| The optimization problems involve a wide range of fields,including image processing,transportation,and so on.Metaheuristic algorithm is a modern method to solve increasingly complex optimization problems,which can achieve a balance between accuracy and time consumption.The Vehicle Routing Problem is an optimization problem in the field of transportation,and the traditional methods are difficult to solve,so it tends to use metaheuristic algorithm.In this paper,some metaheuristic algorithms are improved and applied to some variants of the Vehicle Routing Problem.The specific research contents are as follows:1.Aiming at the disadvantages of Flower Pollination Algorithm such as poor global exploration ability,we propose an Improved Flower Pollination Algorithm.In the global pollination stage,we introduce the median pollen disturbance to enhance the global search ability of the algorithm,and design the adjustment mechanism of switching probability to change the proportion of global exploration and local exploitation.The CEC2013 benchmark function is used to test the performance of the proposed algorithm.Finally,the proposed algorithm is applied to solve the Capacitated Vehicle Routing Problem.2.Aiming at the shortcomings of the Phasmatodea Population Evolution algorithm,such as low convergence accuracy and long running time,an Advanced Phasmatodea Population Evolution algorithm is proposed,in which the competition mechanism,the conditional acceptance and the corresponding three parameter updates are deleted,which shorten the running time of the algorithm.The added jumping mechanism makes the algorithm more likely to jump out of the local optimal solution.The added history-based searching method effectively leverages the historical information of the iteration.The added population approach movement method can improve the early exploration ability of the algorithm.Then,we use the CEC2013 benchmark functions to test the algorithm.Finally,we use the proposed algorithm to solve the Capacitated Vehicle Routing Problem and compare it with some existing work.3.In view of the disadvantages of the Equilibrium Optimizer,such as poor exploration ability and easy to fall into local optimum,an Advanced Equilibrium Optimizer algorithm is proposed,which introduces multi-population method,novel quantum operator and pollination operator inspired by Flower Pollination Algorithm.Multi-population method reduces the possibility of falling into local optimum.The novel quantum operator effectively improves the exploration ability of the algorithm.Pollination operator improves the convergence performance of the algorithm.Then,the CEC2013 benchmark functions are used to test the algorithm.Finally,the proposed algorithm is used to solve the Electric Vehicle Routing Problem with Time Windows.4.Road attributes such as the road height limit is a restriction on the height of the vehicle,can protect the facilities in front,protect the driver.However,road attributes are rarely studied in the Electric Vehicle Routing Problem,so we put forward an Electric Vehicle Routing Problem with Time Window and Road Attributes model,which considers road attributes,such as road safety factor,traffic performance index,road height limit,and road speed limit.5.Aiming at the shortcomings of the Equilibrium Optimizer,such as poor exploration and exploitation performance,a Novel Enhanced Equilibrium Optimizer was proposed.The solutions are divided into dominant solutions,general solutions,and inferior solutions according to the fitness value.A strategy is proposed to update the dominant solution independently,which enhanced the exploitation ability of the dominant solution.A strategy to update the inferior solution independently is proposed to enhance the exploration performance of the inferior solution.Then,the CEC2013 benchmark function is used to test the performance of the proposed algorithm and two update strategies.Finally,the proposed algorithm is used to solve the proposed model effectively. |
