Particle Swarm Optimization with Negative Gradient

2012 ◽  
Vol 468-471 ◽  
pp. 2550-2553
Author(s):  
Ri Su Na ◽  
Qiang Li ◽  
Li Ji Wu
Keyword(s):  
Computer Simulation ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Test Functions ◽  
Swarm Optimization ◽  
Standard Particle Swarm Optimization ◽  
Optimization Efficiency ◽  
Negative Gradient

Based on the standard particle swarm optimization, introduce the information about negative gradient to influence the update of velocities of the particles, proposed the particles swarm optimization with negative gradient , and make the movement of particles more pertinent. The result of computer simulation about several test functions indicates that the particle swarm optimization with negative gradient can improve optimization efficiency and get better results.

scholarly journals Fuzzy adaptive catfish particle swarm optimization

2012 ◽  
Vol 1 (2) ◽  
pp. 149 ◽  
Author(s):  
Li-Yeh Chuang ◽  
Sheng-Wei Tsai ◽  
Cheng-Hong Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Statistical Analysis ◽  
Particle Swarm ◽  
Test Functions ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Interactive Behavior ◽  
Different Dimensions ◽  
Swarm Intelligence Optimization ◽  
Fuzzy Adaptive

The catfish particle swarm optimization (CatfishPSO) algorithm is a novel swarm intelligence optimization technique.This algorithm was inspired by the interactive behavior of sardines and catfish. The observed catfish effect is applied toimprove the performance of particle swarm optimization (PSO). In this paper, we propose fuzzy CatfishPSO(F-CatfishPSO), which uses fuzzy to dynamically change the inertia weight of CatfishPSO. Ten benchmark functions with10, 20, and 30 different dimensions were selected as the test functions. Statistical analysis of the experimental resultsindicates that F-CatfishPSO outperformed PSO, F-PSO and CatfishPSO.

Particle Swarm Optimization with Team Spirit Inertia Weight

2011 ◽  
Vol 383-390 ◽  
pp. 5744-5750
Author(s):  
Xi Zhen Wang ◽  
Yan Li ◽  
Gang Hu Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Convergence Problem ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Standard Particle Swarm Optimization ◽  
Decline Curves ◽  
Team Spirit ◽  
Global And Local

A PSO Algorithm with Team Spirit Inertia weight (TSWPSO) is presented based on the study of the effect of inertia weight on Standard Particle Swarm Optimization (SPSO). Due to the theory of group in organization psychology, swarm is divided into multiple sub-swarms and search is run in a number of different sub-swarms which are parallel performed. Try to find or modify a curve which is compatible with optimized object within many inertia weight decline curves, in order to balance the global and local explorations ability in particle swarm optimization and to avoid the premature convergence problem effectively. The testes by five classical functions show that, TSWPSO has a better performance in both the convergence rate and the precision.

Application of Quantum-Behaved Particle Swarm Optimization in Engineering Constrained Optimization Problems

2011 ◽  
Vol 383-390 ◽  
pp. 7208-7213
Author(s):  
De Kun Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Standard Particle Swarm Optimization ◽  
Fixed Path

To overcome the shortage of standard Particle Swarm Optimization(SPSO) on premature convergence, Quantum-behaved Particle Swarm Optimization (QPSO) is presented to solve engineering constrained optimization problem. QPSO algorithm is a novel PSO algorithm model in terms of quantum mechanics. The model is based on Delta potential, and we think the particle has the behavior of quanta. Because the particle doesn’t have a certain trajectory, it has more randomicity than the particle which has fixed path in PSO, thus the QPSO more easily escapes from local optima, and has more capability to seek the global optimal solution. In the period of iterative optimization, outside point method is used to deal with those particles that violate the constraints. Furthermore, compared with other intelligent algorithms, the QPSO is verified by two instances of engineering constrained optimization, experimental results indicate that the algorithm performs better in terms of accuracy and robustness.

scholarly journals Using particle swarm optimization to solve test functions problems

2021 ◽  
Vol 10 (6) ◽  
pp. 3422-3431
Author(s):  
Issa Ahmed Abed ◽  
May Mohammed Ali ◽  
Afrah Abood Abdul Kadhim
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Standard Deviation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Speed Of Convergence ◽  
Test Functions ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Engineering Problems

In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.

Sign in / Sign up

Export Citation Format

Share Document