论文信息 - Constrained particle swarm optimisation for sequential quadratic programming

Constrained particle swarm optimisation for sequential quadratic programming

Sequential quadratic programming (SQP) methods are commonly used to solve constrained non-linear optimisation problems. However, in recent years there has been great improvement in using evolutionary algorithms to solve non-linear optimisations problems. The difficulty has been determining a correct method for implementing evolutionary algorithm for a non-linear optimisation problem with constraints. In this paper, we are combining the strengths of the traditional SQP method with an evolutionary algorithm, particle swarm optimisation (PSO) for solving a constrained non-linear optimisation problem with equality and inequality constraints. We propose a constrained PSO algorithm be used to solve the quadratic programming (QP) subproblem within the SQP method.

Zach D. Richards

[1] Russell C. Eberhart,et al. Tracking and optimizing dynamic systems with particle swarms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[2] Rainer Laur,et al. Stopping Criteria for a Constrained Single-Objective Particle Swarm Optimization Algorithm , 2007, Informatica.

[3] Kamran Behdinan,et al. Particle Swarm Optimization in Structural Design , 2007 .

[4] Russell C. Eberhart,et al. A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[5] P. Gill,et al. Some theoretical properties of an augmented lagrangian merit function , 1986 .

[6] Russell C. Eberhart,et al. Solving Constrained Nonlinear Optimization Problems with Particle Swarm Optimization , 2002 .

[7] P. Gill,et al. Large-scale SQP methods and their application in trajectory optimization , 1994 .