Study of Available Transfer Capability based on Hybrid Continuous Ant Colony Optimization

This paper presents a deterministic and non-linear model of available transfer capability (abr. ATQ) computation, which is based on hybrid continuous ant colony optimization (abr. HCACO). The algorithm combines the positive feedback of ant colony optimization (abr. ACO) strategy with evolutionary strategy of float genetic algorithm (abr. GA) and introduces pattern search method as eugenic strategy, schedules the behaviors of global ants and local ants, and coordinates their actions via pheromone, which well balance the exploration and exploitation of algorithm and enhance the optimization efficiency, global convergence performance and stability of result, meanwhile, according to the amount overrunning the limit of inequality constraints during the computing process, uses non- stationary multi-stage assignment penalty function to simplify the inequality constrains, adopts forced searching strategy to further increase the convergence speed and global optimization results. The verification results by IEEE-30 bus system show the rationality and availability of algorithm.