Abstract: Research into ant colony algorithms for solvingcontinuous optimization problems forms one of the most significant andpromising areas in swarm computation. Although traditional antalgorithms are designed for combinatorial optimization, they have showngreat potential in solving a wide range of optimization problems,including continuous optimization. Aimed at solving continuous problemseffectively, this paper develops a novel ant algorithm termed``continuous orthogonal ant colony'' (COAC), whose pheromone depositmechanisms would enable ants to search for solutions collaborativelyand effectively. By using the orthogonal design method, ants in thefeasible domain can explore their chosen regions rapidly andefficiently. By implementing an ``adaptive regional radius'' method, theproposed algorithm can reduce the probability of being trapped in localoptima and therefore enhance the global search capability and accuracy.An elitist strategy is also employed to reserve the most valuablepoints. The performance of the COAC is compared with two other antalgorithms for continuous optimization --- API and CACO by testingseventeen functions in the continuous domain. The results demonstratethat the proposed COAC algorithm outperforms the others.