matlab：一种改进的鹦鹉优化算法改进鹦鹉优化器IPO算法

共9个文件

m：6个

docx：2个

txt：1个

版权申诉

matlab

117 浏览量 2024-04-10 21:16:38 上传评论 1 收藏 387KB RAR 举报

资源推荐

资源详情

资源评论

收起资源包目录

IPO（改进鹦鹉优化算法[1]）.rar （9个子文件）

IPO（改进鹦鹉优化算法[1]）

initialization.m 4KB

IPO.m 4KB

main.m 951B

PO.m 3KB

Algorithm section-Parrot optimizer-Algorithm and applications to medical problems.docx 390KB

算法说明.txt 16B

Get_Functions_details.m 9KB

~$gorithm section-Parrot optimizer-Algorithm and applications to medical problems.docx 162B

func_plot.m 5KB

Parrot optimizer: Algorithm and

applications to medical problems

Junbo Lian

1,2,3

, Guohua Hui

1,2,3

, Ling Ma

1,2,3

, Ting Zhu

1,2,3

, Xincan Wu

1,2,3

, Ali

Asghar Heidari

, Yi Chen

, Huiling Chen

College of Mathematics and Computer Sciences, Zhejiang A & F University, Hangzhou 311300

junbolian@qq.com, deliver1982@163.com, 2516453776@qq.com, 862640717@qq.com,

2303378787@qq.com

Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of

Forestry, Zhejiang A & F University, Hangzhou 311300

junbolian@qq.com, deliver1982@163.com, 2516453776@qq.com, 862640717@qq.com,

2303378787@qq.com

Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang

Province, Zhejiang A & F University, Hangzhou 311300

junbolian@qq.com, deliver1982@163.com, 2516453776@qq.com, 862640717@qq.com,

2303378787@qq.com

School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran,

Tehran, Iran

as_heidari@ut.ac.ir

College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035,

PR China

kenyoncy2016@gmail.com, chenhuiling.jlu@gmail.com

*Corresponding Author: Huiling Chen (chenhuiling.jlu@gmail.com)

1 The parrot optimizer (PO)

This section explains the overall background of the PO and the formulated optimization models.

1.1 Inspiration

The Pyrrhura Molinae, a well-liked parrot species, is a popular choice for pet owners owing to its attractive

features, close bonding with its owners, and ease of training [54, 55]. Previous studies and breeding efforts have

revealed that Pyrrhura Molinae exhibits four distinct behavioral traits: foraging, staying, communicating, and a

fear of strangers [56, 57]. These behaviors, illustrated in Fig. 1 within real-world contexts, form the basis of our

motivation for designing the PO.

(a) foraging

(b)staying

(d) communicating

Fig. 1. Four behaviors of Pyrrhura Molinae

� The

foraging

behavior of domesticated Pyrrhura Molinae is fascinating, as individuals choose to

forage in small groups where food is abundant [54]. They can find the food by heading toward it,

utilizing their owner's location and the group's presence. They enhance their search using smell

and visual hints.

� The

staying

behavior involves Pyrrhura Molinae perching randomly on various areas of their

owner's body.

� These sociable birds produce distinctive calls to

communicate

within their group, serving both

for social interaction and information spread.

� The natural

fear of strangers

, a common trait among birds, prompts Pyrrhura Molinae to move

away from unfamiliar individuals and seek safety with their owners for protection [58].

� Importantly, the unpredictability of Pyrrhura Molinae behavior stresses the motivation for our

design, as these four behaviors occur randomly in each individual during each iteration within

domesticated flocks.

1.2 Mathematical model of PO

1.2.1 Population initialization

The initialization formulation for the proposed PO, considering a swarm size of

𝑁

, maximum iterations

𝑀𝑎𝑥

𝑖𝑡𝑒𝑟

, and search space limits of

𝑙𝑏

(lower bound) and

𝑢𝑏

(upper bound), can be shown as:

𝑋

𝑖

𝑙𝑏

𝑟𝑎𝑛𝑑(0,1)

⋅

(𝑢𝑏

―

𝑙𝑏)

(1)

where

𝑟𝑎𝑛𝑑(0,1)

denotes a random number in the range [0, 1] and

𝑋

𝑖

denotes the position of the

𝑖

𝑡ℎ

Pyrrhura

Molinae in the initial phase.

1.2.2 Foraging behavior

During the foraging behavior in PO, they estimate the approximate location of food primarily by observing

the food's location or by considering the owner's position, then they fly towards the respective location.

Therefore, the positional movement follows the equation:

𝑋

𝑡

𝑖

(

𝑋

𝑡

𝑖

―

𝑋

𝑏𝑒𝑠𝑡

)

⋅

𝐿𝑒𝑣𝑦(𝑑𝑖𝑚)

𝑟𝑎𝑛𝑑(0,1)

⋅

―

𝑡

𝑀𝑎

𝑥

𝑖𝑡𝑒𝑟

)

2𝑡

𝑀𝑎

𝑥

𝑖𝑡𝑒𝑟

⋅

𝑋

𝑡

𝑚𝑒𝑎𝑛

(2)

In Eq. (2),

𝑋

𝑡

𝑖

denotes the present location, while

𝑋

𝑡

𝑖

means the location of the succeeding update.

