Improved Artificial Bee Colony Algorithm For Optimal Operation Of Cascade Reservoirs

With the development of our country's hydropower industry,more and more hydropower stations have been exploited,and the cascade reservoirs have become the most common water conservancy project.It has become one of the core contents of the management of water resources that how to plan the cascade reservoir groups reasonably to improve the power generation,and is very important to alleviate the current situation of energy shortage.Therefore,it has great academic significance and application value to study the optimal operation of cascade reservoirs and make the dispatching rules.The optimization of reservoir operation is a nonlinear problem with multi-period and multi-constraints.Although the traditional scheduling method can solve the problem of single reservoir optimization,it will fall into the “dimension disaster” with the increased dimensional number of the optimization problem.With the rapid development of intelligent optimization algorithms,a novel and effective way is provided for the optimal scheduling problem of cascade reservoirs.For its simplicity and good performance,artificial bee colony algorithm has been utilized to solve many kinds of problems to real world.However,there are still many deficiencies in the original ABC algorithm.In this paper,the artificial bee colony algorithm is the research object,and the cascade reservoir scheduling is the target,the main results are as follows:(1)For overcoming the shortcomings of original ABC algorithm,such as slow convergence,the concept of the special center is introduced and improved.Firstly,the improve special center is comprised of a few of individuals which their fitnesses is above average.Compare the improve special center with the current gbest,chooses better one and guides the colony convergence.Secondly,make sure employed bees search are always around the current gbest.It will strengthen ability of developing the hidden solution and improves the accuracy of solution algorithm.In this way,we propose an Artificial Bee Colony Algorithm with Improved Special Center.(2)ABC would easily fall into local optimum when its convergence speed is accelerated.Therefore,we introduced path of transmission vector in ABC.The whole population is divided into several subpopulations and takes different ways to build transmission vector.The novel search mechanisms are designed to facilitate the exchange of information in each iteration between different subpopulations.We propose the comprehensive learning artificial bee colony algorithm.(3)For improving the performance of the single evolutionary model which leads to the imbalance of searching ability of the algorithm,multiple population strategy is adopted to optimize the algorithm.The employment bees are randomly divided into three subgroups which is corresponding to three evolutionary strategies respectively.It can make a goodbalance between exploration and exploitation in the different stages because of the different characteristics which three kinds of search strategies have.We propose an improved multi-strategy artificial bee colony algorithm.In this paper,three kinds of improved algorithm are proposed,12 classical benchmark functions and 28 CEC2013 test functions of simulation results show that,the three algorithms proposed have better search efficiency and search precision.Finally,this paper takes the cascade reservoirs in Qingjiang Basin(Shuibuya-GeheyanGaobazhou)as research objects,and takes the maximum generating capacity of cascade hydropower stations as the objective function.The combined operation model of cascade reservoirs is established,and the three improved ABC algorithms are applied to the optimal operation of cascade reservoirs in Qingjiang.