Application Research On Solving Maximal Boolean Polynomials Based On Improved Swarm Intelligence Algorithm

In recent years,the development and application of artificial intelligence have been favored by more and more people as it brings convenience to people in more and more scenarios.Swarm intelligence,as a kind of artificial intelligence has become an integral part of artificial intelligence research because of its independence from training sets.Swarm intelligence is gaining popularity with the increasing prominence of NP-hard problems in which it is almost impossible to find the global optimum in real-time scenarios.The number of potential solutions in such problems is often infinite.In such cases,it becomes important to find feasible solutions within time constraints.Considering almost all fields of science,engineering and industry,from data mining to optimal routing,scheduling,structural optimization as well as image data analysis,computational intelligence,business planning,dynamical systems,operations research,bioinformatics and industrial applications,population intelligence plays its usefulness in solving nonlinear design problems and practical applications.Solving Boolean polynomial equations is widely used in fields and scenarios such as artificial intelligence,multi-objective optimization,code-breaking,circuit design,path planning,and computer design.Since solving the maximal Boolean polynomial equations is an NP-hard problem and has been a difficult problem since its introduction.Coding theory is a forward error correction technique that gives better solutions for solving the maximal Boolean polynomial problem,but its algorithm complexity is high and relies on computer arithmetic.These shortcomings limit the application scenarios of coding theory in solving the problem of maximal Boolean polynomials.In order to overcome the drawbacks of coding theory in solving the problem of maximal Boolean polynomials,an efficient algorithm for solving the maximal Boolean polynomials is proposed.The main work of this paper is as follows:(1)The genetic algorithm is improved to solve the problem of maximal Boolean polynomials by combining the coding theory of existing literature.Compared with the existing genetic algorithms in the literature,the maximum value of the maximal Boolean polynomials solved by the improved genetic algorithm is improved substantially.The improved genetic algorithm also achieves a significant increase in the maximum value calculated by the improved genetic algorithm compared to the original coding algorithm,and the overall performance is also significantly improved.The improved algorithm retains the advantages of the original coding algorithm,such as high searchability and robustness.(2)Other swarm intelligence algorithms are improved to solve the maximal Boolean polynomial problem by combining the coding theory.In order to verify the performance of the improved algorithm,it is compared with the grouping for random calculations.(3)Comparative analysis of the results of several algorithms.The experimental results show that several improved algorithms obtained effective solutions when solving the maximal Boolean polynomial problem.The difference is that the performance of the improved genetic algorithm and the improved differential evolution algorithm is close,but the stability and robustness of these two algorithms are not as well as the improved particle swarm algorithm.These improved swarm intelligence algorithms combined with coding theory can overcome the disadvantages of high complexity and unsuitability for practical application scenarios when only using the coding algorithm,and overcome the disadvantages of "premature" and easily falling into local optimal solutions of swarm intelligence algorithms by combining coding theory for solving extremely Boolean polynomial problems.