Large-scale evolutionary optimization: A review and comparative study

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 85, С. 101466 - 101466

Опубликована: Янв. 10, 2024

Large-scale global optimization (LSGO) problems have widely appeared in various real-world applications. However, their inherent complexity, coupled with the curse of dimensionality, makes them challenging to solve. Continuous efforts been devoted designing computational intelligence-based approaches solve them. This paper offers a comprehensive review latest developments field, focusing on advances both single-objective and multi-objective large-scale evolutionary algorithms over past five years. We systematically categorize these algorithms, discuss distinct features, highlight benchmark test suites essential for performance evaluation. After that, comparative studies are conducted using numerical solutions evaluate state-of-the-art LSGO problems. Finally, we applications LSGO, some challenges, possible future research directions.

Язык: Английский

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Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Electronics, Год журнала: 2022, Номер 11(12), С. 1919 - 1919

Опубликована: Июнь 20, 2022

The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based performs optimization procedure using novel way exploration and exploitation multiphases search. In this review research, we focused on applications developments well-established robust (HHO) as one most popular techniques 2020. Moreover, several experiments were carried out to prove powerfulness effectivness HHO compared with nine other state-of-art algorithms Congress Evolutionary Computation (CEC2005) CEC2017. literature paper includes deep insight about possible future directions ideas worth investigations regarding new variants its widespread applications.

Язык: Английский

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An Improved Farmland Fertility Algorithm with Hyper-Heuristic Approach for Solving Travelling Salesman Problem

Computer Modeling in Engineering & Sciences, Год журнала: 2022, Номер 135(3), С. 1981 - 2006

Опубликована: Сен. 15, 2022

Travelling Salesman Problem (TSP) is a discrete hybrid optimization problem considered NP-hard. TSP aims to discover the shortest Hamilton route that visits each city precisely once and then returns starting point, making it feasible. This paper employed Farmland Fertility Algorithm (FFA) inspired by agricultural land fertility hyper-heuristic technique based on Modified Choice Function (MCF). The neighborhood search operator can use this strategy automatically select best heuristic method for decision. Lin-Kernighan (LK) local has been incorporated increase efficiency performance of suggested approach. 71 TSPLIB datasets have compared with different algorithms prove proposed algorithm's efficiency. Simulation results indicated algorithm outperforms comparable methods average mean computation time, percentage deviation (PDav), tour length.

Язык: Английский

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Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data

Mathematics, Год журнала: 2022, Номер 10(15), С. 2770 - 2770

Опубликована: Авг. 4, 2022

Many metaheuristic approaches have been developed to select effective features from different medical datasets in a feasible time. However, most of them cannot scale well large datasets, where they fail maximize the classification accuracy and simultaneously minimize number selected features. Therefore, this paper is devoted developing an efficient binary version quantum-based avian navigation optimizer algorithm (QANA) named BQANA, utilizing scalability QANA effectively optimal feature subset high-dimensional using two approaches. In first approach, several versions are S-shaped, V-shaped, U-shaped, Z-shaped, quadratic transfer functions map continuous solutions canonical ones. second mapped space by converting each variable 0 or 1 threshold. To evaluate proposed algorithm, first, all assessed on with varied sizes, including Pima, HeartEW, Lymphography, SPECT Heart, PenglungEW, Parkinson, Colon, SRBCT, Leukemia, Prostate tumor. The results show that BQANA approach superior other find datasets. Then, was compared nine well-known algorithms, were statistically Friedman test. experimental statistical demonstrate has merit for selection

Язык: Английский

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A multi-strategy enhanced northern goshawk optimization algorithm for global optimization and engineering design problems

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2023, Номер 415, С. 116199 - 116199

Опубликована: Июль 19, 2023

Язык: Английский

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