Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization DOI Creative Commons
Xiaopeng Wang, Václav Snåšel, Seyedali Mirjalili

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 295, P. 111737 - 111737

Published: April 12, 2024

This study proposes a novel artificial protozoa optimizer (APO) that is inspired by in nature. The APO mimics the survival mechanisms of simulating their foraging, dormancy, and reproductive behaviors. was mathematically modeled implemented to perform optimization processes metaheuristic algorithms. performance verified via experimental simulations compared with 32 state-of-the-art Wilcoxon signed-rank test performed for pairwise comparisons proposed algorithms, Friedman used multiple comparisons. First, tested using 12 functions 2022 IEEE Congress on Evolutionary Computation benchmark. Considering practicality, solve five popular engineering design problems continuous space constraints. Moreover, applied multilevel image segmentation task discrete experiments confirmed could provide highly competitive results problems. source codes Artificial Protozoa Optimizer are publicly available at https://seyedalimirjalili.com/projects https://ww2.mathworks.cn/matlabcentral/fileexchange/162656-artificial-protozoa-optimizer.

Language: Английский

Osprey optimization algorithm: A new bio-inspired metaheuristic algorithm for solving engineering optimization problems DOI Creative Commons
Mohammad Dehghani, Pavel Trojovský

Frontiers in Mechanical Engineering, Journal Year: 2023, Volume and Issue: 8

Published: Jan. 20, 2023

This paper introduces a new metaheuristic algorithm named the Osprey Optimization Algorithm (OOA), which imitates behavior of osprey in nature. The fundamental inspiration OOA is strategy ospreys when hunting fish from seas. In this strategy, hunts prey after detecting its position, then carries it to suitable position eat it. proposed approach two phases exploration and exploitation mathematically modeled based on simulation natural during process. performance has been evaluated optimization twenty-nine standard benchmark functions CEC 2017 test suite. Furthermore, compared with twelve well-known algorithms. results show that provided superior competitor algorithms by maintaining balance between exploitation. addition, implementation twenty-two real-world constrained problems 2011 suite shows high capability optimizing applications.

Language: Английский

Citations

273

Liver Cancer Algorithm: A novel bio-inspired optimizer DOI
Essam H. Houssein, Diego Oliva, Nagwan Abdel Samee

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 165, P. 107389 - 107389

Published: Aug. 30, 2023

Language: Английский

Citations

149

Crested Porcupine Optimizer: A new nature-inspired metaheuristic DOI
Mohamed Abdel‐Basset, Reda Mohamed, Mohamed Abouhawwash

et al.

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 284, P. 111257 - 111257

Published: Dec. 22, 2023

Language: Английский

Citations

144

Subtraction-Average-Based Optimizer: A New Swarm-Inspired Metaheuristic Algorithm for Solving Optimization Problems DOI Creative Commons
Pavel Trojovský, Mohammad Dehghani

Biomimetics, Journal Year: 2023, Volume and Issue: 8(2), P. 149 - 149

Published: April 6, 2023

This paper presents a new evolutionary-based approach called Subtraction-Average-Based Optimizer (SABO) for solving optimization problems. The fundamental inspiration of the proposed SABO is to use subtraction average searcher agents update position population members in search space. different steps SABO's implementation are described and then mathematically modeled tasks. performance tested fifty-two standard benchmark functions, consisting unimodal, high-dimensional multimodal, fixed-dimensional multimodal types, CEC 2017 test suite. results show that effectively solves problems by balancing exploration exploitation process problem-solving compared with twelve well-known metaheuristic algorithms. analysis simulation shows provides superior most functions. Furthermore, it much more competitive outstanding than its competitor Additionally, implemented four engineering design evaluate handling tasks real-world applications. can solve applications optimal designs

Language: Английский

Citations

131

An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm DOI
Essam H. Houssein, Doaa A. Abdelkareem,

Marwa M. Emam

et al.

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 149, P. 106075 - 106075

Published: Sept. 6, 2022

Language: Английский

Citations

116

Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problems DOI Creative Commons
Jun Wang, Wenchuan Wang,

Xiao-xue Hu

et al.

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(4)

Published: March 23, 2024

Abstract This paper innovatively proposes the Black Kite Algorithm (BKA), a meta-heuristic optimization algorithm inspired by migratory and predatory behavior of black kite. The BKA integrates Cauchy mutation strategy Leader to enhance global search capability convergence speed algorithm. novel combination achieves good balance between exploring solutions utilizing local information. Against standard test function sets CEC-2022 CEC-2017, as well other complex functions, attained best performance in 66.7, 72.4 77.8% cases, respectively. effectiveness is validated through detailed analysis statistical comparisons. Moreover, its application solving five practical engineering design problems demonstrates potential addressing constrained challenges real world indicates that it has significant competitive strength comparison with existing techniques. In summary, proven value advantages variety due excellent performance. source code publicly available at https://www.mathworks.com/matlabcentral/fileexchange/161401-black-winged-kite-algorithm-bka .

Language: Английский

Citations

108

Walrus optimizer: A novel nature-inspired metaheuristic algorithm DOI
Muxuan Han, Zunfeng Du, Kum Fai Yuen

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 239, P. 122413 - 122413

Published: Nov. 3, 2023

Language: Английский

Citations

104

Boosted sooty tern optimization algorithm for global optimization and feature selection DOI
Essam H. Houssein, Diego Oliva, Emre Çelik

et al.

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 213, P. 119015 - 119015

Published: Oct. 17, 2022

Language: Английский

Citations

94

A Sinh Cosh optimizer DOI
Jianfu Bai, Yifei Li, Mingpo Zheng

et al.

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 282, P. 111081 - 111081

Published: Oct. 18, 2023

Language: Английский

Citations

93

Model parameters estimation of the proton exchange membrane fuel cell by a Modified Golden Jackal Optimization DOI

Mehrdad Rezaie,

Keyvan Karamnejadi Azar,

Armin kardan sani

et al.

Sustainable Energy Technologies and Assessments, Journal Year: 2022, Volume and Issue: 53, P. 102657 - 102657

Published: Sept. 12, 2022

Language: Английский

Citations

90