matlab：一种改进的鹦鹉优化算法改进鹦鹉优化器IPO算法

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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

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