Swarm intelligence algorithms parameter tuning

Authors:
Milan Tuba
Affiliations:
Faculty of Computer Science, Megatrend University of Belgrade, N. Belgrade, Serbia
Venue:
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
Year:
2012

Citing 5
Cited 0

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
MAX-MIN Ant system

Future Generation Computer Systems
An object-oriented framework with corresponding graphical user interface for developing ant colony optimization based algorithms

WSEAS Transactions on Computers
A new minimum pheromone threshold strategy (MPTS) for max-min ant system

Applied Soft Computing
An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem

Applied Soft Computing

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Abstract

Nature inspired metaheuristic algorithms are recently successfully used to find suboptimal solutions to hard optimization problems. These algorithms mimic different nature phenomena in hope that nature's implicit intelligence will help to guide the search in untractable problems. Swarm intelligence algorithms are a class of nature inspired algorithms based on collective intelligence of colonies of ants, bees, fish etc. They have a number quantitative and qualitative parameters that can be adjusted. Such adjustments are not allowed for specific test problems but only for a whole class. When some adjustment works for number of classes it can be incorporated into the generic algorithm as a new qualitative parameter (optional modification). In this paper we describe how successful application of pheromone correction strategy for the ant colony optimization (ACO) algorithm on three different graph problems is incorporated in ACO software framework as a module.