A Sinh Cosh optimizer

Knowledge-Based Systems, Год журнала: 2023, Номер 282, С. 111081 - 111081

Опубликована: Окт. 18, 2023

Язык: Английский

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A Literature Review and Critical Analysis of Metaheuristics Recently Developed

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 31(1), С. 125 - 146

Опубликована: Июль 22, 2023

Abstract Metaheuristic algorithms have applicability in various fields where it is necessary to solve optimization problems. It has been a common practice this field for several years propose new that take inspiration from natural and physical processes. The exponential increase of controversial issue researchers criticized. However, their efforts point out multiple issues involved these practices insufficient since the number existing metaheuristics continues yearly. To know current state problem, paper analyzes sample 111 recent studies so-called new, hybrid, or improved are proposed. Throughout document, topics reviewed will be addressed general perspective specific aspects. Among study’s findings, observed only 43% analyzed papers make some mention No Free Lunch (NFL) theorem, being significant result ignored by most presented. Of studies, 65% present an version established algorithm, which reveals trend no longer based on analogies. Additionally, compilation solutions found engineering problems commonly used verify performance state-of-the-art demonstrate with low level innovation can erroneously considered as frameworks years, known Black Widow Optimization Coral Reef analyzed. study its components they do not any innovation. Instead, just deficient mixtures different evolutionary operators. This applies extension recently proposed versions.

Язык: Английский

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IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems

Mathematical Biosciences & Engineering, Год журнала: 2022, Номер 19(11), С. 10963 - 11017

Опубликована: Янв. 1, 2022

<abstract><p>Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) are two newly developed meta-heuristic algorithms that simulate several intelligent hunting behaviors of Aquila vulture in nature, respectively. AO has powerful global exploration capability, whereas its local exploitation phase is not stable enough. On the other hand, AVOA possesses promising capability but insufficient mechanisms. Based on characteristics both algorithms, this paper, we propose an improved hybrid optimizer called IHAOAVOA to overcome deficiencies single algorithm provide higher-quality solutions for solving optimization problems. First, combined retain valuable search competence each. Then, a new composite opposition-based learning (COBL) designed increase population diversity help escape from optima. In addition, more effectively guide process balance exploitation, fitness-distance (FDB) selection strategy introduced modify core position update formula. The performance proposed comprehensively investigated analyzed by comparing against basic AO, AVOA, six state-of-the-art 23 classical benchmark functions IEEE CEC2019 test suite. Experimental results demonstrate achieves superior solution accuracy, convergence speed, optima avoidance than comparison methods most functions. Furthermore, practicality highlighted five engineering design Our findings reveal technique also highly competitive when addressing real-world tasks. source code publicly available at <a href="https://doi.org/10.24433/CO.2373662.v1" target="_blank">https://doi.org/10.24433/CO.2373662.v1</a>.</p></abstract>

Язык: Английский

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Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification

Mathematics, Год журнала: 2022, Номер 10(20), С. 3821 - 3821

Опубликована: Окт. 16, 2022

Nature-inspired metaheuristic algorithms have gained great attention over the last decade due to their potential for finding optimal solutions different optimization problems. In this study, a based on dwarf mongoose algorithm (DMOA) is presented parameter estimation of an autoregressive exogenous (ARX) model. DMOA, set candidate were stochastically created and improved using only one tuning parameter. The performance DMOA ARX identification was deeply investigated in terms its convergence speed, accuracy, robustness reliability. Furthermore, comparative analyses with other recent state-of-the-art metaheuristics Aquila Optimizer, Sine Cosine Algorithm, Arithmetic Optimization Algorithm Reptile Search algorithm—using nonparametric Kruskal–Wallis test—endorsed consistent, accurate proposed identification.

