نتایج جستجو برای: modified pso
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This paper proposes a modified form of operator based on Particle Swarm Optimization (PSO) for designing Genetic Fuzzy Rule Based System (GFRBS). The usual procedure of velocity updating in PSO is modified by calculating the velocity using chromosome’s individual best value and global best value based on an updating probability without considering the inertia weight, old velocity and constricti...
The main idea of this paper is to solve coverage problem in distributed wireless sensor network (WSN) by increasing sensor nodes coverage. This paper proposes a modified discrete binary particle swarm optimization algorithm for Wireless Sensor Network (WSN) nodes placement so that the maximum coverage is obtained.PSO is a real value algorithm, and the discrete PSO is proposed to be adapted to d...
Field programmable gate arrays (FPGAs) are becoming increasingly important implementation platforms for digital circuits. This paper focuses on the implementation of Adaptive Infinite Impulse response (IIR) filter on an FPGA using Modified Particle Swarm Optimization (PSO) Algorithm. The proposed Filter is capable of finding the global optimum solution for system identification problem in less ...
Particle swarm optimisation (PSO) was developed by Kennedy and Eberhart in 1995 (Kennedy & Eberhart, 1995) inspired by the collective behaviour of natural birds or fish. PSO is a stochastic optimisation technique that uses a behaviour of population composed by many search points called particle. In spite of easy implementation in computer algorithms, it is well known as a powerful numerical opt...
In this paper, a comparison among Particle swarm optimization (PSO), Bee Colony Optimization (BCO) and the Bat Algorithm (BA) is presented. In addition, a modification to the main parameters of each algorithm through an interval type-2 fuzzy logic system is presented. The main aim of using interval type-2 fuzzy systems is providing dynamic parameter adaptation to the algorithms. These algorithm...
Multilevel thresholding is one of the most popular image segmentation techniques. This paper presents a new multilevel maximum entropy thresholding method based on modified seeker optimization (MSO) algorithm. In the proposed method the thresholding problem is treated as an optimization problem and solved by using the MSO metaheuristics. Particle swarm optimization (PSO) algorithm is also imple...
Particle Swarm Optimization (PSO) is a bio-inspired evolutionary meta-heuristic that simulates the social behaviour observed in groups of biological individuals [4]. In standard PSO, the particle swarm is often attracted by sub-optimal solutions when solving complex multimodal problems, causing premature convergence of the algorithm and swarm stagnation [5]. Once particles have converged premat...
The digital filters play an important role in the field of science and technology. Due to phase linearity and stability digital FIR filters are used in number of applications. The various design technology are developed for the designing of the digital filters using evolutionary techniques like particle swarm optimization(PSO), genetic algorithm(GA), differential evolution(DE) etc. and the modi...
Particle Swarm Optimization (PSO) is a bioinspired meta-heuristic for solving complex global optimization problems. In standard PSO, the particle swarm frequently gets attracted by suboptimal solutions, causing premature convergence of the algorithm and swarm stagnation. Once the particles have been attracted to a local optimum, they continue the search process within a minuscule region of the ...
This paper presents a novel method for solving optimal power quality monitor placement problem in monitoring voltage sags in power systems using the adaptive quantum-inspired particle swarm optimization (PSO). The optimization considers multi objective functions and handles observability constraint determined by the concept of the topological monitor reach area. The overall objective function c...
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