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.
Motivated by the growing demand of accuracy and low computational time in optimizing functions in various fields of engineering, an approach has been presented using the technique of parallel computing. The parallelization has been carried out on one of the simplest and flexible optimization algorithms, namely the particle swarm optimization (PSO) algorithm. PSO is a stochastic population global optimizer...
In the present study we propose a new hybrid version of differential evolution (DE) and particle swarm optimization (PSO) algorithms. In the proposed algorithm named as hybrid differential evolution (HDE) a `switchover constant' called ?? is defined. HDE starts as the basic DE algorithm which switches over to PSO when ?? is activated. The constant ?? on the other hand is activated at a point where...
The weapon target assignment problem can be modeled as an optimization problem in which the objective is to assign weapons to target in order to maximize the optimum target damage value. The mathematical model of the problem is subject to various constraints depending on the availability of weapons The objective function of the problem is non linear and the constraints are linear in nature. Also,...
The coexistence of multimedia services in e-communication systems, with varying bandwidth utilization characteristics, impedes the efficiency of rate control and thereby impacts on the Quality of Service (QoS), in terms of low throughput. As such, the rate control for multimedia flows remains an open problem. This paper proposes a memetic optimization approach to rate allocation of multiclass services...
Invasive weed optimization (IWO) has been found to be a simple but powerful algorithm for function optimization over continuous spaces. It has reportedly outperformed many types of evolutionary algorithms and other search heuristics when tested over both benchmark and real-world problems. However the performance of most search heuristics deteriorates severely when applied to the task of optimization...
This paper presents a new diversity guided particle swarm optimization algorithm (PSO) named beta mutation PSO or BMPSO for solving global optimization problems. The BMPSO algorithm makes use of an evolutionary programming based mutation operator to maintain the level of diversity in the swarm population, thereby maintaining a good balance between the exploration and exploitation phenomena and preventing...
This paper investigates the effect of initiating the population with various probability distributions and low discrepancy sequences on the behavior of Particle Swarm Optimization (PSO). The probability distributions: Gaussian, Exponential, Beta and Gamma distribution and the low discrepancy sequences: Van der Corput and Sobol are considered in this study. Based on these probability distributions,...
The university course timetabling problem is a combinatorial optimization problem concerning the scheduling of a number of subjects into a finite number of timeslots in order to satisfy a set of specified constraints. The timetable problem can be very hard to solve, especially when attempting to find a near-optimal solutions, with a large number of instances. This paper presents a combination of particle...
Quantum-behaved Particle Swarm Optimization (QPSO) is a new particle swarm optimization (PSO) algorithm. Compared with standard PSO (SPSO), it guarantees that particles converge in global optimum point in probability and this algorithm has better performance and stability. This paper introduces an improved Adaptive QPSO algorithm, puts the parallelisms crude of AQPSO and high speed of computer together,...
In this paper, the particle swarm optimizer is modified to create the multi-swarm accelerating PSO which is applied to dynamic continuous functions. Different from the existing multi-swarm PSOs and local versions of PSO, the swarms are dynamic and the swarms' size is small. The whole population is divided into many small swarms, these swarms are regrouped frequently by using various regrouping schedules...
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.