An augmented Lagrangian fish swarm based method for global optimization

Authors:
Ana Maria A. C. Rocha;Tiago F. M. C. Martins;Edite M. G. P. Fernandes
Affiliations:
Department of Production and Systems, University of Minho, 4710-057 Braga, Portugal;Algorithm R & D Center, Portugal;Department of Production and Systems, University of Minho, 4710-057 Braga, Portugal
Venue:
Journal of Computational and Applied Mathematics
Year:
2011

Citing 17
Cited 6

A globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds

SIAM Journal on Numerical Analysis
Lagrange multipliers and optimality

SIAM Review
Convergence Properties of an Augmented Lagrangian Algorithm for Optimization with a Combination of General Equality and Linear Constraints

SIAM Journal on Optimization
A Globally Convergent Augmented Lagrangian Pattern Search Algorithm for Optimization with General Constraints and Simple Bounds

SIAM Journal on Optimization
On the Convergence of a Population-Based Global Optimization Algorithm

Journal of Global Optimization
Numerical Comparison of Augmented Lagrangian Algorithms for Nonconvex Problems

Computational Optimization and Applications
Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm

Information Processing Letters
Augmented Lagrangian methods under the constant positive linear dependence constraint qualification

Mathematical Programming: Series A and B
A filled function method for constrained global optimization

Journal of Global Optimization
On the Convergence of Augmented Lagrangian Methods for Constrained Global Optimization

SIAM Journal on Optimization
On Augmented Lagrangian Methods with General Lower-Level Constraints

SIAM Journal on Optimization
Convergence properties of augmented Lagrangian methods for constrained global optimization

Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART I
DESA: a new hybrid global optimization method and its application to analog integrated circuit sizing

Journal of Global Optimization
Solving nonlinearly constrained global optimization problem via an auxiliary function method

Journal of Computational and Applied Mathematics
Ant colony method to control variance reduction techniques in the Monte Carlo simulation of clinical electron linear accelerators of use in cancer therapy

Journal of Computational and Applied Mathematics
Global minimization using an Augmented Lagrangian method with variable lower-level constraints

Mathematical Programming: Series A and B
An artificial fish swarm algorithm based and ABC supported qos unicast routing scheme in NGI

ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking

An artificial fish swarm filter-based method for constrained global optimization

ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
Solving multidimensional 0---1 knapsack problem with an artificial fish swarm algorithm

ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
A genetic algorithm based augmented Lagrangian method for constrained optimization

Computational Optimization and Applications
A hybrid differential evolution augmented Lagrangian method for constrained numerical and engineering optimization

Computer-Aided Design
An artificial fish swarm algorithm based hyperbolic augmented Lagrangian method

Journal of Computational and Applied Mathematics
A simplified binary artificial fish swarm algorithm for 0-1 quadratic knapsack problems

Journal of Computational and Applied Mathematics

Quantified Score

Hi-index	7.29

Visualization

Abstract

This paper presents an augmented Lagrangian methodology with a stochastic population based algorithm for solving nonlinear constrained global optimization problems. The method approximately solves a sequence of simple bound global optimization subproblems using a fish swarm intelligent algorithm. A stochastic convergence analysis of the fish swarm iterative process is included. Numerical results with a benchmark set of problems are shown, including a comparison with other stochastic-type algorithms.