1 简介阿基米德优化算法( Archimedes optimization algorithm, AOA)是一种基于群体的启发式算法。在该方法中种群个体是沉浸对象。与其他基于种群的元启发式算法一样AOA也从具有随机体积、密度和加速度的初始种群(候选解)开始搜索过程。he difficulty and complexity of the real-world numerical optimization problems has grown manifold, which demands efficient optimization methods. To date, various metaheuristic approaches have been introduced, but only a few have earned recognition in research community. In this paper, a new metaheuristic algorithm called Archimedes optimization algorithm (AOA) is introduced to solve the optimization problems. AOA is devised with inspirations from an interesting law of physics Archimedes Principle. It imitates the principle of buoyant force exerted upward on an object, partially or fully immersed in fluid, is proportional to weight of the displaced fluid. To evaluate performance, the proposed AOA algorithm is tested on CEC17 test suite and four engineering design problems. The solutions obtained with AOA have outperformed well-known state-of-the-art and recently introduced metaheuristic algorithms such genetic algorithms (GA), particle swarm optimization (PSO), differential evolution variants L-SHADE and LSHADE-EpSin, whale optimization algorithm (WOA), sine-cosine algorithm (SCA), Harris hawk optimization (HHO), and equilibrium optimizer (EO). The experimental results suggest that AOA is a high-performance optimization tool with respect to convergence speed and exploration-exploitation balance, as it is effectively applicable for solving complex problems.2 部分代码clear all clc Solution_no20; %Number of search solutions F_nameF1; %Name of the test function F1-f23 M_Iter1000; %Maximum number of iterations [LB,UB,Dim,F_obj]Get_F(F_name); %Give details of the underlying benchmark function [Best_FF,Best_P,Conv_curve]AOA(Solution_no,M_Iter,LB,UB,Dim,F_obj); % Call the AOA figure(Position,[454 445 694 297]); subplot(1,2,1); func_plot(F_name); title(Parameter space) xlabel(x_1); ylabel(x_2); zlabel([F_name,( x_1 , x_2 )]) subplot(1,2,2); semilogy(Conv_curve,Color,r,LineWidth,2) title(Convergence curve) xlabel(Iteration#); ylabel(Best fitness function); axis tight legend(AOA) display([The best-obtained solution by Math Optimizer is : , num2str(Best_P)]); display([The best optimal value of the objective funciton found by Math Optimizer is : , num2str(Best_FF)]);3 仿真结果4 参考文献[1] Hashim F A , Hussain K , Houssein E H , et al. Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems[J]. Applied Intelligence, 2020:1-21.博主简介擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真相关matlab代码问题可私信交流。部分理论引用网络文献若有侵权联系博主删除。