book chapter · InTech eBooks · 2011
Continuous Dynamic Optimization
Cited by 2Open access
Abstract
In this chapter we introduce a new ant colony algorithm aimed at continuous and dynamic problems. To deal with the changes in the dynamic problems, the diversification in the ant population is maintained by attributing to every ant a repulsive electrostatic charge, that allows to keep ants at some distance from each other. The algorithm is based on a continuous ant colony algorithm that uses a weighted continuous Gaussian distribution, instead of the discrete distribution, used to solve discrete problems. Experimental results and comparisons with two competing methods available in the literature show best performances of our new algorithm called CANDO on a set of multimodal dynamic continuous test functions. To find the shortest way between the colony and a source of food, ants adopt a particular collective organization technique (see figure 1). The first algorithm inspired from ant colonies, called ACO (refer to Dorigo & Gambardella (1997), Dorigo & Gambardella (2002)), was proposed as a multi-agent approach to solve hard combinatorial optimization problems. It was applied to discrete problems like the traveling salesman, routing and communication problems.
Cite this paper
Tfaili, W. (2011). Continuous Dynamic Optimization. InTech eBooks. https://doi.org/10.5772/14794
Tfaili, Walid. “Continuous Dynamic Optimization.” InTech eBooks, 2011, https://doi.org/10.5772/14794.
Tfaili, Walid. 2011. “Continuous Dynamic Optimization.” InTech eBooks. https://doi.org/10.5772/14794.
Tfaili, W. (2011) ‘Continuous Dynamic Optimization’, InTech eBooks. Available at: https://doi.org/10.5772/14794.
@incollection{tfaili2011continuous,
title = {{Continuous Dynamic Optimization}},
author = {Tfaili, Walid},
booktitle = {InTech eBooks},
year = {2011},
doi = {10.5772/14794}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.