Research And Engineering Application Of Improved Grasshopper Optimization Algorithm

With the increasing complexity of practical problems,it is necessary for people to optimize engineering application design.Based on the experimental data,it is applied to the production,manufacturing and construction process of the welded beam design,tension/compression spring design and pressure vessel problem,and the simulation model is established for evaluation,control and optimization,which can reduce the actual cost in industrial application and obtain a lower cost.Swarm intelligence algorithm,as an efficient optimization algorithm,can get a strong optimization effect in engineering applications,and obtain better parameters and models.The grasshopper optimization algorithm is a new heuristic algorithm to simulate the biological behavior of grasshoppers searching for food sources.It still has some shortcomings in engineering application optimization.In some cases,it may quickly fall into local optimum and its convergence speed is slow.In order to more effectively deal with the engineering application problem,this thesis improves the grasshopper optimization algorithm(grasshopper optimization algorithm,GOA)and puts forward three optimization algorithms with better effect,respectively based on the orthogonal learning and chaotic local search of grasshopper optimization algorithm(OLCGOA),grasshopper optimization algorithm based on elite opposition-based learning and Gaussian bare-bones strategy(EGOA),and based on the spiral movement of the grasshopper optimization algorithm(SGOA),and the improved algorithm used in the engineering application of parameter optimization.Firstly,two strategies of orthogonal learning and chaotic local search are introduced into the traditional grasshopper optimization algorithm,so that the original algorithm can find a more stable balance between the exploration and exploitation process.Adding orthogonal learning into the grasshopper optimization algorithm can enhance the diversity of agents,enhance the local search ability of the algorithm,and improve the solution quality.The chaotic exploitation strategy can update the grasshopper position in the limited local area,improve the exploitation ability of the algorithm,and prevent the optimization algorithm from being trapped in the local optimal solution.Secondly,elite opposition-based learning and Gaussian bare-bones strategies are added to the original grasshopper optimization algorithm,which improves the global search ability and local search ability of the original grasshopper optimization algorithm,and effectively balance exploration and exploitation.Elite opposition-based learning can help it find a better grasshopper localization solution in the early stage of exploration,effectively explore the search space to improve the global optimization ability.The Gaussian bare-bones strategy has a good ability to update the locust position,improve the solving accuracy of the algorithm,and enhance the local search ability of the algorithm.Finally,aiming at the continuous optimization problem of spiral motion,the characteristics of optimization are considered.By integrating the spiral motion into the mining and searching stage of the original grasshopper optimization algorithm,the local search performance and global optimization performance of the grasshopper optimization algorithm are further improved,and the exploration and exploitation process are effectively balanced,so as to avoid the possibility of the algorithm falling into the local optimal early,and obtain better results.The improved algorithm based on primitive grasshopper proposed in this thesis has been applied to engineering problems.In the face of different types of engineering problems,the improved grasshopper optimization algorithm can obtain better results in the process of experiment.It provides a new choice for the optimization of production,design and construction process of engineering application.A deep understanding and mastery of the characteristics and coupling rules of design objectives is conducive to ensuring the safety of engineering structures and better facing the situation of large-scale and lightweight engineering structures.Give full play to the advantages of energy saving and high efficiency of this technology,and then realize the optimization of structural design and manufacturing process.