Dynamic-window-search Ant Colony Optimization for Complex Multi-stage Decision Making Problems

A dynamic-window-search ant colony optimization(ACO)algorithm,integratedwith genetic optimization techniques,is proposed for large-scale multi-stage decision mak-ing problems,which are of strong nonlinearity,complex constraints on system states andcontrol inputs,non-analytical system representation,and additive and monotonic objectivefunctions.A subset of the feasible decision set at each stage is dynamically selected for the al-gorithm by real-coded genetic optimization and is mapped to the nodes of the correspondinglayer in a layered construction graph to reduce the size of the search space.Computationalcomplexity analysis and simulation results demonstrate that,in comparison with basic ACOalgorithms,the proposed algorithm greatly improves the computational efficiency.