MSAO: A multi-strategy boosted snow ablation optimizer for global optimization and real-world engineering applications

Advanced Engineering Informatics, Год журнала: 2024, Номер 61, С. 102464 - 102464

Опубликована: Март 15, 2024

Язык: Английский

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Enhanced multi-strategy bottlenose dolphin optimizer for UAVs path planning

Applied Mathematical Modelling, Год журнала: 2024, Номер 130, С. 243 - 271

Опубликована: Март 11, 2024

Язык: Английский

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A novel chaotic artificial rabbits algorithm for optimization of constrained engineering problems

Materials Testing, Год журнала: 2024, Номер 66(9), С. 1449 - 1462

Опубликована: Май 27, 2024

Abstract This study introduces a novel metaheuristic algorithm of optimization named Chaotic Artificial Rabbits Optimization (CARO) for resolving engineering design problems. In the newly introduced CARO algorithm, ten different chaotic maps are used with recently presented (ARO) to manage its parameters, eventually leading an improved exploration and exploitation search. The familiar competitor algorithms were experimented on renowned five mechanical problems design, in brief; pressure vessel rolling element bearing tension/compression spring cantilever beam gear train design. results indicate that is outstanding compared algorithms, equipped best-optimized parameters minimal deviation each case study. Metaheuristic utilized succeed optimal targeting achieve lightweight designs. this present study, optimum vehicle brake pedal piece was achieved through topology shape methods. problem terms mass minimization solved properly by using comparison literature. Consequently, can be effectively

Язык: Английский

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Artificial lemming algorithm: a novel bionic meta-heuristic technique for solving real-world engineering optimization problems

Artificial Intelligence Review, Год журнала: 2025, Номер 58(3)

Опубликована: Янв. 6, 2025

The advent of the intelligent information era has witnessed a proliferation complex optimization problems across various disciplines. Although existing meta-heuristic algorithms have demonstrated efficacy in many scenarios, they still struggle with certain challenges such as premature convergence, insufficient exploration, and lack robustness high-dimensional, nonconvex search spaces. These limitations underscore need for novel techniques that can better balance exploration exploitation while maintaining computational efficiency. In response to this need, we propose Artificial Lemming Algorithm (ALA), bio-inspired metaheuristic mathematically models four distinct behaviors lemmings nature: long-distance migration, digging holes, foraging, evading predators. Specifically, migration burrow are dedicated highly exploring domain, whereas foraging predators provide during process. addition, ALA incorporates an energy-decreasing mechanism enables dynamic adjustments between exploitation, thereby enhancing its ability evade local optima converge global solutions more robustly. To thoroughly verify effectiveness proposed method, is compared 17 other state-of-the-art on IEEE CEC2017 benchmark test suite CEC2022 suite. experimental results indicate reliable comprehensive performance achieve superior solution accuracy, convergence speed, stability most cases. For 29 10-, 30-, 50-, 100-dimensional functions, obtains lowest Friedman average ranking values among all competitor methods, which 1.7241, 2.1034, 2.7241, 2.9310, respectively, 12 again wins optimal 2.1667. Finally, further evaluate applicability, implemented address series cases, including constrained engineering design, photovoltaic (PV) model parameter identification, fractional-order proportional-differential-integral (FOPID) controller gain tuning. Our findings highlight competitive edge potential real-world applications. source code publicly available at https://github.com/StevenShaw98/Artificial-Lemming-Algorithm .

Язык: Английский

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A Comprehensive Survey on Aquila Optimizer

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 30(7), С. 4449 - 4476

Опубликована: Июнь 7, 2023

Язык: Английский

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Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment

