Engineering optimization with particle swarm

2004 ◽  
Author(s):  
Xiaohui Hu ◽  
R.C. Eberhart ◽  
Yuhui Shi
Keyword(s):  
Particle Swarm ◽  
Engineering Optimization

Hypothesis testing-based adaptive PSO

2014 ◽  
Vol 12 (1) ◽  
pp. 89-101 ◽  
Author(s):  
Yanxia Sun ◽  
Karim Djouani ◽  
Barend Jacobus van Wyk ◽  
Zenghui Wang ◽  
Patrick Siarry
Keyword(s):  
Particle Swarm Optimization ◽  
Hypothesis Testing ◽  
Local Minimum ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Method ◽  
Engineering Optimization ◽  
Swarm Optimization ◽  
Content Type ◽  
Adaptive Pso

Purpose – In this paper, a new method to improve the performance of particle swarm optimization is proposed. Design/methodology/approach – This paper introduces hypothesis testing to determine whether the particles trap into the local minimum or not, then special re-initialization was proposed, finally, some famous benchmarks and constrained engineering optimization problems were used to test the efficiency of the proposed method. In the revised manuscript, the content was revised and more information was added. Findings – The proposed method can be easily applied to PSO or its varieties. Simulation results show that the proposed method effectively enhances the searching quality. Originality/value – This paper proposes an adaptive particle swarm optimization method (APSO). A technique is applied to improve the global optimization performance based on the hypothesis testing. The proposed method uses hypothesis testing to determine whether the particles are trapped into local minimum or not. This research shows that the proposed method can effectively enhance the searching quality and stability of PSO.

Engineering Optimization Algorithm based on Particle Swarm Optimization

2013 ◽  
Vol 5 (3) ◽  
pp. 113-121 ◽  
Author(s):  
Xuesong Yan ◽  
Hong Yao ◽  
Qingzhong Liang ◽  
Chengyu Hu ◽  
Yuanyuan Fan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Engineering Optimization ◽  
Swarm Optimization

Particle Swarm Optimization with Adaptive Inertia Weight and its Application in Optimization Design

2010 ◽  
Vol 97-101 ◽  
pp. 3484-3488 ◽  
Author(s):  
Xiao Lei Wang ◽  
Yu Yang ◽  
Qiang Zeng ◽  
Jin Qiang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Engineering Optimization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Convergence Performance ◽  
Basic Particle ◽  
Dynamic Adaptability ◽  
Evolution Speed

To avoid the premature convergence caused by basic particle swarm optimization(PSO) in resolving engineering optimization design of highly complex and nonlinear constraints, a new particle swarm optimization algorithm with adaptive inertia weight (AIW-PSO) is proposed. In this algorithm, inertia weight is adaptively changed according to the current evolution speed and aggregation degree of the swarm, which provides the algorithm with dynamic adaptability, enhances the search ability and convergence performance of the algorithm. Moreover, penalty function is used to eliminate the constraints. Finally, the validity of AIW-PSO is verified through an optimization example.

An improved PSO algorithm for solving nonlinear programing problems with constrained conditions

2020 ◽  
pp. 2150001
Author(s):  
Wei-Der Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Engineering Optimization ◽  
Swarm Optimization ◽  
Design Variables ◽  
Constrained Conditions ◽  
Engineering Optimization Problems

Engineering optimization problems can be always classified into two main categories including the linear programming (LP) and nonlinear programming (NLP) problems. Each programming problem further involves the unconstrained conditions and constrained conditions for design variables of the optimized system. This paper will focus on the issue about the design problem of NLP with the constrained conditions. The employed method for such NLP problems is a variant of particle swarm optimization (PSO), named improved particle swarm optimization (IPSO). The developed IPSO is to modify the velocity updating formula of the algorithm to enhance the search ability for given optimization problems. In this work, many different kinds of physical engineering optimization problems are examined and solved via the proposed IPSO algorithm. Simulation results compared with various optimization methods reported in the literature will show the effectiveness and feasibility for solving NLP problems with the constrained conditions.

An Improved Particle Swarm Optimization Algorithm for Constrained Chemical Engineering Optimization

2014 ◽  
Vol 962-965 ◽  
pp. 746-750
Author(s):  
Jian Chun Yang ◽  
Wen Long
Keyword(s):  
Particle Swarm Optimization ◽  
Chemical Engineering ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Engineering Optimization ◽  
Test Functions ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Mutation Strategy

An improved particle swarm optimization (IPSO) is proposed for solving constrained numerical and engineering optimization problems in this paper. In proposed algorithm, an initialization strategy based on the opposition learning is applied to diversity the initial particles in the search space. Self-adaptive inertia weight is introduced to balance the ability of exploration and exploitation. Diversity mutation strategy is employed for best of particles to introduce diversity in the swarm space. Simulation results and comparisons with other algorithms using two benchmark constrained test functions and chemical engineering optimization problem are provided.

Sign in / Sign up

Export Citation Format

Share Document