A New Particle Swarm Algorithm for Solving Constrained Optimization Problems

2013 ◽  
Vol 734-737 ◽  
pp. 2875-2879
Author(s):  
Tie Bin Wu ◽  
Yun Cheng ◽  
Yun Lian Liu ◽  
Tao Yun Zhou ◽  
Xin Jun Li
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Constrained Optimization Problems ◽  
Particle Population ◽  
Point Set ◽  
Good Point Set ◽  
Improved Pso

Considering that the particle swarm optimization (PSO) algorithm has a tendency to get stuck at the local solutions, an improved PSO algorithm is proposed in this paper to solve constrained optimization problems. In this algorithm, the initial particle population is generated using good point set method such that the initial particles are uniformly distributed in the optimization domain. Then, during the optimization process, the particle population is divided into two sub-populations including feasible sub-population and infeasible sub-population. Finally, different crossover operations and mutation operations are applied for updating the particles in each of the two sub-populations. The effectiveness of the improved PSO algorithm is demonstrated on three benchmark functions.

scholarly journals A Framework for Constrained Optimization Problems Based on a Modified Particle Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Biwei Tang ◽  
Zhanxia Zhu ◽  
Jianjun Luo
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Relaxation Method ◽  
Adaptive Strategy ◽  
Pso Algorithm ◽  
Constrained Optimization Problems ◽  
Solution Quality ◽  
Swarm Optimization

This paper develops a particle swarm optimization (PSO) based framework for constrained optimization problems (COPs). Aiming at enhancing the performance of PSO, a modified PSO algorithm, named SASPSO 2011, is proposed by adding a newly developed self-adaptive strategy to the standard particle swarm optimization 2011 (SPSO 2011) algorithm. Since the convergence of PSO is of great importance and significantly influences the performance of PSO, this paper first theoretically investigates the convergence of SASPSO 2011. Then, a parameter selection principle guaranteeing the convergence of SASPSO 2011 is provided. Subsequently, a SASPSO 2011-based framework is established to solve COPs. Attempting to increase the diversity of solutions and decrease optimization difficulties, the adaptive relaxation method, which is combined with the feasibility-based rule, is applied to handle constraints of COPs and evaluate candidate solutions in the developed framework. Finally, the proposed method is verified through 4 benchmark test functions and 2 real-world engineering problems against six PSO variants and some well-known methods proposed in the literature. Simulation results confirm that the proposed method is highly competitive in terms of the solution quality and can be considered as a vital alternative to solve COPs.

Particle Swarm Algorithm Based on Boundary Buffering-Natural Evolution and its Application in Constrained Optimization

2014 ◽  
Vol 670-671 ◽  
pp. 1517-1521
Author(s):  
Tie Bin Wu ◽  
Tao Yun Zhou ◽  
Wen Li ◽  
Gao Feng Zhu ◽  
Yun Lian Liu
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Natural Evolution ◽  
Constrained Optimization Problems ◽  
Simulation Results ◽  
Swarm Algorithm ◽  
Mutation Strategies

A particle swarm algorithm (PSO) based on boundary buffering-natural evolution was proposed for solving constrained optimization problems. By buffering the particles that cross boundaries, the diversity of populations was intensified; to accelerate the convergence speed and avoid local optimum of PSO, natural evolution was introduced. In other words, particle hybridization and mutation strategies were applied; and by combining the modified feasible rules, the constrained optimization problems were solved. The simulation results proved that the method was effective in solving this kind of problems.

Sign in / Sign up

Export Citation Format

Share Document