Path planning and engineering problems of 3D UAV based on adaptive coati optimization algorithm DOI Creative Commons
Chuan Jia, Ling He, Dan Liu

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 28, 2024

In response to the challenges faced by Coati Optimization Algorithm (COA), including imbalance between exploration and exploitation, slow convergence speed, susceptibility local optima, low accuracy, this paper introduces an enhanced variant termed Adaptive (ACOA). ACOA achieves a balanced exploration–exploitation trade-off through refined strategies developmental methodologies. It integrates chaos mapping enhance randomness global search capabilities incorporates dynamic antagonistic learning approach employing random protons mitigate premature convergence, thereby enhancing algorithmic robustness. Additionally, prevent entrapment in Levy Flight strategy maintain population diversity, improving accuracy. Furthermore, underperforming individuals are eliminated using cosine disturbance-based differential evolution overall quality of population. The efficacy is assessed across four dimensions: balance, characteristics, diverse variations. Ablation experiments further validate effectiveness individual modules. Experimental results on CEC-2017 CEC-2022 benchmarks, along with Wilcoxon rank-sum tests, demonstrate superior performance compared COA other state-of-the-art optimization algorithms. Finally, ACOA's applicability superiority reaffirmed experimentation five real-world engineering complex urban three-dimensional unmanned aerial vehicle (UAV) path planning problem.

Язык: Английский

Optimization based on the smart behavior of plants with its engineering applications: Ivy algorithm DOI
Mojtaba Ghasemi, Mohsen Zare, Pavel Trojovský

и другие.

Knowledge-Based Systems, Год журнала: 2024, Номер 295, С. 111850 - 111850

Опубликована: Апрель 22, 2024

Язык: Английский

Процитировано

35
SDO: A novel sled dog-inspired optimizer for solving engineering problems DOI
Gang Hu,

Cheng Mao,

Essam H. Houssein

и другие.

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102783 - 102783

Опубликована: Авг. 28, 2024

Процитировано

13
Rapid identification of Liubao tea vintage based on terahertz spectroscopy combined with improved differentiated creative search algorithm DOI
Huo Zhang, Guanglei Li, Chunyu Guo

и другие.

Spectroscopy Letters, Год журнала: 2025, Номер unknown, С. 1 - 16

Опубликована: Янв. 10, 2025

As a special microbial fermented tea, the aging year of Liubao tea is crucial determinant its value. This study established fast and high-precision method for identifying age by combining terahertz time-domain spectroscopy technology with chemometric methods. Most common optimization algorithms rely too much on guidance elite individuals in process are prone to fall into local optimal solutions. Therefore, this paper uses differentiated creative search algorithm global thinking optimize support vector machine model parameters. To address problem poor results due unclear goals algorithm's convergence divergence processes, guided learning strategy employed balance these schemes within algorithm. approach yields classification higher efficiency. Compared models optimized Genetic Algorithm, Particle Swarm Optimization, algorithm, new achieved best performance, an accuracy 96.87% F1 score 0.9683. The indicate that can updating scheme enables accurate qualitative analysis offering feasible solution applying identification.

Язык: Английский

Процитировано

1
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations DOI
Daniel Molina, Javier Poyatos, Javier Del Ser

и другие.

Cognitive Computation, Год журнала: 2020, Номер 12(5), С. 897 - 939

Опубликована: Июль 5, 2020

Язык: Английский

Процитировано

41
Improved sandcat swarm optimization algorithm for solving global optimum problems DOI Creative Commons
Heming Jia, Jinrui Zhang, Honghua Rao

и другие.

Artificial Intelligence Review, Год журнала: 2024, Номер 58(1)

Опубликована: Ноя. 4, 2024

The sand cat swarm optimization algorithm (SCSO) is a metaheuristic proposed by Amir Seyyedabbasi et al. SCSO mimics the predatory behavior of cats, which gives strong optimized performance. However, as number iterations increases, moving efficiency decreases, resulting in decline search ability. convergence speed gradually and it easy to fall into local optimum, difficult find better solution. In order improve movement cat, enhance global ability performance algorithm, an improved Swarm Optimization (ISCSO) was proposed. ISCSO we propose low-frequency noise strategy spiral contraction walking according habit add random opposition-based learning restart strategy. frequency factor used control direction hunting carried out, effectively randomness population, expanded range enhanced accelerated algorithm. We use 23 standard benchmark functions IEEE CEC2014 compare with 10 algorithms, prove effectiveness Finally, evaluated using five constrained engineering design problems. results these problems, has 3.08%, 0.23%, 0.37%, 22.34%, 1.38% improvement compared original respectively, proves practical application source code website for https://github.com/Ruiruiz30/ISCSO-s-code.

