean particle swarm optimization algorithm for multi-objective optimization problem is proposed. Finally, three standard non-constrained multi-objective functions and four constrained multi-objective functions are used to...
A hybrid particle swarm optimization algorithm for solving non-linear parameter estimation is proposed, which is based on genetic algorithm. And can increase the diversity of population and make the particles have a definite evolution direction when they are renewed. This improved approach has been tested on some problems commonly used in this paper. The results show that the proposed approach is...
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