| Bald Eagle Search Algorithm(BES)is a novel meta-heuristic algorithm that simulates the hunting strategy and intelligent social behavior of bald eagles when searching for fish.The algorithm has simple structure,few control parameters,strong global search ability,and can effectively solve complex function optimization problems.BES algorithm has been widely used in engineering,control,optimization,energy and other fields since it was proposed.However,with the continuous deepening of research,researchers found that the algorithm is prone to fall into local optimization,low search efficiency,and low global search accuracy.This paper analyzes and improves the shortcomings of the bald eagle search algorithm,proposes two improved bald eagle search algorithms,and applies the improved algorithm to practical optimization problems,in order to improve the performance of BES algorithm and expand its application range.The main research work of this paper is as follows:(1)To increase the diversity of the population and improve the exploration and development ability of the algorithm,a polar coordinate bald eagle search algorithm(PBES)is proposed.The PBES population initialization,search process and position update are all carried out directly in polar coordinates,and the individual position is updated by updating the polar angle and polar diameter respectively.These strategies greatly improve the convergence speed and accuracy of the algorithm,and have been successfully applied to the polar coordinate transcendental equation and the inverse kinematics of the manipulator,broadening the application range of the algorithm.(2)In order to broaden the application field of the bald eagle search algorithm,the proposed polar coordinate bald eagle search algorithm is applied to the curve approximation problem in polar coordinate space.In polar coordinate space,16 different types of curves,such as conic curve,Pascal spiral,spiral and special curve,are tested.The experimental results show that PBES algorithm has good approximation effect and superior performance.Compared with other algorithms,it has strong competitiveness and can effectively implement curve approximation problems.(3)To solve the multi-objective optimization problem,a multi-objective bald eagle search algorithm(MOBES)is proposed.MOBES uses the archiving mechanism,roulette,elite selection strategy and other methods.By testing the CEC 2020 benchmark function and the engineering design problems of two objectives,three objectives and four objectives in real life,experimental results show that the proposed algorithm is superior to other algorithms in terms of convergence,diversity and distribution of solutions,and has strong advantages in dealing with challenging multi-objective optimization problems with unknown real Pareto optimal solutions and frontiers.Compared with other algorithms,it is more competitive and can solve multi-objective optimization problems well. |
