An Improved Particle Swarm Algorithm for Solving Nonlinear Constrained Optimization Problems

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.

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A New Particle Swarm Algorithm for Solving Constrained Optimization Problems

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.734-737.2875 ◽

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.

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Hybridizing Particle Swarm Optimization with Invasive Weed Optimization for Solving Nonlinear Constrained Optimization Problems

Advances in Intelligent Systems and Computing - Proceedings of Fourth International Conference on Soft Computing for Problem Solving ◽

10.1007/978-81-322-2220-0_49 ◽

2014 ◽

pp. 599-610 ◽

Cited By ~ 1

Author(s):

A. K. Ojha ◽

Y. Ramu Naidu

Keyword(s):

Particle Swarm Optimization ◽

Constrained Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Invasive Weed Optimization ◽

Constrained Optimization Problems ◽

Invasive Weed ◽

Swarm Optimization ◽

Nonlinear Constrained Optimization

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OPTIMIZATION OF NONLINEAR CONSTRAINED PARTICLE SWARM

Technological and Economic Development of Economy ◽

10.3846/13928619.2006.9637719 ◽

2006 ◽

Vol 12 (1) ◽

pp. 30-36 ◽

Cited By ~ 11

Author(s):

António Ismael de Freitas Vaz ◽

Edite Manuela da Graça Pinto Fernandes

Keyword(s):

Multiobjective Optimization ◽

Constrained Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Numerical Results ◽

Equality Constraints ◽

Stopping Criterion ◽

Constrained Optimization Problems ◽

Nonlinear Constrained Optimization ◽

Particle Position

We propose an algorithm based on the particle swarm paradigm (PSP) to address nonlinear constrained optimization problems. While some algorithms based on PSP have already been proposed in this context, the equality constraints have been posing some difficulties. The proposed algorithm is based on the relaxation of the dominance concept introduced in the multiobjective optimization. This concept is used to select the best particle position and the best ever particle swarm position. We propose also a stopping criterion for the algorithm and present numerical results with some problems collected from the literature. The new algorithm is implemented in a solver connected with AMPL, allowing easy coding and solving of problems.

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