An Adaptive Convergence Speed Controller Framework for Particle Swarm Optimization Variantsin Single Objective Optimization Problems

2015 ◽  
Author(s):  
Changjian Xu ◽  
Han Huang ◽  
Liang Lv
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Swarm Optimization ◽  
Speed Controller ◽  
Single Objective

A Study of Single-Objective Particle Swarm Optimization and Multi-Objective Particle Swarm Optimization

2014 ◽  
Vol 543-547 ◽  
pp. 1635-1638 ◽  
Author(s):  
Ming Li Song
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simple Realization ◽  
Single Objective ◽  
Selection Of

The complexity of optimization problems encountered in various modeling algorithms makes a selection of a proper optimization vehicle crucial. Developments in particle swarm algorithm since its origin along with its benefits and drawbacks are mainly discussed as particle swarm optimization provides a simple realization mechanism and high convergence speed. We discuss several developments for single-objective case problem and multi-objective case problem.

scholarly journals Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Zahra Beheshti ◽  
Siti Mariyam Shamsuddin ◽  
Sarina Sulaiman
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Convergence Speed ◽  
Swarm Optimization ◽  
Local Optima ◽  
Solution Accuracy ◽  
Global And Local ◽  
Local Topology

In recent years, particle swarm optimization (PSO) has been extensively applied in various optimization problems because of its structural and implementation simplicity. However, the PSO can sometimes find local optima or exhibit slow convergence speed when solving complex multimodal problems. To address these issues, an improved PSO scheme called fusion global-local-topology particle swarm optimization (FGLT-PSO) is proposed in this study. The algorithm employs both global and local topologies in PSO to jump out of the local optima. FGLT-PSO is evaluated using twenty (20) unimodal and multimodal nonlinear benchmark functions and its performance is compared with several well-known PSO algorithms. The experimental results showed that the proposed method improves the performance of PSO algorithm in terms of solution accuracy and convergence speed.

scholarly journals An Improved Particle Swarm Optimization with Gaussian Disturbance

2018 ◽  
Vol 232 ◽  
pp. 03015
Author(s):  
Changjun Wen ◽  
Changlian Liu ◽  
Heng Zhang ◽  
Hongliang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Local Optimal Solution ◽  
Gaussian Disturbance ◽  
Griewank Function

The particle swarm optimization (PSO) is a widely used tool for solving optimization problems in the field of engineering technology. However, PSO is likely to fall into local optimum, which has the disadvantages of slow convergence speed and low convergence precision. In view of the above shortcomings, a particle swarm optimization with Gaussian disturbance is proposed. With introducing the Gaussian disturbance in the self-cognition part and social cognition part of the algorithm, this method can improve the convergence speed and precision of the algorithm, which can also improve the ability of the algorithm to escape the local optimal solution. The algorithm is simulated by Griewank function after the several evolutionary modes of GDPSO algorithm are analyzed. The experimental results show that the convergence speed and the optimization precision of the GDPSO is better than that of PSO.

Multi-Objective Particles Swarm Optimization Approaches

2011 ◽  
pp. 20-42 ◽  
Author(s):  
Konstantinos E. Parsopoulos ◽  
Michael N. Vrahatis
Keyword(s):  
Particle Swarm Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Future Research ◽  
Swarm Optimization ◽  
Computational Burden ◽  
Multi Objective ◽  
Active Research ◽  
Single Objective

The multiple criteria nature of most real world problems has boosted research on multi-objective algorithms that can tackle such problems effectively, with the smallest possible computational burden. Particle Swarm Optimization has attracted the interest of researchers due to its simplicity, effectiveness and efficiency in solving numerous single-objective optimization problems. Up-to-date, there are a significant number of multi-objective Particle Swarm Optimization approaches and applications reported in the literature. This chapter aims at providing a review and discussion of the most established results on this field, as well as exposing the most active research topics that can give initiative for future research.

scholarly journals Improvement of Interior Ballistic Performance Utilizing Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hazem El Sadek ◽  
Xiaobing Zhang ◽  
Mahmoud Rashad ◽  
Cheng Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Feasible Region ◽  
Evolution Operators ◽  
Ballistic Performance ◽  
Swarm Optimization ◽  
Charge Design ◽  
Single Objective

This paper investigates the interior ballistic propelling charge design using the optimization methods to select the optimum charge design and to improve the interior ballistic performance. The propelling charge consists of a mixture propellant of seven-perforated granular propellant and one-hole tubular propellant. The genetic algorithms and some other evolutionary algorithms have complex evolution operators such as crossover, mutation, encoding, and decoding. These evolution operators have a bad performance represented in convergence speed and accuracy of the solution. Hence, the particle swarm optimization technique is developed. It is carried out in conjunction with interior ballistic lumped-parameter model with the mixture propellant. This technique is applied to both single-objective and multiobjective problems. In the single-objective problem, the optimization results are compared with genetic algorithm and the experimental results. The particle swarm optimization introduces a better performance of solution quality and convergence speed. In the multiobjective problem, the feasible region provides a set of available choices to the charge’s designer. Hence, a linear analysis method is adopted to give an appropriate set of the weight coefficients for the objective functions. The results of particle swarm optimization improved the interior ballistic performance and provided a modern direction for interior ballistic propelling charge design of guided projectile.

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