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Image thresholding is an important technique for image processing and pattern recognition. In this paper, a new multilevel image thresholding algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. Three different methods such as the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO) and the fast Otsupsilas...
As an important parameter, up to day, many strategies for cognitive coefficient have been proposed. However, there is still some work need to do. Since each particle maintains different living experience e.g. feeding, nursing baby and so on. Thus different individual will make a different decision. However, this decision mechanism is not included in the improved particle swarm optimization (PSO)....
Cognitive learning factor is an important parameter in particle swarm optimization algorithm(PSO). Although many selection strategies have been proposed, there is still much work need to do. Inspired by the black stork foraging process, this paper designs a new cognitive selection strategy, in which the whole swarm is divided into adult and infant particle, and each kind particle has its special choice...
Alignment particle swarm optimization (APSO) is a novel variant of particle swarm optimization aiming to improve the population diversity. The topology structure of APSO is gbest model. Since the small-world model is more suit for the natural animal communication network, in this paper, it is incorporated into the methodology of APSO to further improve the performance. Simulation results show this...
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...
Particle swarm optimization (PSO) is a new swarm intelligent optimization technique. Although it maintains a fast convergent speed, it is still easy trapped into a local optimum when dealing with high-dimensional numerical problems. To overcome this shortcoming, in this paper, a new variant of PSO is designed hybrid with a dynamic population strategy and crossover operator. Simulation results show...
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...
Aiming at the demerits of extremum random disturbed arithmetic operator of a particle swarm optimization algorithm, the reasonable amelioration is put forward based on the design idea of extremum random disturbed arithmetic operator. An improved particle swarm optimization algorithm is put forward and applied to parameter selection of support vector machine. The regress modeling of two common functions...
To solve fuzzy and non-linear features of mechanical equipment. A new computational intelligence method was proposed by combing based on extended T-S fuzzy model of self-adaptive disturbed PSO and BP neural network algorithm. Firstly, the T-S fuzzy model is modified, and then uses the extended T-S model to adjust the PSO parameter. Secondly, the neural network is trained by the modified PSO algorithm...
A Particle Swarm Optimization algorithm with feasibility-based rules (FRPSO) is proposed in this paper to solve mixed-variable optimization problems. An approach to handle various kinds of variables is discussed. Constraint handling is based on simple feasibility-based rules, not needing addinional penalty parameters and not guaranteeing to be in the feasible region at all times. Two real-world mixed-varible...
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