Comparative Study Of Several Intelligent Optimization Algorithm And Their Application In Ancient Ceramic Classification

With the rapid development of information technology,intelligent optimization algorithms have received great attention and are widely used,and have been rapidly developed in the fields of financial engineering,biological information,medicine,chemical engineering,drones,and transportation.The intelligent optimization algorithm overcomes the limitations of traditional optimization methods and can effectively save time,cost and energy when dealing with large-scale and relatively complex optimization problems.Many intelligent optimization algorithms have made great progress in their own improvement and application and research in combination with practical problems.However,there are relatively few comparative studies between intelligent optimization algorithms.In terms of the applicability of the algorithms,they are still based on the researchers themselves.The situation of using experience to choose^[1].In order to carry out related comparative research and analysis on several more classic intelligent optimization algorithms,and compare the related performance of each algorithm more intuitively,the paper respectively discusses genetic algorithm（GA）,simulated annealing algorithm（SA）,and an adaptive inertia.The weighted particle swarm algorithm and hybrid particle swarm algorithm are used for the test function solving experiment.The selected test functions are:fixed-dimensional multi-modal test function Schaffer,multi-dimensional single-mode test function Sphere,multi-dimensional multi-modal function Griewank and Rastrigin.Based on the related results of the test function solution,compare and analyze the performance of each algorithm in terms of convergence,global search ability,time complexity,stability and accuracy.Analyze the main factors that affect the algorithm,such as parameters,models,etc.In order to combine the practical application,the paper chooses to use genetic algorithm to optimize the parameters of the BP neural network algorithm,and applies it to the age classification prediction of ancient ceramics,compares and analyzes the results after optimization and before optimization,and draws a conclusion,which is optimized by genetic algorithm The latter BP neural network model has better prediction accuracy than the network model before optimization.choose^[1].