The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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...
Correct recognition of the lines is essential for technical drawing understanding. Automation solution is quite difficult due to the limitations of machine vision algorithm. In order to promote development of better technology, according to the fast and high-quality clustering algorithm particle swarm optimization (PSO), a new fast and high-quality line clustering algorithm present in this paper,...
Analyzing the distance between the location and the new location, we conclude inertia weight method which linearly decreases from 0.9 to 0.4 has the powerful local search ability on Schafferpsilas F6 function. In order to improve the balance between local and global search ability, the novel adaptive PSO algorithm which evaluates a reset function to control the inertia weight value is proposed. Once...
In this paper, we investigate the performance of various interference cancellation techniques in direct-sequence ultra-wideband (DS-UWB) communication systems. Multiple access interference (MAI) causes the performance of the conventional single user detector in DS-UWB systems to degrade. Due to high complexity of the optimum multiuser detector, suboptimal multiuser detectors with less complexity and...
This paper present a class of investment problem, in which many items could be chosen in a group decision environment. Usually there is a decision table from the board of directors after discussions. Most of the data come from their experience or estimation. The information is redundant and inaccurate. Swarm-based rough set approach is introduced to make an attempt to solve the problem. Rough set...
Particle Swarm Optimization (PSO) is arguably one of the most popular nature- inspired algorithms for real parameter optimization at present. The existing theoretical research on PSO is mostly based on the gbest (global best) particle topology, which usually is susceptible to false or premature convergence over multi-modal fitness landscapes. The present standard PSO (SPSO 2007) uses an lbest (local...
Scheduling is a key problem in distributed heterogeneous computing systems in order to benefit from the large computing capacity of such systems and is an NP-complete problem. In this paper, we present a Particle Swarm Optimization (PSO) approach for this problem. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles...
In this paper, the Lyapunov stability theory is employed to analyze the stability of standard version of particle swarm optimization, and a random inertia weight selection strategy is obtained from the analyzed results. Simulation results show this random strategy may provide an efficient performance.
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.