An Orthogonal Search Embedded Ant Colony Optimization Approach to Continuous Function Optimization

Ant colony optimization has been one of the most promising meta-heuristics since its appearance in early 1990s but it is specialized in discrete space optimization problems. To explore the utility of ACO in the filed of continuous problems, this paper proposes an orthogonal search embedded ACO (OSEACO) algorithm. By generating some grids in the search space and embedding an orthogonal search scheme into ACO, the search space is learned much more comprehensively with only few computation efforts consumed. Hence, solutions are obtained in higher precision. Some adaptive strategies are also developed to prevent the algorithm from trapping in local optima as well as to improve its performance. Moreover, the effectiveness of this algorithm is demonstrated by experimental results on 9 diverse test functions for it is able to obtain near-optimal solutions in all cases.