Design And Application Of Intelligent Optimization Algorithm

In the past decade,intelligent optimization algorithms have developed rapidly with the development of intelligent computing.Intelligent optimization algorithms will try to make changes in different ways to fall into a local optimal situation,and can find a better solution in a shorter time.The intelligent optimization algorithm is basically based on the idea of an improved method.The method is to construct a initial solution randomly first,and then the existing local solution is improved according to different iterative ideas until the results meet certain preset conditions.The intelligent optimization algorithm concludes Genetic Algorithm(GA),Particle Swarm Optimization(PSO),Ant Colony Optimization(ACO),Simulated Annealing(SA),and so on.The intelligent optimization algorithm is widely used,and it has a wide range of applications in both continuous and discrete problems.On the one hand,this paper applies to find the optimal configuration of chemically atomic clusters,On the other hand,it is applied to the transportation of hazardous chemicals.Swarm intelligence optimization algorithms are mainstream algorithms for solving complex optimization problems.Among these algorithms,the particle swarm optimization(PSO)algorithm has the advantages of fast computation speed and few parameters.However,PSO is prone to premature convergence.To solve this problem,we develop a new PSO algorithm(RPSOLF)by combining the characteristics of random learning mechanism and Levy flight.On the one hand,we carry out a large number of numerical experiments on benchmark test functions,and compare these results with the PSO algorithm with Levy flight(PSOLF)algorithm and other PSO variants in previous reports.The results show that the optimal solution can be found faster and more efficiently by the RPSOLF algorithm.On the other hand,the RPSOLF algorithm can also be applied to optimize the Lennard-Jones clusters,and the results indicate that the algorithm obtains the optimal structure(2-60 atoms)with an extraordinary high efficiency.In summary,RPSOLF algorithm proposed in our paper is proved to be an extremely effective tool for global optimization.In the process of hazardous chemicals transport,security is one important factor which deserves serious consideration besides cost.In addition,along with more and more attention to environments,carbon emission of hazardous chemicals transportation becomes another critical important factor to be taken seriously.In this paper,we establish a novel model of environmental hazardous vehicle routing problem not only considering the costs and the hazard risks but also considering the carbon emission.In this model,the total cost includes only fixed costs,the hazard risk is characterized by the number of exposed populations and is influenced by the load of each vehicle.The carbon emission is characterized by the amount of C02 emission which is indicated by a function of the goods carried by the vehicles.In order to solve the proposed novel model,this paper designs an improved hybrid ant colony algorithm by incorporating simulated annealing algorithm.Finally,we conduct some numerical experiments and list Pareto solutions under different CO2 emissions for decision-makers to choose.