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

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 61, P. 102464 - 102464

Published: March 15, 2024

Language: Английский

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Pied kingfisher optimizer: a new bio-inspired algorithm for solving numerical optimization and industrial engineering problems

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: May 16, 2024

Language: Английский

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Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

IET Renewable Power Generation, Journal Year: 2024, Volume and Issue: 18(6), P. 959 - 978

Published: Feb. 20, 2024

Abstract The pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial maximizing conversion efficiency. Here, a novel hybrid gazelle‐Nelder–Mead (GOANM) algorithm proposed and evaluated. GOANM synergistically integrates the gazelle optimization (GOA) with Nelder–Mead (NM) algorithm, offering an efficient powerful approach parameter extraction PV models. This investigation involves thorough assessment of algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed‐dimensional CEC2020 functions. Notably, consistently outperforms other approaches, demonstrating enhanced convergence speed, accuracy, reliability. Furthermore, application extended to single diode double models RTC France cell model Photowatt‐PWP201 module. experimental results demonstrate that approaches terms accurate estimation, low root mean square values, fast convergence, alignment data. These emphasize its role achieving superior efficiency renewable systems.

Language: Английский

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Artificial Ecosystem-Based Optimization with Dwarf Mongoose Optimization for Feature Selection and Global Optimization Problems

International Journal of Computational Intelligence Systems, Journal Year: 2023, Volume and Issue: 16(1)

Published: June 16, 2023

Abstract Meta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because their strong capabilities picking the optimal features and removing redundant irrelevant features. Artificial Ecosystem-based Optimization (AEO) shows extraordinary ability exploration stage poor exploitation its stochastic nature. Dwarf Mongoose Algorithm (DMOA) is recent MH algorithm showing high capability. This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO DMOA to develop an efficient FS with better equilibrium between exploitation. The performance investigated on seven datasets from different domains collection twenty-eight global optimization functions, eighteen CEC2017, ten CEC2019 benchmark functions. Comparative study statistical analysis demonstrate that gives competitive results statistically significant compared other popular approaches. function also indicate enhanced high-dimensional search space.

Language: Английский

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Novel rockburst prediction criterion with enhanced explainability employing CatBoost and nature-inspired metaheuristic technique

Underground Space, Journal Year: 2024, Volume and Issue: 19, P. 101 - 118

Published: June 13, 2024

Rockburst is a major challenge to hard rock engineering at great depth. Accurate and timely assessment of rockburst risk can avoid unnecessary casualties property losses. Despite the existence various methods for assessment, there remains an urgent need comprehensive reliable criterion that easy both apply interpret. Developing new based on simple parameters potentially fill this gap. With its advantages, facilitate more effective efficient prediction potential, thereby contributing significantly enhancing safety measures. In paper, combined with internal external factors rockburst, four control variables (i.e., integrity index, stress brittleness elastic energy index) were selected be incorporated into rockburstability index (RBSI). Based 116 sets cases, potential was accurately quantified predicted using categorical boosting (CatBoost) model nature-inspired metaheuristic African vultures optimization algorithm (AVOA). performance validation, achieved highest accuracy 95.45%, verifying reliability effectiveness proposed RBSI criterion. Additionally, interpretive method applied analyze variable influence criterion, facilitating explanation predictions analysis formula's robustness under different conditions. general, compared existing involving relevant indicators, newly enhances prediction, it effectively swiftly evaluate preliminary rockburst. Lastly, graphical user interface developed provide clear visualization potential.

Language: Английский

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A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 81, P. 469 - 488

Published: Sept. 22, 2023

There are many tricky optimization problems in real life, and metaheuristic algorithms the most effective way to solve at a lower cost. The dung beetle algorithm (DBO) is more innovative proposed 2022, which affected by action of beetles such as ball rolling, foraging, reproduction. Therefore, A based on quasi-oppositional learning Q-learning (QOLDBO). First, quantum state update idea cleverly integrated into increase randomness generated population. And best behavior pattern selected adding rolling stage improve search effect. In addition, variable spiral local domain method make up for shortage developing only around neighborhood optimum. For optimal solution each iteration, dimensional adaptive Gaussian variation retained. Experimental performance tests show that QOLDBO performs well both benchmark test functions CEC 2017. Simultaneously, validity verified several classical practical application engineering problems.

