Research On Imp Roved Artificial Bee Colony Algorithm And Its Application In Optimal Allocation Of Water Resources

Artificial bee colony algorithm is a new swarm intelligence algorithm.Because of its advantages of easy implementation,few parameters and strong global search ability,it has attracted wide attention.But ABC algorithm still has many shortcomings.In the face of multimodal problems,the convergence speed is slow and it is easy to get into local optimal.In the face of multi-objective optimization problems,it is difficult to obtain well-distributed solutions.In order to solve these problems,this paper,on the one hand,improves the original operation mechanism of ABC algorithm to enhance the ability of ABC algorithm to solve multi-modal problems,and on the other hand,introduces other mechanisms to enhance the ability of ABC algorithm to solve multi-objective optimization problems.The main work of this paper is as follows:(1)A new ABC variant based on multiple search strategies and dimension selection(ABC-MSDS)is proposed.Firstly,multiple search strategies based on dual strategy pool are designed.Compared to other existing ABC with multiple search strategies,our approach constructs two strategy pools for employed and onlooker bees,respectively.Secondly,a new dimension selection method is used to replace the random dimension selection in the standard ABC.In the search process,each dimension is chosen one by one in terms of the quality of offspring.Finally,a modified scout bee phase is employed to accelerate the search.Experimental study is conducted on classical benchmark problems and CEC 2013 shifted and rotated problems.The performance of ABC-MSDS is compared with several recently published ABC variants.Computational results demonstrate the effectiveness of our approach.(2)A novel ABC with adaptive neighborhood search and Gaussian perturbation(called ABCNG)is proposed.Firstly,an adaptive method is used to dynamically adjust the neighborhood size.Then,a modified global best solution guided search strategy is constructed based on the neighborhood.Finally,a new Gaussian perturbation with evolutionary rate is designed to evolve the unchanged solutions in each iteration.Performance of ABCNG is tested on two benchmark sets and compared with some excellent ABC variants.Results show that ABCNG is more competitive than six other ABCs.(3)A new multi-objective artificial bee colony algorithm based on reference point and opposition(called ROMOABC)is proposed.Firstly,the original framework of artificial bee colony(ABC)is modified to improve the efficiency of population renewal and accelerate the convergence rate.On basis of this framework,two new strategies are proposed.in scout bee,opposition-based learning and elite solutions are used to reduce the waste of computing resources.Distribution of solutions is improved by using the reference point associated external archive.Experiments are conducted on sixteen ZDT,DTLZ and WFG multi-objective benchmark functions.The comparison of ROMOABC with five other multi-objective algorithms shows that it has competitive convergence and diversity.(4)Aiming at water supply benefit,regional water shortage and pollutant discharge,the model of optimal allocation of water resources in Nanchang city was established.Set 2020 as the year of horizontal planning to optimize the allocation of water resources.Firstly,the water demand prediction model of Nanchang city is established to predict the water demand of Nanchang city in 2020.Taking the projected water demand of Nanchang in 2020 as the water demand of Nanchang in 2020,the multi-objective ABC algorithm ROMOABC was adopted to optimize the regional water resource allocation of Nanchang in 2020.The results show that ROMOABC is effective in solving the optimal allocation of water resources in Nanchang city.