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.
By combining CMBOA and PSO with quasi-oppositional learning, a cloud estimation of distribution particle swarm optimizer is firstly introduced. Then, it is extended to multi-objective optimization problems by using maximum ranking. In the algorithm's offspring generation scheme, new individuals are generated in the cloud estimation of distribution way or in the PSO way. And instead of Pareto dominance,...
Cloud estimation of distribution particle swarm optimizer combining PSO and cloud model is introduced. In the algorithm's offspring generation scheme, new particles are generated in the cloud estimation of distribution way or in the PSO way. The innovation of the algorithm is production of cloud particles according to the cloud model theory. The cognitive population obtained during optimization is...
A new multi-objective estimation of distribution algorithm combined with PSO by using a Pareto-based method is proposed and applied in RFID network design. In the algorithm's offspring generation scheme, one part of individuals is sampled in the search space from the constructed probabilistic distribution model and the other part individuals are generated by the velocity-free PSO. A balance parameter...
To solve the premature problem of particle swarm optimization, firstly, the dynamic nonlinear inertia weights are designed which can make particles retain the favorable conditions and converge to the global optima continually; secondly, two kinds of anti-mistake equations are introduced which can make the stagnated particles break away from the local optima and dynamically search the global optima;...
To solve the premature convergence problem of particle swarm optimization, two novel methods are introduced to adjust the inertia weight in parallel according to different fitness values of two dynamic sub-swarms. When the fitness values of the particles are worse than the average, the inertia weight is adjusted by the introduced dynamic piecewise linear chaotic map which can make the local-optima...
Two new methods are introduced to modify the velocity in particle swarm optimization cooperatively: when the fitness values of some particles are worse than the average, the dynamic Zaslavskii chaotic map is devised to modify the velocity, which can make particles break away from the local optima and search global optima dynamically in very complex environments. On the contrary, when the fitness values...
To solve the premature convergence problem of particle swarm optimization, two novel methods are introduced to adjust the inertia weight in parallel according to different fitness values of two dynamic sub-swarms. When fitness values are better than or equal to the average, two types of dynamic nonlinear equations are proposed to adjust the inertia weight in a continuous convex area which can retain...
A no velocity particle swarm optimiser with forgetting factor and center is presented. In the algorithm, the position of a particle is influenced not only by the personal best position and global best position but also by the swarm's center , and a particle has only position without velocity similar to bare bones PSO. The proposed algorithm determined by four real parameters is theoretically analyzed...
The blind multi-channels identification problem is studied in this paper. A cost function based on the orthogonal property between the output autocorrelation matrix and the channels parameter matrix is first constructed for a signal-input multiple-output FIR system. Then, an improved particle swarm optimizer, in which the personal best particle is replaced with the weight average of personal best...
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.