Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 30(4), С. 2683 - 2723

Опубликована: Янв. 12, 2023

Язык: Английский

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Improved bald eagle search algorithm for global optimization and feature selection

Alexandria Engineering Journal, Год журнала: 2023, Номер 68, С. 141 - 180

Опубликована: Янв. 18, 2023

The use of metaheuristics is one the most encouraging methodologies for taking care real-life problems. Bald eagle search (BES) algorithm latest swarm-intelligence metaheuristic inspired by intelligent hunting behavior bald eagles. In recent research works, BES has performed reasonably well over a wide range application areas such as chemical engineering, environmental science, physics and astronomy, structural modeling, global optimization, engineering design, energy efficiency, etc. However, it still lacks adequate searching efficiency tendency to stuck in local optima which affects final outcome. This paper introduces modified (mBES) that removes shortcomings original incorporating three improvements; Opposition-based learning (OBL), Chaotic Local Search (CLS), Transition & Pharsor operators. OBL embedded different phases standard viz. initial population, selecting, space, swooping update positions individual solutions strengthen exploration, CLS used enhance position best agent will lead enhancing all individuals, operators help provide sufficient exploration–exploitation trade-off. mBES initially evaluated with 29 CEC2017 10 CEC2020 optimization benchmark functions. addition, practicality tested real-world feature selection problem five design Results are compared against number classical algorithms using statistical metrics, convergence analysis, box plots, Wilcoxon rank sum test. case composite test functions F21-F30, wins 70% cases, whereas rest functions, generates good results 65% cases. proposed produces performance 55% 45% generated competitive results. On other hand, problems, among algorithms. problem, also showed competitiveness observations problems show superiority robustness baseline metaheuristics. It can be safely concluded improvements suggested proved effective making enough solve variety

Язык: Английский

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Particle guided metaheuristic algorithm for global optimization and feature selection problems

Expert Systems with Applications, Год журнала: 2024, Номер 248, С. 123362 - 123362

Опубликована: Фев. 1, 2024

Язык: Английский

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A Hybrid Marine Predator Sine Cosine Algorithm for Parameter Selection of Hybrid Active Power Filter

Mathematics, Год журнала: 2023, Номер 11(3), С. 598 - 598

Опубликована: Янв. 23, 2023

Power quality issues are handled very well by filter technologies. In recent years, the advancement of hybrid active power filters (HAPF) has been enhanced due to ease control and flexibility as compared other These a beneficial asset for producer that requires smooth filtered output power. However, design these is daunting task perform. Often, metaheuristic algorithms employed dealing with this nonlinear optimization problem. work, new algorithm (Marine Predator Algorithm Sine Cosine Algorithm) proposed selecting best parameters HAPF. The comparison different obtaining HAPF also performed show case efficacy algorithm. It can be concluded produces robust results potential tool estimating parameters. confirmation performance conducted fitness statistical results, boxplots, numerical analyses.

Язык: Английский

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Advances in teaching–learning-based optimization algorithm: A comprehensive survey(ICIC2022)

Neurocomputing, Год журнала: 2023, Номер 561, С. 126898 - 126898

Опубликована: Окт. 5, 2023

Язык: Английский

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Dynamic opposition learning-based rank-driven teaching learning optimizer for parameter extraction of photovoltaic models

Alexandria Engineering Journal, Год журнала: 2025, Номер 117, С. 325 - 339

Опубликована: Янв. 16, 2025

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

Alexandria Engineering Journal, Год журнала: 2023, Номер 73, С. 543 - 577

Опубликована: Май 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.

Язык: Английский

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A hybrid whale optimization algorithm based on equilibrium concept

Alexandria Engineering Journal, Год журнала: 2022, Номер 68, С. 763 - 786

Опубликована: Дек. 22, 2022

This research paper proposes a hybrid Whale Optimization Algorithm (WOA) variant based on Equilibrium Optimizer (EO), named (EWOA). The major finding lies in an efficient hybridization of bio-inspired and physics-based (EO) metaheuristic algorithms. Upon mathematical modelling, EWOA main architecture that combines WOA's encircling net-bubble attacking mechanisms via EO's weight balance strategy. proposed algorithm was tested 23 classical, 28 constrained CEC 2017, 30 unconstrained 10 2019, 2020 benchmark problems, comparison with six recently state-of-the-art algorithms (including WOA EO). outperforms other the best statistical mean performance 46 out 101 functions most promising clustering data graph, respectively. fact could achieve SD 2 total 5 sets proves is competitively robust. can converge to optimum before 50% iterations functions, achieving fastest convergence rate compared contribution thereby successful development this algorithm, which yields better optimization efficiency than original terms statistics, clustering.

Язык: Английский

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A teaching-learning-based optimization algorithm with reinforcement learning to address wind farm layout optimization problem

Applied Soft Computing, Год журнала: 2023, Номер 151, С. 111135 - 111135

Опубликована: Дек. 7, 2023

Язык: Английский

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