Improvement Of Whale Optimization Algorithm And Its Application In Path Planning

The optimization problems encompass various fields such as mathematics,computer science,and engineering.With the continuous development of computer technology and complex optimization problems,higher demands are placed on algorithm efficiency and theoretical research.Path planning problems are essentially optimization problems that hold significant importance in industries like robotics and modern logistics.Reasonable path planning can reduce costs,minimize resource waste,and promote sustainable development.However,traditional optimization algorithms have lower efficiency in solving path planning problems and struggle to find optimal solutions within limited time frames.Therefore,researchers are dedicated to seeking more advanced algorithms.Swarm intelligence algorithms have gained significant attention and research due to their efficiency and wide applicability.The Whale Optimization Algorithm(WOA)is a novel swarm intelligence algorithm that mimics the foraging behavior of whales and can find global optimal solutions in multi-dimensional spaces.This paper aims to study the WOA,analyze its limitations,propose improvement strategies,and apply it to global path planning and multimodal transportation path planning problems.The main research contents are as follows:(1)To address the drawbacks of the Whale Optimization Algorithm,such as susceptibility to local optima,slow convergence speed,and low optimization accuracy,a revised Whale Optimization Algorithm based on Kent mapping and adaptive parameters is proposed.Firstly,the Kent mapping is introduced to initialize the population,enrich the diversity of the population,and lay the foundation for the next global search.Secondly,a non-linear convergence factor strategy is proposed to improve the global search speed and local optimization accuracy.Finally,an inertia weight maintenance algorithm is added to balance between global search and local optimization.Four swarm intelligence algorithms and four variants of the Whale Optimization Algorithm are selected as comparative algorithms,and experiments are conducted using standard test functions.The experimental results show that the improved Whale Optimization Algorithm proposed in this paper has significant advantages in global search,convergence speed,optimization accuracy,and stability.(2)Using the improved whale optimization algorithm to solve global path planning and multimodal transportation path planning problems.In the global path planning,first,the environment model is established using the grid method,the encoding of the whale individuals is defined,and the fitness function of the algorithm is constructed.Then,the improved whale optimization algorithm is compared with two swarm intelligence algorithms and two variants of whale optimization algorithms for path planning simulation experiments in maps with different levels of complexity.The experimental results show that the improved whale optimization algorithm can obtain shorter routes with fewer iterations in solving global path planning problems,demonstrating its significant advantages in terms of convergence speed,convergence accuracy,and stability.In the multimodal transportation path planning problem,first,a multi-objective multimodal transportation path planning model is constructed based on transportation cost,time cost,carbon emission cost,and penalty cost.Then,the improved whale optimization algorithm and the whale optimization algorithm are combined with the model for comparative experiments.The experimental results show that the improved whale optimization algorithm can generate transportation plans based on the multi-objective function and has significant advantages in convergence speed compared to the whale optimization algorithm,demonstrating the effectiveness of the improvement strategy.