Язык: Английский

Процитировано

4
Multi-strategy enhanced dandelion optimizer based on elliptic approximation strategy and adaptive fitness-distance-similarity balance for solar photovoltaic parameter estimation DOI
Tianbao Liu, Yufeng Zhang

The Journal of Supercomputing, Год журнала: 2025, Номер 81(3)

Опубликована: Фев. 3, 2025

Язык: Английский

Процитировано

0
An improved termite life cycle optimizer algorithm for global function optimization DOI Creative Commons
Yanjiao Wang, M. Wei

PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2671 - e2671

Опубликована: Фев. 17, 2025

The termite life cycle optimizer algorithm (TLCO) is a new bionic meta-heuristic that emulates the natural behavior of termites in their habitat. This work presents an improved TLCO (ITLCO) to increase speed and accuracy convergence. A novel strategy for worker generation established enhance communication between individuals population population. would prevent original from effectively balancing convergence diversity reduce risk reaching local optimum. soldier proposed, which incorporates step factor adheres principles evolution further algorithm's speed. Furthermore, replacement update mechanism executed when individual lower quality than individual. ensures balance findings CEC2013, CEC2019, CEC2020 test sets indicate ITLCO exhibits notable benefits regarding speed, accuracy, stability comparison with basic four most exceptional algorithms thus far.

Язык: Английский

Процитировано

0
A Labor Division Artificial Gorilla Troops Algorithm for Engineering Optimization DOI Creative Commons

C. L. Liu,

Bowen Wu, Liangkuan Zhu

и другие.

Biomimetics, Год журнала: 2025, Номер 10(3), С. 127 - 127

Опубликована: Фев. 20, 2025

The Artificial Gorilla Troops Optimizer (GTO) has emerged as an efficient metaheuristic technique for solving complex optimization problems. However, the conventional GTO algorithm a critical limitation: all individuals, regardless of their roles, utilize identical search equations and perform exploration exploitation sequentially. This uniform approach neglects potential benefits labor division, consequently restricting algorithm’s performance. To address this limitation, we propose enhanced Labor Division (LDGTO), which incorporates natural mechanisms division outcome allocation. In phase, stimulus-response model is designed to differentiate tasks, enabling gorilla individuals adaptively adjust based on environmental changes. allocation three behavioral development modes—self-enhancement, competence maintenance, elimination—are implemented, corresponding developmental stages: elite, average, underperforming individuals. performance LDGTO rigorously evaluated through benchmark test suites, comprising 12 unimodal, 25 multimodal, 10 combinatorial functions, well two real-world engineering applications, including four-bar transplanter mechanism design color image segmentation. Experimental results demonstrate that consistently outperforms variants seven state-of-the-art algorithms in most cases.

Язык: Английский

Процитировано

0
A review on metaheuristic algorithms: Recent and future trends DOI
M. Santoshi Kumari

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 103 - 128

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Atom Search Optimization: a comprehensive review of its variants, applications, and future directions DOI Creative Commons
M.A. El‐Shorbagy, Anas Bouaouda, Laith Abualigah

и другие.

PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2722 - e2722

Опубликована: Фев. 28, 2025

The Atom Search Optimization (ASO) algorithm is a recent advancement in metaheuristic optimization inspired by principles of molecular dynamics. It mathematically models and simulates the natural behavior atoms, with interactions governed forces derived from Lennard-Jones potential constraint based on bond-length potentials. Since its inception 2019, it has been successfully applied to various challenges across diverse fields technology science. Despite notable achievements rapidly growing body literature ASO domain, comprehensive study evaluating success implementations still lacking. To address this gap, article provides thorough review half decade advancements research, synthesizing wide range studies highlight key variants, their foundational principles, significant achievements. examines applications, including single- multi-objective problems, introduces well-structured taxonomy guide future exploration ASO-related research. reviewed reveals that several variants algorithm, modifications, hybridizations, implementations, have developed tackle complex problems. Moreover, effectively domains, such as engineering, healthcare medical Internet Things communication, clustering data mining, environmental modeling, security, engineering emerging most prevalent application area. By addressing common researchers face selecting appropriate algorithms for real-world valuable insights into practical applications offers guidance designing tailored specific

Язык: Английский

Процитировано

0