B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

Computers, Journal Year: 2021, Volume and Issue: 10(11), P. 136 - 136

Published: Oct. 25, 2021

Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there redundant irrelevant features, which reduce the performance of algorithms. To tackle this challenge, metaheuristic algorithms used to select effective However, most them scalable enough from as well small ones. Therefore, paper, a binary moth-flame optimization (B-MFO) is proposed datasets. Three categories B-MFO were developed using S-shaped, V-shaped, U-shaped transfer functions convert canonical MFO continuous binary. These evaluated on seven results compared with four well-known algorithms: BPSO, bGWO, BDA, BSSA. In addition, convergence behavior comparative assessed, statistically analyzed Friedman test. The experimental demonstrate superior solving feature selection problem for different other

Language: Английский

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

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(4), P. 2683 - 2723

Published: Jan. 12, 2023

Language: Английский

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An Improved Harris Hawks Optimization Algorithm with Multi-strategy for Community Detection in Social Network

Journal of Bionic Engineering, Journal Year: 2022, Volume and Issue: 20(3), P. 1175 - 1197

Published: Dec. 19, 2022

Language: Английский

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DMDE: Diversity-maintained multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimization

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 198, P. 116895 - 116895

Published: March 17, 2022

Language: Английский

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An Improved Tunicate Swarm Algorithm with Best-random Mutation Strategy for Global Optimization Problems

Journal of Bionic Engineering, Journal Year: 2022, Volume and Issue: 19(4), P. 1177 - 1202

Published: March 28, 2022

Language: Английский

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A Feature Selection Based on the Farmland Fertility Algorithm for Improved Intrusion Detection Systems

Journal of Network and Systems Management, Journal Year: 2022, Volume and Issue: 30(3)

Published: March 19, 2022

Language: Английский

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Advances in Tree Seed Algorithm: A Comprehensive Survey

Archives of Computational Methods in Engineering, Journal Year: 2022, Volume and Issue: 29(5), P. 3281 - 3304

Published: Jan. 10, 2022

Language: Английский

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Secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(5)

Published: April 23, 2024

Abstract This study introduces a novel population-based metaheuristic algorithm called secretary bird optimization (SBOA), inspired by the survival behavior of birds in their natural environment. Survival for involves continuous hunting prey and evading pursuit from predators. information is crucial proposing new that utilizes abilities to address real-world problems. The algorithm's exploration phase simulates snakes, while exploitation models escape During this phase, observe environment choose most suitable way reach secure refuge. These two phases are iteratively repeated, subject termination criteria, find optimal solution problem. To validate performance SBOA, experiments were conducted assess convergence speed, behavior, other relevant aspects. Furthermore, we compared SBOA with 15 advanced algorithms using CEC-2017 CEC-2022 benchmark suites. All test results consistently demonstrated outstanding terms quality, stability. Lastly, was employed tackle 12 constrained engineering design problems perform three-dimensional path planning Unmanned Aerial Vehicles. demonstrate that, contrasted optimizers, proposed can better solutions at faster pace, showcasing its significant potential addressing

Language: Английский

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MFO-SFR: An Enhanced Moth-Flame Optimization Algorithm Using an Effective Stagnation Finding and Replacing Strategy

Mathematics, Journal Year: 2023, Volume and Issue: 11(4), P. 862 - 862

Published: Feb. 8, 2023

Moth-flame optimization (MFO) is a prominent problem solver with simple structure that widely used to solve different problems. However, MFO and its variants inherently suffer from poor population diversity, leading premature convergence local optima losses in the quality of solutions. To overcome these limitations, an enhanced moth-flame algorithm named MFO-SFR was developed global The introduces effective stagnation finding replacing (SFR) strategy effectively maintain diversity throughout process. SFR can find stagnant solutions using distance-based technique replaces them selected solution archive constructed previous effectiveness proposed extensively assessed 30 50 dimensions CEC 2018 benchmark functions, which simulated unimodal, multimodal, hybrid, composition Then, obtained results were compared two sets competitors. In first comparative set, well-known variants, specifically LMFO, WCMFO, CMFO, ODSFMFO, SMFO, WMFO, considered. Five state-of-the-art metaheuristic algorithms, including PSO, KH, GWO, CSA, HOA, considered second set. then statistically analyzed through Friedman test. Ultimately, capacity mechanical engineering problems evaluated latest 2020 test-suite. experimental statistical analysis confirmed superior algorithms for solving complex problems, 91.38% effectiveness.

Language: Английский

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A Critical Review of Moth-Flame Optimization Algorithm and Its Variants: Structural Reviewing, Performance Evaluation, and Statistical Analysis

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(4), P. 2177 - 2225

Published: Feb. 2, 2024

Language: Английский

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