This work introduces an ant-inspired algorithm for optimization in continuous search spaces that is based on the generation of random vectors with multivariate Gaussian pdf. The proposed approach is called MACACO - Multivariate Ant Colony Algorithm for Continuous Optimization - and is able to simultaneously adapt all the dimensions of the random distribution employed to generate the new individuals at each iteration. In order to analyze MACACO's search efficiency, the approach was compared to a pair of counterparts: the Continuous Ant Colony System (CACS) and the approach known as Ant Colony Optimization in Rn (ACOR). The comparative analysis, which involves wellknown benchmark problems from the literature, has indicated that MACACO outperforms CACS and ACOR in most cases as the quality of the final solution is concerned, and it is just about two times more costly than the least expensive contender. Copyright 2008 ACM.916Bilchev, G., Parmee, I.C., The ant colony metaphor for searching continuous design spaces (1995) Lecture Notes in Computer Science, 993, pp. 25-39. , T. C. Fogarty, editor, Evolutionary Computing, AISB Workshop, of, SpringerBox, G.E.P., Muller, M.A., A note on the generation of random normal deviates (1958) Annals. Math. Stat, 29, pp. 610-611de França, F.O., Von Zuben, F.J., de Castro, L.N., Max min ant system and capacitated p-medians: Extensions and improved solutions (2005) Informatica (Slovenia), 29 (2), pp. 163-172Dorigo, M., (1992) Optimization, Learning and Natural Algorithms, , PhD thesis, Politecnico di Milano, ItalyDorigo, M., Di Caro, G., The ant colony optimization meta-heuristic (1999) New Ideas in Optimization, pp. 11-32. , D. Corne, M. Dorigo, and F. Glover, editors, McGraw-Hill, LondonDorigo, M., Stützle, T., The ant colony optimization metaheuristic: Algorithms, applications, and advances (2003) Handbook of Metaheuristics, pp. 251-286. , F. W. Glover and G. A. Kochenberger, editors, Kluwer Academic PressDréo, J., Siarry, P., A new ant colony algorithm using the heterarchical concept aimed at optimization of multiminima continuous functions (2002) Lecture Notes in Computer Science, 2463, pp. 216-221. , M. Dorigo, G. D. Caro, and M. Sampels, editors, Ant Algorithms, of, SpringerM. Guntsch and M. Middendorf. A population based approach for ACO. In S. Cagnoni, J. Gottlieb, E. Hart, M. Middendorf, and G. Raidl, editors, Applications of Evolutionary Computing, Proceedings of Evo Workshops2002: Evo COP, EvoIASP, EvoSTim, 2279 of LNCS, pages 72-81, Kinsale, Ireland, 3-4 2002. Springer-VerlagHernádvölgyi, I.T., Generating random vectors from the multivariate normal distribution (1998), Technical Report TR-98-07, University of Ottawa, Aug. 20Marsaglia, G., Tsang, W.W., The ziggurat method for generating random variables (2000) Journal of Statistical Software, 5 (8), pp. 1-7Monmarché, N., Venturini, G., Slimane, M., On how Pachycondyla apicalis ants suggest a new search algorithm (2000) Future Generation Computer Systems, 16 (8), pp. 937-946Pourtakdoust, S.H., Nobahari, H., An extension of ant colony system to continuous optimization problems (2004) Lecture Notes in Computer Science, 3172, pp. 294-301. , M. Dorigo, M. Birattari, C. Blum, L. M. Gambardella, F. Mondada, and T. Stützle, editors, ANTS Workshop, of, SpringerShang, Y.-W., Qiu, Y.-H., A note on the extended Rosenbrock function (2006) Evolutionary Computation, 14 (1), pp. 119-126. , MarchSocha, K., Dorigo, M., Ant colony optimization for continuous domains (2006) European Journal of Operational Research, In Press, Corrected ProofStützle, T., Dorigo, M., ACO algorithms for the quadratic assignment problem (1999) New Ideas in Optimization, pp. 33-50. , D. Corne, M. Dorigo, and F. Glover, editors, McGraw-Hill, Londo
