A dynamic-edge ACS algorithm for continuous variables problems

Ant colony systems (ACS) have been successfully applied to solving optimization problems. Especially, they are efficient and effective in finding nearly optimal solutions to discrete search spaces. When the solution spaces of the problems to be solved are continuous, it is not so appropriate to use the original ACS to solve it. This paper thus proposes a dynamic-edge ACS algorithm for solving continuous variables problems. It can dynamically generate edges between two nodes and give a pheromone measures for them in a continuous solution space through distribution functions. In addition, it maps the encoding representation and the operators of the original ACS into continuous spaces easily. The proposed algorithm thus provides a simple and standard approach for applying ACS to a problem that has a continuous solution space, and retains the original process and characteristics of the traditional ACS. Several constrained functions are also evaluated to demonstrate the performance of the proposed algorithm.