# 【智能优化算法】基于倭黑猩猩优化算法求解单目标优化问题附matlab代码

## 1 内容介绍

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.

## 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;

## 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。

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