𝑋

𝑡

𝑚𝑒𝑎𝑛

represents the average location inside the present population, and

𝐿𝑒𝑣𝑦(𝐷)

denotes the Levy

distribution, It is used to describe the flight of parrots.

𝑋

𝑏𝑒𝑠𝑡

denotes the best position that has been searched

from initialization to the current, and it also represents the host's current position.

𝑡

denotes the current number

of iterations.

(

𝑋

𝑡

𝑖

―

𝑋

𝑏𝑒𝑠𝑡

)

⋅

𝐿𝑒𝑣𝑦(𝑑𝑖𝑚)

indicates movement based on one's position in relation to the owner,

and

𝑟𝑎𝑛𝑑(0,1)

⋅

―

𝑡

𝑀𝑎

𝑥

𝑖𝑡𝑒𝑟

)

2𝑡

𝑀𝑎

𝑥

𝑖𝑡𝑒𝑟

⋅

𝑋

𝑡

𝑚𝑒𝑎𝑛

indicates observation of the position of the population as a whole

to further target the orientation of the food. The process is depicted in Fig. 2.

Fig. 2. The foraging behavior

The average location of the current swarm, is shown by

𝑋

𝑡

𝑚𝑒𝑎𝑛

, is attained using the formula shown in

评论收藏

内容反馈

版权申诉

天`南

粉丝: 1291
资源: 270

matlab：一种改进的鹦鹉优化算法改进鹦鹉优化器 IPO算法

最新资源

matlab：一种改进的鹦鹉优化算法 改进鹦鹉优化器 IPO算法

蚁群优化算法的改进及应用

施肥优化算法（matlab实现）

lizi_改进_ipso_优化_自适应粒子群优化算法_matlab

【智能仿生算法】融合邻域扰动和随机权重的改进蝴蝶优化算法【matlab代码】

MATLAB算法实战应用案例精讲-鹦鹉优化器（PO）（附MATLAB代码实现）

matlab代码：一种改进的火鹰优化算法 改进FHO算法

matlab代码：一种改进的蜜獾优化算法 改进后的IHBA2

MATLAB代码：基于改进粒子群算法的含电动汽车参与园区综合能源优化调度 关键词：电动汽车 改进粒子群 综合能源 优化调度 园区

matlab实现的改进的粒子群优化算法完整程序代码

【仿生智能算法】基于Bernoulli映射和动态因数的改进蜣螂优化算法【matlab代码】

改进类电磁优化算法附matlab代码

【仿生智能算法】基于Chebyshev映射和黄金正弦的改进蜣螂优化算法【matlab代码】

改良花授粉算法：改良花授粉算法-matlab开发

matlab matlab

PO-BP鹦鹉优化算法优化BP神经网络分类预测（Matlab完整源码和数据）

MATLAB智能优化算法 从写代码到算法思想.pptx

matlab代码：一种改进的同核分子优化算法 IHMO算法

Matlab优化算法PDF

matlab: 基于改进的差分进化算法JADE算法优化单目标

一种改进的蚁狮优化算法 改进的ALO算法【matlab代码】 - 在原始ALO算法的基础上添加了两种改进策略 - 改进1：将原先

智能优化算法及其MATLAB实例源程序

改进遗传算法_改进遗传算法_matlab_遗传算法改进_遗传算法改进_遗传算法_

基于遗传算法matlab优化程序_改进遗传算法_优化算法_源码

MATLAB 最优化算法合集

一种改进的粒子群优化算法-MATLAB 2023年提出 改进包含： 1、改进惯性权重 2、改进学习因子

【黏菌优化算法】精英反向与二次插值改进的黏菌算法(ISMA)求解单目标优化问题含Matlab源码.zip

智能优化算法实现（matlab）_数学建模

基于matlab的各种优化算法（书和代码）

算术优化算法MATLAB源代码，可直接运行

最新资源

matlab：一种改进的鹦鹉优化算法改进鹦鹉优化器 IPO算法

matlab代码：一种改进的火鹰优化算法改进FHO算法

matlab代码：一种改进的蜜獾优化算法改进后的IHBA2

MATLAB代码：基于改进粒子群算法的含电动汽车参与园区综合能源优化调度关键词：电动汽车改进粒子群综合能源优化调度园区

MATLAB智能优化算法从写代码到算法思想.pptx

一种改进的蚁狮优化算法改进的ALO算法【matlab代码】 - 在原始ALO算法的基础上添加了两种改进策略 - 改进1：将原先

一种改进的粒子群优化算法-MATLAB 2023年提出改进包含： 1、改进惯性权重 2、改进学习因子