An improved ant colony optimization algorithm and its application to electromagnetic devices designs

Based on the success in the design of a new global search procedure on the development of a novel trail updating mechanism and the introduction of an elitist strategy to available ant colony optimization (ACO) methods, an improved ACO algorithm is proposed. In order to facilitate the implementation of the search procedure, the available local search phase is simplified also. The algorithm is tested on a mathematical function and an inverse problem, and its performances are compared with those of other well designed methods.

[1]  S. L. Ho,et al.  A combined finite element-domain elimination method for minimizing torque ripples in inverter-fed AC motor drive systems , 2000 .

[2]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[3]  In-Keun Yu,et al.  Application of the ant colony search algorithm to short-term generation scheduling problem of thermal units , 1998, POWERCON '98. 1998 International Conference on Power System Technology. Proceedings (Cat. No.98EX151).

[4]  José Márcio Machado,et al.  Wavelet-Galerkin method for computations of electromagnetic fields-computation of connection coefficients , 2000 .

[5]  Nurhan Karaboga,et al.  Null steering of linear antenna arrays with use of modified touring ant colony optimization algorithm , 2002 .

[6]  Marco Dorigo,et al.  Distributed Optimization by Ant Colonies , 1992 .

[7]  Shiyou Yang,et al.  Developments of an efficient global optimal design technique – a combined approach of MLS and SA algorithm , 2002 .

[8]  S. Ho,et al.  A common Tabu search algorithm for the global optimization of engineering problems , 2001 .

[9]  Johann Dréo,et al.  A New Ant Colony Algorithm Using the Heterarchical Concept Aimed at Optimization of Multiminima Continuous Functions , 2002, Ant Algorithms.

[10]  Ian C. Parmee,et al.  The Ant Colony Metaphor for Searching Continuous Design Spaces , 1995, Evolutionary Computing, AISB Workshop.

[11]  Shiyou Yang,et al.  A self-learning simulated annealing algorithm for global optimizations of electromagnetic devices , 2000 .

[12]  Marc Gravel,et al.  Comparing an ACO algorithm with other heuristics for the single machine scheduling problem with sequence-dependent setup times , 2002, J. Oper. Res. Soc..

[13]  V. K. Jayaraman,et al.  Ant Colony Approach to Continuous Function Optimization , 2000 .