Barnacles Mating Optimizer: An Evolutionary Algorithm for Solving Optimization

被引：0

作者：

Sulaiman, Mohd Herwan ^{[1
]}

Mustaffa, Zuriani ^{[2
]}

Saari, Mohd Mawardi ^{[1
]}

Daniyal, Hamdan ^{[1
]}

Musirin, Ismail ^{[3
]}

Daud, Mohd Razali ^{[1
]}

机构：

[1] Univ Malaysia Pahang, Fac Elect & Elect Engn, Pekan, Pahang, Malaysia

[2] Univ Malaysia Pahang, Fac Comp Syst & Software Engn, Gambang, Pahang, Malaysia

[3] Univ Teknol MARA, Fac Elect Engn, Shah Alam, Malaysia

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC CONTROL AND INTELLIGENT SYSTEMS (I2CACIS) | 2018年

关键词：

BMO; evolutionary algorithm; optimization; swarm intelligence;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new evolutionary algorithm called Barnacles Mating Optimizer (BMO) to solve optimization problems is presented in this paper. BMO is inspired from the behavior of barnacle' mating in nature. They are known as micro-organisms that existed since Jurassic times and classified as hermaphroditic micro-organisms. They have a unique feature which is they own long penises that can be said as the longest among microorganisms, relatively to size of their body. To show the effectiveness of proposed BMO in solving optimization problems, a set of 23 mathematical functions are used to test the characteristic of BMO in finding the optimal solutions especially in unimodal, multimodal and composite test functions. Comparisons with other evolutionary and swarm algorithms also will he presented.

引用

页码：99 / 104

页数：6

共 50 条

[41] An improved evolutionary algorithm as function optimizer
Bandyopadhyay, S
Maulik, U
IETE JOURNAL OF RESEARCH, 2000, 46 (1-2) : 47 - 56
[42] Woodpecker Mating Algorithm (WMA): a nature-inspired algorithm for solving optimization problems
Parizi, Morteza Karimzadeh
Keynia, Farshid
Bardsiri, Amid Khatibi
INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS, 2020, 11 (01): : 137 - 157
[43] Analysis of a multi-objective hybrid system to generate power in different environmental conditions based on improved the Barnacles Mating Optimizer Algorithm
Fan, Guangli
Li, Meng
Chen, Xinxiao
Dong, Xiaolei
Jermsittiparsert, Kittisak
ENERGY REPORTS, 2021, 7 : 2950 - 2961
[44] Stock price predictive analysis: An application of hybrid Barnacles Mating Optimizer with Artificial Neural Network
Mustaffa Z.
Sulaiman M.H.
International Journal of Cognitive Computing in Engineering, 2023, 4 : 109 - 117
[45] Barnacles Mating Optimizer with Hopfield Neural Network Based Intrusion Detection in Internet of Things Environment
Velumani, Rajakani
Kalimuthu, Vinoth Kumar
TEHNICKI VJESNIK-TECHNICAL GAZETTE, 2023, 30 (06): : 1821 - 1828
[46] Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification
Dutta, Ashit Kumar
Mageswari, R. Uma
Gayathri, A.
Dallfin Bruxella, J. Mary
Ishak, Mohamad Khairi
Mostafa, Samih M.
Hamam, Habib
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
[47] A Logistic Chaotic Barnacles Mating Optimizer With Masi Entropy for Color Image Multilevel Thresholding Segmentation
Li, Hongbo
Zheng, Gang
Sun, Kangjian
Jiang, Zichao
Li, Yao
Jia, Heming
IEEE ACCESS, 2020, 8 : 213130 - 213153
[48] A hybrid membrane evolutionary algorithm for solving constrained optimization problems
Xiao Jianhua
Huang Yufang
Cheng Zhen
He Juanjuan
Niu Yunyun
OPTIK, 2014, 125 (02): : 897 - 902
[49] An Effective Hybrid Evolutionary Algorithm for Solving the Numerical Optimization Problems
Qian, Xiaohong
Wang, Xumei
Su, Yonghong
He, Liu
2ND INTERNATIONAL CONFERENCE ON MACHINE VISION AND INFORMATION TECHNOLOGY (CMVIT 2018), 2018, 1004
[50] A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems
Liu, Yuting
Ding, Hongwei
Wang, Zongshan
Jin, Gushen
Li, Bo
Yang, Zhijun
Dhiman, Gaurav
MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2023, 20 (09) : 17242 - 17271

← 1 2 3 4 5 →