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A particle swarm-BFGS algorithm for nonlinear programming problems

Mohammad Nezhad, A ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1016/j.cor.2012.11.008
  3. Publisher: 2013
  4. Abstract:
  5. This article proposes a hybrid optimization algorithm based on a modified BFGS and particle swarm optimization to solve medium scale nonlinear programs. The hybrid algorithm integrates the modified BFGS into particle swarm optimization to solve augmented Lagrangian penalty function. In doing so, the algorithm launches into a global search over the solution space while keeping a detailed exploration into the neighborhoods. To shed light on the merit of the algorithm, we provide a test bed consisting of 30 test problems to compare our algorithm against two of its variations along with two state-of-the-art nonlinear optimization algorithms. The numerical experiments illustrate that the proposed algorithm makes an effective use of hybrid framework when dealing with nonlinear equality constraints although its convergence cannot be guaranteed
  6. Keywords:
  7. Active set SQP algorithm ; Augmented Lagrangian penalty function ; Interior/CG algorithm ; Augmented Lagrangians ; BFGS algorithm ; Equality constraints ; Global search ; Hybrid algorithms ; Hybrid framework ; Hybrid optimization algorithm ; Non-linear optimization algorithms ; Nonlinear programming problem ; Nonlinear programs ; Numerical experiments ; Penalty function ; Solution space ; SQP algorithm ; Test problem ; Equipment testing ; Lagrange multipliers ; Nonlinear programming ; Particle swarm optimization (PSO) ; Quadratic programming ; Algorithms
  8. Source: Computers and Operations Research ; Volume 40, Issue 4 , April , 2013 , Pages 963-972 ; 03050548 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0305054812002523