An extended EigenAnt colony system applied to the sequential ordering problem

Author

Ezzat, Ahmed ; Abdelbar, Ashraf M. ; Wunsch, Donald C.

Author_Institution

Dept. of Comput. Sci. & Eng., American Univ. in Cairo Egypt, Cairo, Egypt

fYear

2014

fDate

9-12 Dec. 2014

Firstpage

1

Lastpage

7

Abstract

The EigenAnt Ant Colony System (EAAS) model is an Ant Colony Optimization (ACO) model based on the EigenAnt algorithm. In previous work, EAAS was found to perform competitively with the Enhanced Ant Colony System (EACS) algorithm, a state-of-the-art method for the Sequential Ordering Problem (SOP). In this paper, we extend EAAS by increasing the amount of stochasticity in its solution construction procedure. In experimental results on the SOPLIB instance library, we find that our proposed method, called Probabilistic EAAS (PEAAS), performs better than both EAAS and EACS. The non-parametric Friedman test is applied to determine statistical significance.

Keywords

ant colony optimisation; eigenvalues and eigenfunctions; nonparametric statistics; order processing; statistical testing; stochastic processes; ACO model; EACS algorithm; PEAAS model; SOPLIB instance library; ant colony optimization model; eigenant algorithm; eigenant ant colony system model; enhanced ant colony system; nonparametric Friedman test; probabilistic EAAS; sequential ordering problem; solution construction procedure; statistical significance; stochasticity; Ant colony optimization; Equations; Libraries; Mathematical model; Probabilistic logic; Standards; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Swarm Intelligence (SIS), 2014 IEEE Symposium on

Conference_Location

Orlando, FL

Type

conf

DOI

10.1109/SIS.2014.7011806

Filename

7011806