Research On Optimal Sche-Duling Scheme Of Rural Leisure Tourist Flow Based On Gravitational Search Algorithm

In recent years,rural leisure tourism's rapid development makes it has become one of the highlights of today's tourism industry.However,the concentration of tourists and the occlusion of the spread of information have caused serious imbalance in the distribution of tourists.This problem leads to a serious imbalance between local economic and ecological benefits,and development differences between villages.Therefore,how to properly dispatch the tourists flow and attenuate the unbalance of passenger flow has become an urgent problem to be solved in the industry.Existing scenic passenger flow dispatchings mostly start from the perspective of the scenic spot operator,which select a single indicator as the constraint condition,take average distribution of passenger flow as the target,to increase the overall passenger flow capacity.To a certain extent,this kind of approach alleviates the eco-economic issues caused by the concentration of passenger flow.However,in the problem of rural leisure tourists flow scheduling,due to the scheduling decision process needs to comprehensively consider the interests of tourists,operators,regulatory authorities and so on,many constraints are brought to the optimization of the scheduling scheme.How to build the rural leisure tourism model and design scheduling optimization strategy needs to be further studied.In this thesis,the heuristic optimization algorithm,which is widely used in solving constrained optimization problems,is studied,and the gravitational search algorithm is analyzed and improved.Aiming at the problem of rural leisure tourism flow scheduling,this thesis makes concrete modeling and analysis,and proposes an iterative scheduling strategy to solve the problem.In the research of gravitational search algorithm,this thesis presents an improved revolving gravitational search algorithm.In the standard gravitational search algorithm,the selection of kbest set particles has a significant impact on the algorithm's optimization ability.This thesis demonstrates that the current particle selection for kbest set may limit the algorithm's optimization ability,and proposes a specific optimization scheme.In the improved algorithm,the particle complement set is constructed based on the original kbest set.Therefore,the particles not included in the kbest still have the opportunity to influence part of other agnets5 movements exclusively.Thereforce,the randomness of poor fitness particles,motion is increased and the exploration ability of the algorithm is further enhanced.On this basis,in order to deal with the constrained optimization problem,a constrained revolving gravitational search algorithm is proposed,in which an adaptive sorting criterion is combined with the revolving gravitational search algorithm.Simulation results show the effectiveness of the improved algorithm in dealing with constrained optimization problems.This thesis proposes an iterative scheduling strategy to slove the problem of passenger flow scheduling,which calculates the optimal scheduling schemes iteratively for multiple overload operating points,to find the optimal scheduling solution.Different from the common method of constructing decision variables for each parameter and proceeding optimization with heuristic optimization algorithm,the strategy constructs the utility function by factors such as the distance and the scheduling capacity.F or each optimization,the utility function is used to match the current overloaded operating points with target scheduling sites.Then the operation points are updated according to the obtained scheduling scheme,and the overall regional scheduling scheme is obtained by multiple iterations.Simulation results verify the effectiveness and reliability of the proposed scheduling strategy.