Results 221 to 230 of about 28,590 (257)

Nonlinear inertia weight in particle swarm optimization

2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), 2017
In this paper, a particle swarm optimization method with a new strategy for inertia weight has been considered. The author abandoned the commonly used linear inertia weight and proposed a new dynamic inertia weight based on fitness of the particles. The new weight is a function of the best and the worst fitness of the particles.
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Dynamic Inertia Weight in Particle Swarm Optimization

2017
This paper proposes a particle swarm optimization method with a novel strategy for inertia weight. Instead of a commonly used linear inertia weight, a nonlinear, dynamic changing inertia weight is applied. The new presented weight is a function of the worst and the best fitness of individuals of a population.
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Inertia weight control strategies for PSO algorithms

2018
Particle swarm optimization (PSO) is a stochastic population-based algorithm which was originally introduced by Kennedy and Eberhart [1]. This optimization algorithm is motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. As in most of the metaheuristic optimization algorithms, in PSO, a population of
Ahmad Nickabadi, Reza Safabakhsh, Mohammad Mehdi Ebadzadeh   +2 more
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A dynamic inertia weight particle swarm optimization algorithm

Chaos, Solitons & Fractals, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jiao, Bin, Lian, Zhigang, Gu, Xingsheng
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A non-deterministic adaptive inertia weight in PSO

Proceedings of the 13th annual conference on Genetic and evolutionary computation, 2011
Particle Swarm Optimization (PSO) is a relatively recent swarm intelligence algorithm inspired from social learning of animals. Successful implementation of PSO depends on many parameters. Inertia weight is one of them. The selection of an appropriate strategy for varying inertia weight w is one of the most effective ways of improving the performance ...
Kusum Deep, Madhuri Arya, Jagdish Chand Bansal   +2 more
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Performance Evaluation of WMNs by WMN-PSOHC System Considering Random Inertia Weight and Linearly Decreasing Inertia Weight Replacement Methods

2019
Wireless Mesh Networks (WMNs) have many advantages such as low cost and increased high-speed wireless Internet connectivity, therefore WMNs are becoming an important networking infrastructure. In our previous work, we implemented a Particle Swarm Optimization (PSO) based simulation system for node placement in WMNs, called WMN-PSO. Also, we implemented
Shinji Sakamoto, Seiji Ohara, Leonard Barolli, Shusuke Okamoto   +3 more
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Exponential Inertia Weight in Particle Swarm Optimization

2016
This paper presents an improved particle swarm optimization algorithm (EWPSO) with a novel strategy for inertia weight. In the new algorithm, nonlinear inertia weight is proposed. The new weight is an exponential function of the minimal and maximal fitness of the particles in each iteration. The set of benchmark function was used to test the new method.
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Inertia Weight Adaption in Particle Swarm Optimization Algorithm

2011
In Particle Swarm Optimization (PSO), setting the inertia weight w is one of the most important topics. The inertia weight was introduced into PSO to balance between its global and local search abilities. In this paper, first, we propose a method to adaptively adjust the inertia weight based on particle's velocity information.
Zheng Zhou, Yuhui Shi
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Improved DPSO Algorithm with Dynamically Changing Inertia Weight

2015
Population Diversity in Particle Swarm Optimization DPSO algorithm can effectively balance the "exploration" and "exploitation" ability of the PSO optimization algorithm and improve the optimization accuracy and stability of standard PSO algorithm. However, the accuracy of DPSO for solving the multi peak function will be obviously decreased.
Jing Xin, Cuicui Yan, Xiangshuai Han
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Time and space, weight and inertia

Nuclear Physics A, 1967
A. D. Fokker, Arthur Komar
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