1 内容介绍
非传统的优化工具已经证明了它们在解决各种优化问题方面的潜力。这些问题处理单个目标或多个/多个目标。倭黑猩猩优化器（BO）是一种智能自适应元启发式优化算法，灵感来自倭黑猩猩的社会行为和繁殖策略。文献中没有研究扩展这个 BO 来解决多目标优化问题。本文提出了一种多目标 Bonobo 优化器 (MOBO) 来解决不同的优化问题。本文提出了三个不同版本的 MOBO，每个版本都使用不同的方法，例如自适应网格方法的非支配排序；一种使用拥挤距离方法对人口进行排序的排序方案；分解技术，其中解决方案是通过将一个多目标问题分解为多个单目标问题来获得的。提议的 MOBO 的所有三个不同版本的性能已经在一组 30 个多样化的基准测试功能上进行了测试，并将结果与文献中可用的其他四种著名的多目标优化技术的结果进行了比较。获得的结果表明，与其他算法相比，所提出算法的前两个版本在收敛性和多样性方面表现出色或具有竞争力。
tled attacker, barrier, chaser, and driver are employed for simulating the diverse intelligence. Moreover, the four main steps of hunting, driving, blocking, and attacking, are implemented. Afterward, the algorithm is tested on 30 well-known benchmark functions, and the results are compared to four newly proposed meta-heuristic algorithms in term of convergence speed, the probability of getting stuck in local minimums, and the accuracy of obtained results. The results indicate that the ChOA outperforms the other benchmark optimization algorithms.
编辑
2 仿真代码
% This is the matlab code for the optimization algorithm, namely Bonobo Optimizer (BO).
% This is written for solving unconstrained optimization problems. However, it can also solve constrained optimization
% problems with penalty function approaches.
% Moreover, this for solving minimization problems.
% For details of the BO algorithm, kindly refer and cite as mentioned below:
% A. K. Das and D. K. Pratihar, "Bonobo optimizer (BO): an intelligent heuristic with selfadjusting parameters over continuous spaces and its applications to engineering problems,"
% Applied Intelligence, 2021, DOI: 10.1007/s10489-021-02444-w
% For any query, please email to: amit.besus@gmail.com
clc;close all;clear all;
tic; % CPU time measure
CostFunction = @(x)MyObjectiveFunction(x); % Objective function
d=4; % No. of Variables
Var_min=[-100 -100 -100 -100]; % Lower variable Boundaries
Var_max=[100 100 100 100]; % Upper variable Boundaries
%% Common parameters of BO similar to other optimization algorithms
N=30; % No. of bonobos in the population, i.e. population size
max_it=100; % Maximum number of iterations
[bestcost,alphabonobo,convergence_curve]=BO(N,d,Var_min,Var_max,max_it,CostFunction);
disp(['Best Cost: ' num2str(bestcost)]);
disp(['Bestsolution: ' num2str(alphabonobo)]);
figure
plot (1:max_it,convergence_curve,'-*')
title('Convergence Curve')
xlabel('Number of iterations')
ylabel('Evolution of best objective value')
toc;
3 运行结果
编辑
4 参考文献
[1] Das A K , Nikum A K , Krishnan S V , et al. Multi-objective Bonobo Optimizer (MOBO): an intelligent heuristic for multi-criteria optimization[J]. Knowledge and Information Systems, 2020, 62(6).
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。
