DTSA: Dynamic Tree-Seed Algorithm with Velocity-Driven Seed Generation and Count-Based Adaptive Strategies

Symmetry, Journal Year: 2024, Volume and Issue: 16(7), P. 795 - 795

Published: June 25, 2024

The Tree-Seed Algorithm (TSA) has been effective in addressing a multitude of optimization issues. However, it faced challenges with early convergence and difficulties managing high-dimensional, intricate problems. To tackle these shortcomings, this paper introduces TSA variant (DTSA). DTSA incorporates suite methodological enhancements that significantly bolster TSA’s capabilities. It the PSO-inspired seed generation mechanism, which draws inspiration from Particle Swarm Optimization (PSO) to integrate velocity vectors, thereby enhancing algorithm’s ability explore exploit solution spaces. Moreover, DTSA’s adaptive adaptation mechanism based on count parameters employs counter dynamically adjust effectively curbing risk premature strategically reversing vectors evade local optima. also integrates trees population integrated evolutionary strategy, leverages arithmetic crossover natural selection diversity, accelerate convergence, improve accuracy. Through experimental validation IEEE CEC 2014 benchmark functions, demonstrated its enhanced performance, outperforming recent variants like STSA, EST-TSA, fb-TSA, MTSA, as well established algorithms such GWO, PSO, BOA, GA, RSA. In addition, study analyzed best value, mean, standard deviation demonstrate efficiency stability handling complex issues, robustness are proven through successful application five complex, constrained engineering scenarios, demonstrating superiority over traditional by optimizing solutions overcoming inherent limitations.

Language: Английский

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An improved equilibrium optimization algorithm for feature selection problem in network intrusion detection

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

Published: Aug. 12, 2024

Abstract In this paper, an enhanced equilibrium optimization (EO) version named Levy-opposition-equilibrium (LOEO) is proposed to select effective features in network intrusion detection systems (IDSs). The opposition-based learning (OBL) approach applied by algorithm improve the diversity of population. Also, Levy flight method utilized escape local optima. Then, binary rendition called BLOEO employed feature selection IDSs. One main challenges IDSs high-dimensional space, with many irrelevant or redundant features. designed intelligently most informative subset empirical findings on NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets demonstrate effectiveness algorithm. This has acceptable ability effectively reduce number data features, maintaining a high accuracy over 95%. Specifically, UNSW-NB15 dataset, selected only 10.8 average, achieving 97.6% precision 100%.

Language: Английский

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Enhancing chernobyl disaster optimization: a novel hybridization approach with modified grey wolf optimizer for solving complex optimization problems

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 21, 2025

Language: Английский

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Smart cities optimization using computational intelligence in power-constrained IoT sensor networks

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 94, P. 101889 - 101889

Published: Feb. 27, 2025

Language: Английский

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Enhancing terminal user peak trends forecasting with Activity Factor (AF) and Grey Wolf Fusion with Flexible Parameters (GWFFP)

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127052 - 127052

Published: March 1, 2025

Language: Английский

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An intelligent fault diagnosis model for bearings with adaptive hyperparameter tuning in multi-condition and limited sample scenarios

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 24, 2025

Abstract Bearing fault diagnosis under multiple operating conditions is challenging due to the complexity of changing environments and limited availability training data. To address these issues, this paper presents an advanced method using a hybrid Grey Wolf Algorithm (HGWA)-optimized convolutional neural network (CNN) Bidirectional long short-term memory (BiLSTM) architecture. The proposed model leverages CNN for extracting spatial features BiLSTM capturing temporal dependencies. Through HGWA, hyperparameters are efficiently optimized, achieving 100% diagnostic accuracy across four with CWRU dataset. Additionally, optimized CNN–BiLSTM demonstrated high when applied as pre-trained in new environments, even minimal not only improves performance but also enhances optimization efficiency, faster results within same time frame. This approach mitigates challenges manually tuning effectively addresses bearing constrained sample conditions, representing meaningful contribution field rolling diagnostics.

Language: Английский

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Enhancing Unmanned Marine Vehicle Path Planning: A Fractal-Enhanced Chaotic Grey Wolf and Differential Evolution Approach

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113481 - 113481

Published: April 1, 2025

Language: Английский

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Quadrotor Robust Fractional‐Order Control Based on a Recent Bonobo Optimization Algorithm

Journal of Mathematics, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

In recent years, quadrotors have emerged as essential roles in robotics, and their diversity usefulness emphasize importance. This research work presents an in‐depth analysis of a quadcopter terms modeling, control, optimization, where central to the operation quadcopters all robotics systems is idea stability response. paper discusses possibility providing by demonstrating impact fractional controller sensitive systems. The five parameters for each engine are also improved using Bonobo Optimization (BO) algorithm. optimized results this compared with algorithms used, such Genetic Algorithm (GA), Particle Swarm (PSO), Grey Wolf (GWO). fractional‐order proportional integral derivative (FOPID) has greater control power its classic counterpart, PID it provided improvement minimizing overshoot 90%, showed great settling rising times GWO 25% BO 50% some superiority BO. By examining both advantages constraints inherent these methodologies, we seek advance field forward, promoting more breakthroughs crucial area.

Language: Английский

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Atom search optimization: a systematic review of current variants and applications

Knowledge and Information Systems, Journal Year: 2025, Volume and Issue: unknown

Published: April 12, 2025

Language: Английский

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Magnetic targets positioning method based on multi-strategy improved Grey Wolf optimizer

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 2, 2025

Magnetic target state estimation is a widely applied technology, but it also faces many challenges in practical applications. One of the most critical issue accuracy. The Grey Wolf Optimizer (GWO) one more successful swarm intelligence algorithms recent years, its shortcomings have been exposed when facing increasingly complex problems. Therefore, Multi-Strategy Improved (MSIGWO) algorithm has proposed to enhance accuracy magnetic estimation. In initialization phase, Tent chaos mapping introduced population diversity, prevent falling into local optima, and improve convergence speed. Multi-population fusion evolution strategies accuracy, global search ability. Nonlinear factors better balance exploration exploitation behaviors. Dynamic weight increase diversity samples reduce likelihood optima. Adaptive dimensional learning balances searches, enhancing diversity. Levy flight enhances ability jump out optima ensures CEC2018 benchmark function set 29 problems problems, MSIGWO was tested, statistical indicators Friedman test results show that compared with GWO advanced variants, superior performance. application this proven effectiveness applicability.

Language: Английский

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