Improvement Of JAYA Algorithm And Its Applications In Green Logistics Distributing

With the continuous increase and improvement of the quantity and demand for logistics,the logistics on the environment has become increasingly severe.The logistics and transportation industry has become one of the major sources of greenhouse gases such as carbon dioxide in the world.The vehicle distribution routing problem is becoming larger and larger,and the traditional optimization method has been difficult to solve it.When facing the problem,the intelligent algorithms have better effect.Therefore,the researchers attach great importance to the research of the intelligent algorithms.JAYA algorithm is an intelligent algorithm proposed by R.venkata Rao in 2016.It has been applied to many fields such as traveling salesman,flexible job shop scheduling and so on.However,JAYA algorithm has some shortcomings,such as unstable solution and easy to fall into local extremum.Therefore,the paper proposes two new improved algorithms,which are applied to two different sub problems of green logistics distribution problem,and the results are satisfactory.The main research work and innovations of the paper are as follows:(1)In order to better solve the complex function optimization problem,the algorithm is well applied to the electric vehicle path planning problem with time window,simultaneous pick-up and delivery and including charging station,and a hybrid evolutionary JAYA algorithm for global optimization is proposed.Firstly,the opposition-based learning is introduced to calculate the current optimal and worst individuals;Then,the sine cosine operator and differential perturbation mechanism are introduced and integrated into the individual location update;Finally,the mixed evolution strategy with different parity is adopted in the algorithm structure.Then the pseudo code of the algorithm is given.Theoretical analysis shows that the time complexity of H-JAYA(Hybrid evolutionary JAYA algorithm)is the same as the basic JAYA.The multi-dimensional function extreme value optimization test of 6 algorithms on CEC2017 test suite shows that the convergence performance and robustness of H-JAYA are significantly improved.(2)In order to further solve different types of complex function optimization and engineering constraint optimization problems,the JAYA algorithm is better applied to the fuel vehicle path planning problem with time window and simultaneous pick-up and delivery in the multi-objective low-carbon environment with more complex constraints,and the JAYA algorithm based on multi role differential evolution strategy is proposed.Firstly,the cosine similarity strategy is introduced to update the individuals with high cosine similarity with the optimal individual position.The individuals with poor fitness value are fused with the rotation transform operator to update the position,while the individuals with good fitness value are fused with the non-uniform mutation operator;Then the multi role strategy and the symbiosis strategy and cauchy mutation mechanism are introduced to the algorithm;Finally,the small hole imaging opposition-based learning strategy is introduced to the algorithm.Then the pseudo code of the algorithm is given.Theoretical analysis shows that the time complexity of M-JAYA(Modified JAYA Algorithm)is the same as the basic JAYA.Through the comparison test of multi-dimensional and multi algorithm function extreme value optimization of 12 complex standard test functions,it shows that the convergence performance and robustness of M-JAYA are significantly improved,and the solution effect is quite excellent.Furthermore,by solving 6 more challenging engineering constrained optimization problems in IEEE CEC2020,it is fully verified and shown that M-JAYA algorithm has obvious advantages and adaptability in dealing with different types of engineering constrained optimization design problems.(3)The above two improved algorithms are applied to two different sub problems of green logistics distribution problem — electric vehicle path planning problem with time window and simultaneous pick-up and delivery and including charging station(EVRPTW),fuel vehicle path planning problem with time window and simultaneous pick-up and delivery in low-carbon environment(GVRPTW).The mathematical models of EVRPTW and GVRPTW are constructed respectively,the corresponding individual coding and decoding methods in the algorithms are defined,and the target distribution cost established by multiple different constraints contained in the two applications is solved as the objective function.The results of different scale test examples show that the model and algorithms in the paper have good solving ability to deal with the problem of green logistics distribution path optimization of different scales,and can effectively reduce the total cost and promote the energy conservation and emission reduction of logistics distribution.The green logistics distribution problem studied in the paper further broadens the relevant theories and methods of logistics distribution in the theoretical category.In terms of application scope,it provides management enlightenment and method reference for logistics companies to realize the vision of green logistics and the unity of economic and environmental benefits in actual operation.