Research on Continuous Function Optimization Algorithm Based on Swarm-Intelligence

On the basis of the analyses of ant colony optimization (ACO) and particle swarm optimization (PSO), continuous ant-particle swarm optimization (CA-PSO) applied in continuous function optimization is proposed. After the space partition is properly employed, ACO is applied to search the sensitive areas through the whole solution space. And then PSO is initialized according to ACO search results and applied to search the optimal solution in every area. After every iteration, swarm renovation is processed following ant-colony topological structure and transfer rules. As a result, the ants gradually assemble in the areas where optimal solution is mostly likely to be. With the number growth of particles in the sensitive areas, local PSO can search more meticulously until find the optimal solution. In conclusion, the CA-PSO can not only ensure the distributing diversity of the solution, but also avoid suboptimal solution when it is applied in the multimodal function optimization.