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Flying ant colony optimization algorithm for combinatorial optimization

Fidanova S., Atanassov K.

Studia Informatica

2017

Vol. 38, nr 4

31--40

In this paper is introduce "flying" ants in Ant Colony Optimization (ACO). In traditional ACO algorithms the ants construct their solution regarding one step forward. In proposed ACO algorithm, the ants make their decision, regarding more than one step forward, but they include only one new element in their solutions.

Artykuł przedstawia "latające" mrówki w problemie optymalizacji algorytmów mrówkowych. W tradycyjnych podejściach dla algorytmów mrówkowych agenci (mrówki) budują swoje rozwiązanie w kolejnych krokach. W zaproponowanym podejściu optymalizacji algorytmu mrówkowego agenci podejmują decyzję na podstawie więcej niż jednego kroku, jednakże tylko jeden element wprowadzany jest do rozwiązania.

New evaluations of ant colony optimization start nodes

Fidanova S., Marinov P., Atanassov K.

Control and Cybernetics

2014

Vol. 43, no. 3

471--485

Ant Colony Optimization (ACO) is a stochastic search method that mimics the social behavior of real ant colonies, managing to establish the shortest route to the feeding sources and back. Such algorithms have been developed to arrive at near-optimal solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. In this paper, the semi-random start procedure is applied. A new kind of evaluation of start nodes of the ants is developed and several starting strategies are prepared and combined. The idea of semi-random start is related to a better management of the ants. This new technique is tested on the Multiple Knapsack Problem (MKP). A Comparison among the strategies applied is presented in terms of quality of the results. A comparison is also carried out between the new evaluation and the existing one. Based on this comparative analysis, the performance of the algorithm is discussed. The study presents the idea that should be beneficial to both practitioners and researchers involved in solving optimization problems.