Extended ant colony optimization for non-convex mixed integer nonlinear programming

Authors:
Martin Schlüter;Jose A. Egea;Julio R. Banga
Affiliations:
Department of Computer Science, University of Bayreuth, 95440 Bayreuth, Germany;Process Engineering Group, Instituto de Investigaciones Marinas (IIM-CSIC), 36208 Vigo, Spain;Process Engineering Group, Instituto de Investigaciones Marinas (IIM-CSIC), 36208 Vigo, Spain
Venue:
Computers and Operations Research
Year:
2009

Citing 7
Cited 5

An outer-approximation algorithm for a class of mixed-integer nonlinear programs

Mathematical Programming: Series A and B
Ant algorithms for discrete optimization

Artificial Life
Test Examples for Nonlinear Programming Codes

Test Examples for Nonlinear Programming Codes
Ant Colony Optimization

Ant Colony Optimization
Scatter search for chemical and bio-process optimization

Journal of Global Optimization
Mixed Ant Colony Optimization for the Unit Commitment Problem

ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Application of ACO in continuous domain

ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II

The oracle penalty method

Journal of Global Optimization
A heuristic method for the inventory routing and pricing problem in a supply chain

Expert Systems with Applications: An International Journal
A dynamic convexized method for nonconvex mixed integer nonlinear programming

Computers and Operations Research
pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization

Structural and Multidisciplinary Optimization
Optimal camera placement to measure distances regarding static and dynamic obstacles

International Journal of Sensor Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

Two novel extensions for the well known ant colony optimization (ACO) framework are introduced here, which allow the solution of mixed integer nonlinear programs (MINLPs). Furthermore, a hybrid implementation (ACOmi) based on this extended ACO framework, specially developed for complex non-convex MINLPs, is presented together with numerical results. These extensions on the ACO framework have been developed to serve the needs of practitioners who face highly non-convex and computationally costly MINLPs. The performance of this new method is evaluated considering several non-convex MINLP benchmark problems and one real-world application. The results obtained by our implementation substantiate the success of this new approach.