A binary reptile search algorithm based on transfer functions with a new stochastic repair method for 0–1 knapsack problems

Computers & Industrial Engineering, Journal Year: 2023, Volume and Issue: 178, P. 109080 - 109080

Published: Feb. 20, 2023

Language: Английский

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IBJA: An improved binary DJaya algorithm for feature selection

Journal of Computational Science, Journal Year: 2023, Volume and Issue: 75, P. 102201 - 102201

Published: Dec. 14, 2023

Language: Английский

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A new binary coati optimization algorithm for binary optimization problems

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 36(6), P. 2797 - 2834

Published: Nov. 24, 2023

Language: Английский

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A comprehensive approach with DTW-driven IMF selection, multi-domain fusion, and TSA-based feature selection for compound fault diagnosis

Measurement, Journal Year: 2024, Volume and Issue: 242, P. 115974 - 115974

Published: Oct. 12, 2024

Language: Английский

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BINARY HONEY BADGER ALGORITHM ENHANCED WITH TIME-VARYING SIGMOID TRANSFER FUNCTION AND CROSSOVER STRATEGY

Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi :/Osmangazi Üniversitesi Mühendislik-Mimarlık Fakültesi dergisi, Journal Year: 2025, Volume and Issue: 33(1), P. 1695 - 1711

Published: April 16, 2025

Modeling the foraging behavior of honey badgers, Honey Badger Algorithm (HBA) is a recently proposed metaheuristic algorithm. In this study, binary version algorithm that was for solving continuous optimization problems developed. The S-shaped transfer function and crossover strategy were used to transform into Eight functions with constant time-varying features used, most successful determined. Additionally, effect examined. Three strategies, single-point, two-point, uniform, applied as uniform strategy, which more than others, integrated (BinHBA) developed in way tested on total twenty-seven knapsack problems, fifteen small-scale twelve large-scale. Statistical tests employed analyze results compare them methods found existing literature. showed BinHBA effective preferable.

Language: Английский

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A diversity enhanced tree-seed algorithm based on double search with genetic and automated learning search strategies for image segmentation

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113143 - 113143

Published: April 1, 2025

Language: Английский

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A novel feature selection using binary hybrid improved whale optimization algorithm

The Journal of Supercomputing, Journal Year: 2023, Volume and Issue: 79(9), P. 10020 - 10045

Published: Jan. 28, 2023

Language: Английский

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MSBWO: A Multi-Strategies Improved Beluga Whale Optimization Algorithm for Feature Selection

Biomimetics, Journal Year: 2024, Volume and Issue: 9(9), P. 572 - 572

Published: Sept. 22, 2024

Feature selection (FS) is a classic and challenging optimization task in most machine learning data mining projects. Recently, researchers have attempted to develop more effective methods by using metaheuristic FS. To increase population diversity further improve the effectiveness of beluga whale (BWO) algorithm, this paper, we propose multi-strategies improved BWO (MSBWO), which incorporates circle mapping dynamic opposition-based (ICMDOBL) initialization as well elite pool (EP), step-adaptive Lévy flight spiral updating position (SLFSUP), golden sine algorithm (Gold-SA) strategies. Among them, ICMDOBL contributes increasing during search process reducing risk falling into local optima. The EP technique also enhances algorithm's ability escape from SLFSUP, distinguished original BWO, aims rigor accuracy development spaces. Gold-SA introduced quality solutions. hybrid performance MSBWO was evaluated comprehensively on IEEE CEC2005 test functions, including qualitative analysis comparisons with other conventional state-of-the-art (SOTA) approaches that were 2024. results demonstrate superior algorithms terms maintains better balance between exploration exploitation. Moreover, according proposed continuous MSBWO, binary variant (BMSBWO) optimizers obtained function ten UCI datasets random forest (RF) classifier. Consequently, BMSBWO has proven very competitive classification precision feature reduction.

Language: Английский

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Time-band network model and binary tree algorithm for multimodal irregular flight recovery

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 4, 2024

Abstract Recovery of irregular flights caused by various reasons such as aircraft failures and airport closures is studied in this research a multimodal time-band network model for solving the issue proposed. It transforms flight routing problem into time-based network, which used to obtain delay cancellation costs each flight. With variables, proposed aims minimize recovery under constraints. This also suggests developed binary tree algorithm, improves efficiency solving. The results show that rescheduled re-selected routes are at lowest cost helpful achieve balance flow without affecting safety. method work shows its certain value helping airlines restore operations shortest possible time cost, improving operational service quality.

Language: Английский

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An Efficient Binary Hybrid Equilibrium Algorithm for Binary Optimization Problems: Analysis, Validation, and Case Studies

International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: April 22, 2024

Abstract Binary optimization problems belong to the NP-hard class because their solutions are hard find in a known time. The traditional techniques could not be applied tackle those computational cost required by them increases exponentially with increasing dimensions of problems. Therefore, over last few years, researchers have paid attention metaheuristic algorithms for tackling an acceptable But unfortunately, still suffer from being able avert local minima, lack population diversity, and low convergence speed. As result, this paper presents new binary technique based on integrating equilibrium optimizer (EO) search operator, which effectively integrates single crossover, uniform mutation flipping swapping operator improve its exploration exploitation operators. In more general sense, is two folds: first fold borrows single-point crossover accelerate speed, addition avoiding falling into minima using strategy; second applying different operators best-so-far solution hope finding better solution: flip bit selected randomly given solution, swap unique positions solution. This variant called hybrid (BHEO) three common problems: 0–1 knapsack, feature selection, Merkle–Hellman knapsack cryptosystem (MHKC) investigate effectiveness. experimental findings BHEO compared classical algorithm six other well-established evolutionary swarm-based algorithms. From findings, it concluded that strong alternative Quantatively, reach average fitness 0.090737884 section problem difference optimal profits some used Knapsack 2.482.

Language: Английский

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