نتایج جستجو برای: pso variants

تعداد نتایج: 118716  

A. R. Fathi H. R. Mohammadi Daniali N. Bakhshinezhad S. A. Mir Mohammad Sadeghi

Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms ...

Journal: :Appl. Soft Comput. 2011
S. Ganguly N. C. Sahoo D. Das

This paper presents a comprehensive study on monoand multi-objective approaches for electrical distribution network design using particle swarmoptimization (PSO). Specifically, two distribution network design problems, i.e., static and expansion planning, are solved using PSO. The network planning involves optimization of both network topology and branch conductor sizes. Both the planning probl...

2013
Desheng LI

This paper proposes a hybrid cooperative quantum particle swarm optimization (HCQPSO), hybridizing dynamic varying search area, cooperative evolution, simulated annealing and quantum particle swarm optimization (PSO) for function optimization. In the proposed HQCPSO, a technique of dynamic varying search area helps reduce the search spaces and populations of swarms, which could make the optimiz...

2006
Snehal Kamalapur Varsha Patil Shirish Sane

Particle swarm Optimization (PSO) is mainly inspired by social behavior patterns of organisms that live and interact within large groups. The term PSO refers to a relatively new family of algorithms that is used to find optimal or near to optimal solutions to numerical and qualitative problems. It is an optimization paradigm that simulates the ability of human to process knowledge. The capabili...

2015
Wei Hong Lim Nor Ashidi Mat Isa

In this paper, a new variant of particle swarm optimisation (PSO) called PSO with improved learning strategy (PSO-ILS) is developed. Specifically, an ILS module is proposed to generate a more effective and efficient exemplar, which could offer a more promising search direction to the PSO-ILS particle. Comparison is made on the PSO-ILS with 6 well-established PSO variants on 10 benchmark functio...

2010
Ying Tan Yuanchun Zhu

Inspired by observing fireworks explosion, a novel swarm intelligence algorithm, called Fireworks Algorithm (FA), is proposed for global optimization of complex functions. In the proposed FA, two types of explosion (search) processes are employed, and the mechanisms for keeping diversity of sparks are also well designed. In order to demonstrate the validation of the FA, a number of experiments ...

2002
E. C. Laskari K. E. Parsopoulos

The investigation of the performance of the Particle Swarm Optimization (PSO) method in Integer Programming problems, is the main theme of the present paper. Three variants of PSO are compared with the widely used Branch and Bound technique, on several Integer Programming test problems. Results indicate that PSO handles e ciently such problems, and in most cases it outperforms the Branch and Bo...

Journal: :Journal of Algorithms & Computational Technology 2016

2018
Nikolai Dyrberg Loft Lone Skov Mads Kirchheiner Rasmussen Robert Gniadecki Tomas Norman Dam Ivan Brandslund Hans Jürgen Hoffmann Malene Rohr Andersen Ram Benny Dessau Ann Christina Bergmann Niels Møller Andersen Mikkel Kramme Abildtoft Paal Skytt Andersen Merete Lund Hetland Bente Glintborg Steffen Bank Ulla Vogel Vibeke Andersen

BACKGROUND Psoriasis (PsO) is a chronic inflammatory disease with predominantly cutaneous manifestations. Approximately one third of patients with PsO develop psoriatic arthritis (PsA), whereas the remaining proportion of patients has isolated cutaneous psoriasis (PsC). These two phenotypes share common immunology, but with different heredity that might in part be explained by genetic variables...

Journal: :CoRR 2010
Suresh Chandra Satapathy Gunanidhi Pradhan Sabyasachi Pattnaik J. V. R. Murthy P. V. G. D. Prasad Reddy

In this paper we have investigated the performance of PSO Particle Swarm Optimization based clustering on few real world data sets and one artificial data set. The performances are measured by two metric namely quantization error and inter-cluster distance. The K means clustering algorithm is first implemented for all data sets, the results of which form the basis of comparison of PSO based app...

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