Язык: Английский

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A Comprehensive Survey on Arithmetic Optimization Algorithm

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 30(5), С. 3379 - 3404

Опубликована: Март 15, 2023

Язык: Английский

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A chimp-inspired remora optimization algorithm for multilevel thresholding image segmentation using cross entropy

Artificial Intelligence Review, Год журнала: 2023, Номер 56(S1), С. 159 - 216

Опубликована: Июнь 23, 2023

Язык: Английский

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Research on energy-saving optimization method for central air conditioning system based on multi-strategy improved sparrow search algorithm

International Journal of Refrigeration, Год журнала: 2024, Номер 160, С. 263 - 274

Опубликована: Янв. 12, 2024

Aiming at the problem of high energy consumption caused by nonlinear, strong coupling, and large hysteresis characteristics central air conditioning (CAC) system, an energy-saving optimization method based on WTD-CNN-LSTM multi-strategy improved sparrow search algorithm (MISSA) is proposed, which works to minimize total during operation CAC system. Firstly, sum chillers, freezing pumps, cooling towers as objective function, use range operating parameters each equipment basic constraint conditions establish model for Then, used predict future load, predicted results are key condition achieve on-demand cooling. Finally, MISSA proposed improve initialization, update convergence stages system parameter process, accurately obtaining optimal parameters. Compared with manual experience, reduces 15.32% improves efficiency ratio 20.76%. Meanwhile, compared other algorithms, 4.85% - 13.26% 6.53% 16.33% after optimizing The experiment verifies that more efficient when applied has advantages accuracy, fast convergence, global capability, ability jump out local optimization.

Язык: Английский

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Dynamic Chaotic Opposition-Based Learning-Driven Hybrid Aquila Optimizer and Artificial Rabbits Optimization Algorithm: Framework and Applications

Processes, Год журнала: 2022, Номер 10(12), С. 2703 - 2703

Опубликована: Дек. 14, 2022

Aquila Optimizer (AO) and Artificial Rabbits Optimization (ARO) are two recently developed meta-heuristic optimization algorithms. Although AO has powerful exploration capability, it still suffers from poor solution accuracy premature convergence when addressing some complex cases due to the insufficient exploitation phase. In contrast, ARO possesses very competitive potential, but its ability needs be more satisfactory. To ameliorate above-mentioned limitations in a single algorithm achieve better overall performance, this paper proposes novel chaotic opposition-based learning-driven hybrid called CHAOARO. Firstly, global phase of is combined with local maintain respective valuable search capabilities. Then, an adaptive switching mechanism (ASM) designed balance procedures. Finally, we introduce learning (COBL) strategy avoid fall into optima. comprehensively verify effectiveness superiority proposed work, CHAOARO compared original AO, ARO, several state-of-the-art algorithms on 23 classical benchmark functions IEEE CEC2019 test suite. Systematic comparisons demonstrate that can significantly outperform other competitor methods terms accuracy, speed, robustness. Furthermore, promising prospect real-world applications highlighted by resolving five industrial engineering design problems photovoltaic (PV) model parameter identification problem.

Язык: Английский

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Advanced RIME architecture for global optimization and feature selection

Journal Of Big Data, Год журнала: 2024, Номер 11(1)

Опубликована: Июнь 18, 2024

Abstract The article introduces an innovative approach to global optimization and feature selection (FS) using the RIME algorithm, inspired by RIME-ice formation. algorithm employs a soft-RIME search strategy hard-RIME puncture mechanism, along with improved positive greedy resist getting trapped in local optima enhance its overall capabilities. also Binary modified (mRIME), binary adaptation of address unique challenges posed FS problems, which typically involve spaces. Four different types transfer functions (TFs) were selected for issues, their efficacy was investigated CEC2011 CEC2017 tasks related disease diagnosis. results proposed mRIME tested on ten reliable algorithms. advanced architecture demonstrated superior performance tasks, providing effective solution complex problems various domains.

Язык: Английский

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Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization

Mathematics, Год журнала: 2022, Номер 10(23), С. 4509 - 4509

Опубликована: Ноя. 29, 2022

An efficient optimization method is needed to address complicated problems and find optimal solutions. The gazelle algorithm (GOA) a global stochastic optimizer that straightforward comprehend has powerful search capabilities. Nevertheless, the GOA unsuitable for addressing multimodal, hybrid functions, data mining problems. Therefore, current paper proposes orthogonal learning (OL) with Rosenbrock’s direct rotation strategy improve sustain solution variety (IGOA). We performed comprehensive experiments based on various including 23 classical IEEE CEC2017 Moreover, eight clustering taken from UCI repository were tested verify proposed method’s performance further. IGOA was compared several other meta-heuristic algorithms. Wilcoxon signed-rank test further assessed experimental results conduct more systematic analyses. surpassed comparative optimizers in terms of convergence speed precision. empirical show achieved better outcomes than basic state-of-the-art methods quality.

Язык: Английский

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