A particle swarmaBFGS algorithm for nonlinear programming problems

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...

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Bibliographic Details
Published in:Computers & operations research 2013-04, Vol.40 (4), p.963-972
Main Authors: Nezhad, Ali, Shandiz, Roohollah, Jahromi, Abdolhamid
Format: Article
Language:English
Subjects:
Algorithms
Convergence
Dealing
Exploration
Nonlinear programming
Nonlinearity
Operations research
Optimization
Swarm intelligence
Online Access:Get full text
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Summary: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.
ISSN:0305-0548
DOI:10.1016/j.cor.2012.11.008