A Modified Particle Swarm Optimizer with Dynamical Inertia Weight

2009 ◽  
pp. 767-776 ◽  
Author(s):  
Ai-min Miao ◽  
Xin-ling Shi ◽  
Jun-hua Zhang ◽  
En-yong Wang ◽  
Shu-qing Peng
Keyword(s):  
Particle Swarm ◽  
Particle Swarm Optimizer ◽  
Inertia Weight

Improved Particle Swarm Optimizer with Dynamically Adjusted Search Space and Velocity Limits for Global Optimization

2015 ◽  
Vol 24 (05) ◽  
pp. 1550017 ◽  
Author(s):  
Aderemi Oluyinka Adewumi ◽  
Akugbe Martins Arasomwan
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Particle Swarm Optimizer ◽  
Search Range ◽  
Original Algorithm ◽  
Inertia Weight ◽  
And Performance

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm.

An adaptive inertia weight strategy for particle swarm optimizer

2005 ◽  
Author(s):  
Kaiyou Lei ◽  
Fang Wang ◽  
Yuhui Qiu ◽  
Yi He
Keyword(s):  
Particle Swarm ◽  
Particle Swarm Optimizer ◽  
Inertia Weight

scholarly journals Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Jun Zheng ◽  
Hai-Feng Ling ◽  
Qiu Guan
Keyword(s):  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Method ◽  
Evolutionary Information ◽  
Adaptive Mechanism ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Adaptive Parameters ◽  
Individual Particles

Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.

A decreasing inertia weight particle swarm optimizer

2007 ◽  
Vol 39 (2) ◽  
pp. 203-228 ◽  
Author(s):  
Shu-Kai S. Fan ◽  
Yi-Yin Chiu
Keyword(s):  
Particle Swarm ◽  
Particle Swarm Optimizer ◽  
Inertia Weight

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear
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