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.
Abstract-A new approach to ORPF (optimal reactive power flow) based on SFLA (shuffled frog leaping algorithm) is proposed. The algorithm approaches to solving ORPF problem are given. By applying the algorithm to dealing with IEEE 30-bus system, compared with the particle swarm optimization (PSO) algorithm and SGA(simple genetic algorithm),the experimental results show that the algorithm is indeed...
Aiming at the phenomenon of premature convergence and later period oscillatory occurrences, an adaptive particle swarm optimization algorithm with the changes of the population diversity was proposed. In the algorithm, the adaptive exponent decreasing inertia weight and a dynamic adaptive changing threshold were proposed, the satisfied particle of threshold will be mutation by the average distance...
A particle swarm optimization algorithm with partial mutation strategy (PMPSO) is developed. During the searching process, only premature convergence particles are mutated to escape from local optimum, and they are no more mutated in next several generations in order to search efficiently in other areas; other non-premature particles go on their evolutions normally. Several parameters of the PMPSO...
A particle swarm optimization with rich social cognition is developed for solving the premature convergence of particle swarm optimization. In this algorithm, the optimum from the particles' experiments is determined by learning probability and selective probability. The learning probability is adjusted to balance between the personal cognition and the social cognition. Experimental results for complex...
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...
The paper gives an improved particle swarm optimal algorithm in which a kind of exponent decreasing inertia weights is given to improve the convergence speed and a kind of stochastic mutations is used to improve the diversity of the swarm in order to overcome the disadvantage of premature convergence and later period oscillatory occurrences. It is shown by five representative benchmarks functionpsilas...
Particle Swarm Optimization (PSO) algorithm is known to be very efficient solution for electromagnetic (EM) optimization problems. In this paper we show that binary tournament selection applied to PSO algorithm further speeds-up its convergence. Having in mind that EM simulation is the most time-consuming part of the optimization, reducing the overall number of iterations (EM solver calls) is of a...
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.