IEEE Access, Год журнала: 2023, Номер 11, С. 100052 - 100069

Опубликована: Янв. 1, 2023

An Electrocardiogram (ECG) is a non-invasive test that broadly utilized for monitoring and diagnosing the cardiac arrhythmia. irregularity of heartbeat generally defined as arrhythmia, which potentially causes fatal difficulties creates an instantaneous life risk. Therefore, arrhythmia classification challenging task because overfitting issue caused by high dimensional feature space ECG signal. In this research, incorporation Internet Medical Things (IoMT) developed with artificial intelligence to provide health people who are having work, time, time-frequency, entropy, nonlinearity features deep from Convolutional Neural Network (CNN) extracted obtain different categories signal features. The Selective Opposition (SO) strategy based Artificial Rabbits Optimization (SOARO) proposed selecting optimal subset overall avoid issue. chosen used improve done Auto Encoder (AE). Further, Shapley additive explanations (SHAP) model interpret classified output AE. MIT-BIH database evaluating SOARO-AE. performance SOARO-AE evaluated using accuracy, sensitivity, specificity, recall F1-Measure. existing researches such C-LSTM, DL-LAC-CNN, CNN-DNN, MC-ECG, FC MEAHA-CNN evaluate method. accuracy 98.89% when compared MEAHA-CNN.

Язык: Английский

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Modified random-oppositional chaotic artificial rabbit optimization algorithm for solving structural problems and optimal sizing of hybrid renewable energy system

Evolutionary Intelligence, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 9, 2025

Язык: Английский

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Multi-strategy boosted Aquila optimizer for function optimization and engineering design problems

Cluster Computing, Год журнала: 2024, Номер 27(6), С. 7147 - 7198

Опубликована: Март 14, 2024

Язык: Английский

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Opposition-Based Chaotic Tunicate Swarm Algorithms for Global Optimization

IEEE Access, Год журнала: 2024, Номер 12, С. 18168 - 18188

Опубликована: Янв. 1, 2024

Tunicate Swarm Algorithm (TSA) is a novel swarm intelligence algorithm developed in 2020. Though it has shown superior performance numerical benchmark function optimization and six engineering design problems over its competitive algorithms, still needs further improvements. This article proposes two improved TSA algorithms using chaos theory, opposition-based learning (OBL) Cauchy mutation. The proposed are termed OCSTA COCSTA. static dynamic OBL used respectively the initialization generation jumping phase of OCTSA, whereas centroid computing used, same phases, COCTSA. tested on 30 IEEE CEC2017 consists unimodal, multimodal, hybrid, composite functions with 30, 50, 100 dimensions. experimental results compared classical TSA, local escaping operator (TSA-LEO), Sine Cosine (SCA), Giza-Pyramid Construction (GPC), Covariance Matrix Adaptation Evolution Strategy (CMAES), Archimedes Optimization (AOA), Opposition-Based Arithmetic (OBLAOA), Chimp (ChOAOBL). statistical analysis Wilcoxon Signed Rank Test establishes that outperform other for most problems. Moreover, high dimensions to validate scalability OCTSA COCTSA, show modified least impacted by larger demonstrate effectiveness solving global

Язык: Английский

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Modified artificial rabbits optimization combined with bottlenose dolphin optimizer in feature selection of network intrusion detection

Electronic Research Archive, Год журнала: 2024, Номер 32(3), С. 1770 - 1800

Опубликована: Янв. 1, 2024

<p>For the feature selection of network intrusion detection, issue numerous redundant features arises, posing challenges in enhancing detection accuracy and adversely affecting overall performance to some extent. Artificial rabbits optimization (ARO) is capable reducing can be applied for detection. The ARO exhibits a slow iteration speed exploration phase population prone an iterative stagnation condition exploitation phase, which hinders its ability deliver outstanding aforementioned problems. First, enhance global capabilities further, thinking incorporates mud ring feeding strategy from bottlenose dolphin optimizer (BDO). Simultaneously, adjusting phases, employs adaptive switching mechanism. Second, avoid original algorithm getting trapped local optimum during levy flight adopted. Lastly, dynamic lens-imaging introduced variety facilitate escape optimum. Then, this paper proposes modified ARO, namely LBARO, hybrid that combines BDO model. LBARO first empirically evaluated comprehensively demonstrate superiority proposed algorithm, using 8 benchmark test functions 4 UCI datasets. Subsequently, integrated into process model classification experimental validation. This integration validated utilizing NSL-KDD, UNSW NB-15, InSDN datasets, respectively. Experimental results indicate based on successfully reduces characteristics while detection.</p>

Язык: Английский

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