Research On Improvement Of Swarm Intelligence Optimization Algorithm And Application In Path Planning

An optimization problem is to select a set of variables that satisfy the constraint conditions so that the target reaches the optimal value.Robot path planning is a classical optimization problem,which is also NP-hard problem.It is specifically to find an optimal path that satisfies constraints in a specific environment.In the face of increasingly complex working environment,the traditional algorithm is difficult to solve the path planning problem of robots,while the swarm intelligent optimization algorithm has strong robustness and effectiveness,and has obvious advantages in solving.Based on the above background,two new swarm intelligent optimization algorithms are selected for study in this paper,namely,Harris Hawk optimization algorithm and Sparrow search algorithm.The mathematical principles of these two algorithms are analyzed,and improvements are made in terms of initializing the population and global search for the shortcomings of the algorithms.The performance of the algorithms is verified by using the international common CEC function test set,and the improved algorithms are applied to the solution of robot path planning problems with good results.Specifically,the main research of this paper is as follows:(1)The Improved Harris Hawks Optimization fused with multiple strategies(IHHO)is proposed to address the problem that the HHO is easy to fall into local optimal and imbalance between global search and local exploitation.The quality of the initial solution is improved by using the good point set to initialize the population.The exploration and exploitation ability of the algorithm is balanced by resetting the escape energy of the algorithm,and the discovery phase is improved by introducing the discoverer position update formula in the sparrow search algorithm to enhance the global search ability of the algorithm.The Cauchy-Gaussian variation is used to perturb the optimal solution,which effectively avoids the algorithm from falling into local optimum.Simulation experiments are carried out on the international common CEC function test set and compared and analyzed with other swarm intelligence optimization algorithms,and the results show that the IHHO algorithm has improved in both the optimization search accuracy and convergence speed.(2)The Sparrow Search Algorithm Based on Memory Sequence and Adaptive Parameters(MASSA)is proposed to address the problems of insufficient stability and easy to fall into local optimal of the sparrow search algorithm.The population is initialized using the good point set method to improve the diversity of the population.The discoverer formula is improved with the help of weight factor to enhance the exploration ability of the algorithm.Improving the joiner formula by introducing memory sequences to ensure the diversity of the population.Dynamically adjust the discoverer and joiner ratios by adaptive parameters to balance the exploration and development of the algorithm.Simulation experiments are conducted on the international common CEC function test set and compared and analyzed with other swarm intelligence optimization algorithms,and the experimental results show that the performance of the algorithm is better than the comparison algorithms.(3)The IHHO algorithm and MASSA algorithm are applied to solve the robot path planning problem in two-dimensional environment,and the environment is modeled by using the raster method to construct three different environment models and define the problem encoding and fitness functions.The performance of the IHHO algorithm and MASSA algorithm in solving the path planning problem is experimentally verified to be improved compared with the original algorithm.