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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Metaheuristic optimization algorithms: a comprehensive overview and classification of benchmark test functions

Soft Computing, Год журнала: 2023, Номер 28(4), С. 3123 - 3186

Опубликована: Окт. 11, 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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A novel improved chef-based optimization algorithm with Gaussian random walk-based diffusion process for global optimization and engineering problems

Mathematics and Computers in Simulation, Год журнала: 2023, Номер 212, С. 195 - 223

Опубликована: Май 6, 2023

Язык: Английский

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Self-adaptive hybrid mutation slime mould algorithm: Case studies on UAV path planning, engineering problems, photovoltaic models and infinite impulse response

Alexandria Engineering Journal, Год журнала: 2024, Номер 98, С. 364 - 389

Опубликована: Май 11, 2024

There are many classic highly complex optimization problems in the world, therefore, it is still necessary to find an applicable and effective algorithm solve these problems. In this paper, self-adaptive hybrid cross mutation slime mold proposed, which AHCSMA, efficiently. Specifically, there three innovations paper: (i) new Cauchy operator developed improve ability of population; (ii) crossover rate balance mechanism proposed make up for neglected relationship between individuals rates. Then differential vector information dominant individual other population utilized increase evolution speed algorithm; (iii) restart opposition learning designed alleviate situation where falls into local optimality. To verify competitive UAV path planning problems, engineering nonlinear parameter extraction photovoltaic model identification infinite impulse response used test accumulation more than 50 algorithms as comparison algorithms, results report that AHCSMA extremely performs better when optimizing real-life

Язык: Английский

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A modified slime mold algorithm for parameter identification of hydrogen-powered proton exchange membrane fuel cells

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 86, С. 853 - 874

Опубликована: Сен. 3, 2024

Язык: Английский

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Novel memetic of beluga whale optimization with self-adaptive exploration–exploitation balance for global optimization and engineering problems

Soft Computing, Год журнала: 2023, Номер 27(19), С. 13951 - 13989

Опубликована: Июнь 6, 2023

Abstract A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous deficiencies that has to be strengthened. Premature convergence a disparity between exploitation exploration are some these challenges. Furthermore, the absence transfer parameter in typical when moving from phase direct impact on algorithm’s performance. This work proposes novel modified (mBWO) incorporates an elite evolution strategy, randomization control factor, transition factor exploitation. The strategy preserves top candidates for subsequent generation so helps generate effective solutions with meaningful differences them prevent settling into local maxima. random mutation improves search offers more crucial ability prevents stagnation optimum. mBWO controlling algorithm away optima region during BWO. Gaussian (GM) acts initial position vector produce new location. Because this, majority altered operators scattered close original position, which is comparable carrying out small region. method can now depart optimal zone because this modification, also increases optimizer’s precision traverses space using placements, lead zone. Transition (TF) used make transitions agents gradually concerning amount time required. undergoes comparison 10 additional optimizers 29 CEC2017 functions. Eight engineering problems addressed by mBWO, involving design welded beams, three-bar trusses, tension/compression springs, speed reducers, best industrial refrigeration systems, pressure vessel challenges, cantilever beam designs, multi-product batch plants. In both constrained unconstrained settings, results preformed superior those other methods.

Язык: Английский

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An Improved Gradient-Based Optimization Algorithm for Solving Complex Optimization Problems

Processes, Год журнала: 2023, Номер 11(2), С. 498 - 498

Опубликована: Фев. 7, 2023

In this paper, an improved gradient-based optimizer (IGBO) is proposed with the target of improving performance and accuracy algorithm for solving complex optimization engineering problems. The IGBO has added features adjusting best solution by adding inertia weight, fast convergence rate modified parameters, as well avoiding local optima using a novel functional operator (G). These make it feasible majority nonlinear problems which quite hard to achieve original version GBO. effectiveness scalability are evaluated well-known benchmark functions. Moreover, statistically analyzed ANOVA analysis, Holm–Bonferroni test. addition, was assessed real-world results functions show that very competitive, superior compared its competitors in finding optimal solutions high coverage. studied real prove superiority difficult indefinite search domains.

Язык: Английский

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Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models

Scientific Reports, Год журнала: 2022, Номер 12(1)

Опубликована: Окт. 26, 2022

Feature classification in digital medical images like mammography presents an optimization problem which researchers often neglect. The use of a convolutional neural network (CNN) feature extraction and has been widely reported the literature to have achieved outstanding performance acceptance disease detection procedure. However, little emphasis is placed on ensuring that only discriminant features extracted by operations are passed classifier, avoid bottlenecking operation. Unfortunately, since this left unaddressed, subtle impairment resulted from omission. Therefore, study devoted addressing these drawbacks using metaheuristic algorithm optimize number CNN, so suggestive applied for process. To achieve this, new variant Ebola-based proposed, based population immunity concept chaos mapping initialization strategy. resulting algorithm, called immunity-based Ebola search (IEOSA), addressed study. optimized represent output IEOSA, receives noisy unfiltered detected process as input. An exhaustive evaluation IEOSA was carried out classical IEEE CEC benchmarked functions. A comparative analysis presented, with some recent algorithms. experimental result showed performed well all tested benchmark Furthermore, then solve enhancement selection CNN better prediction breast cancer mammography. accuracy returned method approach improved when models.

Язык: Английский

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An enhanced slime mould algorithm based on adaptive grouping technique for global optimization

Expert Systems with Applications, Год журнала: 2023, Номер 222, С. 119877 - 119877

Опубликована: Март 14, 2023

Язык: Английский

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