Dung Beetle Optimization Algorithm Based on Improved Multi-Strategy Fusion

Electronics, Год журнала: 2025, Номер 14(1), С. 197 - 197

Опубликована: Янв. 5, 2025

The Dung Beetle Optimization Algorithm (DBO) is characterized by its great convergence accuracy and quick speed. However, like other swarm intelligent optimization algorithms, it also has the disadvantages of having an unbalanced ability to explore world use local resources, as well being prone settling into optimal search in latter stages optimization. In order address these issues, this research suggests a multi-strategy fusion dung beetle method (MSFDBO). To enhance quality first solution, refractive reverse learning technique expands algorithm space stage. algorithm’s increased adding adaptive curve control population size prevent from reaching optimum. improve balance exploitation global exploration, respectively, triangle wandering strategy subtractive averaging optimizer were later added Rolling Breeding Beetle. Individual beetles will congregate at current position, which near value, during last stage MSFDBO; however, value could not be value. Thus, variationally perturb solution (so that leaps out final MSFDBO) algorithmic performance (generally specifically, effect optimizing search), Gaussian–Cauchy hybrid variational perturbation factor introduced. Using CEC2017 benchmark function, MSFDBO’s verified comparing seven different intelligence algorithms. MSFDBO ranks terms average performance. can lower labor production expenses associated with welding beam reducer design after testing two engineering application challenges. When comes lowering manufacturing costs overall weight, outperforms methods.

Язык: Английский

---

GOHBA: Improved Honey Badger Algorithm for Global Optimization

Biomimetics, Год журнала: 2025, Номер 10(2), С. 92 - 92

Опубликована: Фев. 6, 2025

Aiming at the problem that honey badger algorithm easily falls into local convergence, insufficient global search ability, and low convergence speed, this paper proposes a optimization (Global Optimization HBA) (GOHBA), which improves ability of population, with better to jump out optimum, faster stability. The introduction Tent chaotic mapping initialization enhances population diversity initializes quality HBA. Replacing density factor range in entire solution space avoids premature optimum. addition golden sine strategy capability HBA accelerates speed. Compared seven algorithms, GOHBA achieves optimal mean value on 14 23 tested functions. On two real-world engineering design problems, was optimal. three path planning had higher accuracy convergence. above experimental results show performance is indeed excellent.

Язык: Английский

---

Reinforcement learning-based comprehensive learning grey wolf optimizer for feature selection

Applied Soft Computing, Год журнала: 2023, Номер 149, С. 110959 - 110959

Опубликована: Окт. 19, 2023

Язык: Английский

---

Improved GWO and its application in parameter optimization of Elman neural network

PLoS ONE, Год журнала: 2023, Номер 18(7), С. e0288071 - e0288071

Опубликована: Июль 7, 2023

Traditional neural networks used gradient descent methods to train the network structure, which cannot handle complex optimization problems. We proposed an improved grey wolf optimizer (SGWO) explore a better structure. GWO was by using circle population initialization, information interaction mechanism and adaptive position update enhance search performance of algorithm. SGWO applied optimize Elman new prediction method (SGWO-Elman) proposed. The convergence analyzed mathematical theory, ability SGWO-Elman were examined comparative experiments. results show: (1) global probability 1, its process finite homogeneous Markov chain with absorption state; (2) not only has when solving functions different dimensions, but also for parameter optimization, can significantly structure accurate performance.

Язык: Английский

---

CMGWO: Grey wolf optimizer for fusion cell-like P systems

Heliyon, Год журнала: 2024, Номер 10(14), С. e34496 - e34496

Опубликована: Июль 1, 2024

The grey wolf optimizer is a widely used parametric optimization algorithm. It affected by the structure and rank of wolves prone to falling into local optimum. In this study, we propose for fusion cell-like P systems. Cell-like systems can parallelize computation communicate from cell membrane membrane, which help jump out Design new convergence factors use different in other membranes balance overall exploration utilization capabilities At same time, dynamic weights are introduced accelerate speed Experiments performed on 24 test functions verify their global performance. Meanwhile, support vector machine model optimized has been developed tested six benchmark datasets. Finally, optimizing ability constrained problems verified three real engineering design problems. Compared with algorithms, obtains higher accuracy faster function, at it find better parameter set stably parameters, addition being more competitive results show that improves searching population, optimum, speed, stability.

Язык: Английский

---

Carbon Dioxide Storage and Cumulative Oil Production Predictions in Unconventional Reservoirs Applying Optimized Machine-Learning Models

Petroleum Science, Год журнала: 2024, Номер unknown

Опубликована: Сен. 1, 2024

Язык: Английский

---

An optimization method for wireless sensor networks coverage based on genetic algorithm and reinforced whale algorithm

Mathematical Biosciences & Engineering, Год журнала: 2024, Номер 21(2), С. 2787 - 2812

Опубликована: Янв. 1, 2024

<abstract> <p>In response to the problem of coverage redundancy and holes caused by random deployment nodes in wireless sensor networks (WSN), a WSN optimization method called GARWOA is proposed, which combines genetic algorithm (GA) reinforced whale (RWOA) balance global search local development performance. First, population initialized using sine map piecewise linear chaotic (SPM) distribute it more evenly space. Secondly, non-linear improvement made control factor 'a' (WOA) enhance efficiency exploration development. Finally, Levy flight mechanism introduced improve algorithm's tendency fall into optima premature convergence phenomena. Simulation experiments indicate that among 10 standard test functions, outperforms other algorithms with better ability. In three experiments, ratio 95.73, 98.15, 99.34%, 3.27, 2.32 0.87% higher than mutant grey wolf optimizer (MuGWO), respectively.</p> </abstract>

Язык: Английский

---

A Velocity-Guided Grey Wolf Optimization Algorithm With Adaptive Weights and Laplace Operators for Feature Selection in Data Classification

IEEE Access, Год журнала: 2024, Номер 12, С. 39887 - 39901

Опубликована: Янв. 1, 2024

The rapid growth of data quantity directly leads to the increasing feature dimension, which challenges machine learning and mining. Wrapper-based intelligent swarm algorithms are effective solution techniques. Grey Wolf Optimization (GWO) algorithm is a novel population algorithm. Simple principles few parameters characterize it. However, basic GWO has disadvantages, such as difficulty coordinating exploration exploitation capabilities premature convergence. As result, fails identify many irrelevant redundant features. To improve performance algorithm, this paper proposes velocity-guided grey wolf optimization with adaptive weights Laplace operators (VGWO-AWLO). Firstly, by introducing uniformly distributed dynamic weighting mechanism, control $a$ guided undergo nonlinear changes achieve good transition from exploratory phase development phase. Second, velocity-based position update formula designed an individual memory function enhance local search capability wolves drive them converge optimal solution. Thirdly, cross-operator strategy applied increase diversity help escape Finally, VGWO-AWLO evaluated for its comprehensive in terms classification accuracy, dimensionality approximation, convergence, stability 18 classified datasets. experimental results show that accuracy convergence speed better than GWO, variants, other state-of-the-art meta-heuristic algorithms.

Язык: Английский

---

Integrated Multi-strategy Sand Cat Swarm Optimization for Path Planning Applications

Intelligent Systems with Applications, Год журнала: 2025, Номер unknown, С. 200486 - 200486

Опубликована: Янв. 1, 2025

Язык: Английский

---

Time-varying elite sand cat optimisation algorithms for engineering design and feature selection

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127026 - 127026

Опубликована: Март 1, 2025

Язык: Английский

---