Language: Английский

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Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 73, P. 543 - 577

Published: May 11, 2023

Archimedes Optimization Algorithm (AOA) is a new physics-based optimizer that simulates principles. AOA has been used in variety of real-world applications because potential properties such as limited number control parameters, adaptability, and changing the set solutions to prevent being trapped local optima. Despite wide acceptance AOA, it some drawbacks, assumption individuals modify their locations depending on altered densities, volumes, accelerations. This causes various shortcomings stagnation into optimal regions, low diversity population, weakness exploitation phase, slow convergence curve. Thus, specific region conventional may be examined achieve balance between exploration capabilities AOA. The bird Swarm (BSA) an efficient strategy strong ability search process. In this study, hybrid called AOA-BSA proposed overcome limitations by replacing its phase with BSA one. Moreover, transition operator have high exploitation. To test examine performance, first experimental series, 29 unconstrained functions from CEC2017 whereas series second experiments use seven constrained engineering problems AOA-BSA's handling issues. performance suggested algorithm compared 10 optimizers. These are original algorithms 8 other algorithms. experiment's results show effectiveness optimizing suite. AOABSA outperforms metaheuristic across 16 functions. statically validated using Wilcoxon Rank sum. shows superior capability. due added power integration not only seen faster achieved AOABSA, but also found For further validation extensive statistical analysis performed during process recording ratios problems, achieves competitive curve reaches lowest values problem. It minimum standard deviation which indicates robustness solving these problems. Also, obtained counterparts regarding problem variables behavior best values.

Language: Английский

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An enhanced hunter‐prey optimization for optimal power flow with FACTS devices and wind power integration

IET Generation Transmission & Distribution, Journal Year: 2023, Volume and Issue: 17(14), P. 3115 - 3139

Published: June 1, 2023

Abstract This paper proposes an improved version of the Hunter‐prey optimization (HPO) method to enhance its search capabilities for solving Optimal Power Flow (OPF) problem, which includes FACTS devices and wind power energy integration. The new algorithm is inspired by behavior predator prey animals, such as lions, wolves, leopards, stags, gazelles. primary contribution this study address tendency original HPO approach get trapped in local optima, proposing enhanced (EHPO) that improves both exploration exploitation phases. achieved through a random mutation adaptive process exploitation, balances transition between two performance EHPO compared with other algorithms, subsequently, it used solve OPF problem incorporating power. results demonstrate effectiveness superiority proposed algorithm. In conclusion, successfully enhances provide better accuracy faster convergence finding optimal solutions complex real‐world problems.

Language: Английский

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HBWO-JS: jellyfish search boosted hybrid beluga whale optimization algorithm for engineering applications

Journal of Computational Design and Engineering, Journal Year: 2023, Volume and Issue: 10(4), P. 1615 - 1656

Published: June 27, 2023

Abstract Beluga whale optimization (BWO) algorithm is a recently proposed population intelligence algorithm. Inspired by the swimming, foraging, and falling behaviors of beluga populations, it shows good competitive performance compared to other state-of-the-art algorithms. However, original BWO faces challenges unbalanced exploration exploitation, premature stagnation iterations, low convergence accuracy in high-dimensional complex applications. Aiming at these challenges, hybrid based on jellyfish search optimizer (HBWO-JS), which combines vertical crossover operator Gaussian variation strategy with fusion (JS) optimizer, developed for solving global this paper. First, fused JS improve problem that tends fall into best local solution exploitation stage through multi-stage collaborative exploitation. Then, introduced cross solves processes normalizing upper lower bounds two stochastic dimensions agent, thus further improving overall capability. In addition, forces agent explore minimum neighborhood, extending entire iterative process alleviating Finally, superiority HBWO-JS verified detail comparing basic eight algorithms CEC2019 CEC2020 test suites, respectively. Also, scalability evaluated three (10D, 30D, 50D), results show stable terms dimensional scalability. practical engineering designs Truss topology problems demonstrate practicality HBWO-JS. The has strong ability broad application prospects.

Language: Английский

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Separation of fault characteristic impulses of flexible thin-wall bearing based on wavelet transform and correlated Gini index

Mechanical Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 209, P. 111118 - 111118

Published: Jan. 18, 2024

Language: Английский

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