2 research outputs found
Orthogonal methods based ant colony search for solving continuous optimization problems
Research into ant colony algorithms for solving continuous optimization problems forms one of the most
significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial
optimization, they have shown great potential in solving a wide range of optimization problems, including continuous
optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for
solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore
their chosen regions rapidly and e±ciently. By implementing an "adaptive regional radius" method, the proposed
algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is
compared with two other ant algorithms for continuous optimization of API and CACO by testing seventeen functions
in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others