An incremental ant colony algorithm with local search for continuous optimization

Authors:
Tianjun Liao;Marco A. Montes de Oca;Dogan Aydin;Thomas Stützle;Marco Dorigo
Affiliations:
IRIDIA, CoDE, Universite Libre de Bruxelles, Brussels, Belgium;IRIDIA, CoDE, Universite Libre de Bruxelles, Brussels, Belgium;Dept. of Computer Engineering, Ege University, Izmir, Turkey;IRIDIA, CoDE, Universite Libre de Bruxelles, Brussels, Belgium;IRIDIA, CoDE, Universite Libre de Bruxelles, Brussels, Belgium
Venue:
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Year:
2011

Citing 13
Cited 8

On how pachycondyla apicalis ants suggest a new search algorithm

Future Generation Computer Systems
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
On the Adaptation of Arbitrary Normal Mutation Distributions in Evolution Strategies: The Generating Set Adaptation

Proceedings of the 6th International Conference on Genetic Algorithms
A Method for Solving Optimization Problems in Continuous Space Using Ant Colony Algorithm

ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant Colony Optimization

Ant Colony Optimization
Continuous interacting ant colony algorithm based on dense heterarchy

Future Generation Computer Systems - Special issue: Computational chemistry and molecular dynamics
An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training

Neural Computing and Applications
Towards incremental social learning in optimization and multiagent systems

Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Improvement strategies for the F-Race algorithm: sampling design and iterative refinement

HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
An alternative ACOR algorithm for continuous optimization problems

ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems

Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
An incremental particle swarm for large-scale continuous optimization problems: an example of tuning-in-the-loop (re)design of optimization algorithms

Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Incremental Social Learning in Particle Swarms

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Tuning parameters across mixed dimensional instances: a performance scalability study of Sep-G-CMA-ES

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Learning fuzzy cognitive maps from data by ant colony optimization

Proceedings of the 14th annual conference on Genetic and evolutionary computation
DACCO: a discrete ant colony algorithm to cluster geometry optimization

Proceedings of the 14th annual conference on Genetic and evolutionary computation
An ACO algorithm benchmarked on the BBOB noiseless function testbed

Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
A centralised cooperative strategy for continuous optimisation: The influence of cooperation in performance and behaviour

Information Sciences: an International Journal
A non-adaptive stochastic local search algorithm for the CHeSC 2011 competition

LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Improving performance via population growth and local search: the case of the artificial bee colony algorithm

EA'11 Proceedings of the 10th international conference on Artificial Evolution
Solution to non-convex economic dispatch problem with valve point effects by incremental artificial bee colony with local search

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

ACOR is one of the most popular ant colony optimization algorithms for tackling continuous optimization problems. In this paper, we propose IACOR-LS, which is a variant of ACOR that uses local search and that features a growing solution archive. We experiment with Powell's conjugate directions set, Powell's BOBYQA, and Lin-Yu Tseng's Mtsls1 methods as local search procedures. Automatic parameter tuning results show that IACOR-LS with Mtsls1 (IACOR-Mtsls1) is not only a significant improvement over ACOR, but that it is also competitive with the state-of-the-art algorithms described in a recent special issue of the Soft Computing journal. Further experimentation with IACOR-Mtsls1 on an extended benchmark functions suite, which includes functions from both the special issue of Soft Computing and the IEEE 2005 Congress on Evolutionary Computation, demonstrates its good performance on continuous optimization problems.