Profit Information System of Exhibition Enterprises Based on Multiobjective Optimization Algorithm

Abstract

To solve the problem of publishing company revenue data, a multiobjective optimization algorithm is proposed. Therefore, based on the research on the multipurpose optimization algorithm, this paper proposed a multipurpose optimization hybrid approval algorithm. The proposed algorithm generates a candidate set through the hybrid recommendation algorithm and then uses the multiobjective optimization algorithm to generate recommendation list with optimal accuracy and diversity. MATLABR2017a was used to calculate the c-metric and hypervolume of the measurement data. Besides, Excel was used to calculate the Pareto solution developed in this paper many times. The test environment is running on 64 bit Win10, running 6 GB memory, and the CPU frequency is 2.30 GHz. Experimental decisions were made by comparing the Pareto optimal solutions developed by the input algorithm and the control algorithm in 3 consecutive groups of experimental users. Experimental analysis shows that Pareto believes that the solution developed by the hybrid approval algorithm is multipurpose optimization, superior to the solution developed by the board, and that the algorithm can be used to create realistic and diverse results.