An Improved Fick’s Law Algorithm Based on Dynamic Lens-Imaging Learning Strategy for Planning a Hybrid Wind/Battery Energy System in Distribution Network

Mathematics, Journal Year: 2023, Volume and Issue: 11(5), P. 1270 - 1270

Published: March 6, 2023

In this paper, optimal and multi-objective planning of a hybrid energy system (HES) with wind turbine battery storage (WT/Battery) has been proposed to drop power loss, smooth voltage profile, enhance customers reliability, as well minimize the net present cost plus degradation (BDC). Decision variables include installation site size farm storage. These are found help novel metaheuristic approach called improved Fick’s law algorithm (IFLA). To exploration performance avoid early incomplete convergence conventional (FLA) algorithm, dynamic lens-imaging learning strategy (DLILS) based on opposition adopted. The problem implemented in two approaches without considering BDC analyze its impact reserve level amount quality reliability. A 33-bus distribution also employed validate capability efficiency suggested method. Simulation results have shown that WT/Battery improves reliability decreases loss by managing charging discharging units creating electrical injection into network. simulations evaluation statistic analysis indicate superiority IFLA achieving solution faster than FLA, particle swarm optimization (PSO), manta ray foraging optimizer (MRFO), bat (BA). It observed methodology different obtained lower more desirable profile counterparts. reports demonstrate BDC, values losses deviations increased 2.82% 1.34%, respectively, network is weakened 5.59% comparison case which neglected. Therefore, taking account parameter objective function can lead correct real calculation improvement rate each objectives, especially level, making decisions planners these findings.

Language: Английский

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A Velocity-Guided Grey Wolf Optimization Algorithm With Adaptive Weights and Laplace Operators for Feature Selection in Data Classification

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 39887 - 39901

Published: Jan. 1, 2024

The rapid growth of data quantity directly leads to the increasing feature dimension, which challenges machine learning and mining. Wrapper-based intelligent swarm algorithms are effective solution techniques. Grey Wolf Optimization (GWO) algorithm is a novel population algorithm. Simple principles few parameters characterize it. However, basic GWO has disadvantages, such as difficulty coordinating exploration exploitation capabilities premature convergence. As result, fails identify many irrelevant redundant features. To improve performance algorithm, this paper proposes velocity-guided grey wolf optimization with adaptive weights Laplace operators (VGWO-AWLO). Firstly, by introducing uniformly distributed dynamic weighting mechanism, control $a$ guided undergo nonlinear changes achieve good transition from exploratory phase development phase. Second, velocity-based position update formula designed an individual memory function enhance local search capability wolves drive them converge optimal solution. Thirdly, cross-operator strategy applied increase diversity help escape Finally, VGWO-AWLO evaluated for its comprehensive in terms classification accuracy, dimensionality approximation, convergence, stability 18 classified datasets. experimental results show that accuracy convergence speed better than GWO, variants, other state-of-the-art meta-heuristic algorithms.

Language: Английский

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Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

Computer Modeling in Engineering & Sciences, Journal Year: 2024, Volume and Issue: 140(2), P. 1563 - 1593

Published: Jan. 1, 2024

In classification problems, datasets often contain a large amount of features, but not all them are relevant for accurate classification.In fact, irrelevant features may even hinder accuracy.Feature selection aims to alleviate this issue by minimizing the number in subset while simultaneously error rate.Single-objective optimization approaches employ an evaluation function designed as aggregate with parameter, results obtained depend on value parameter.To eliminate parameter's influence, problem can be reformulated multi-objective problem.The Whale Optimization Algorithm (WOA) is widely used problems because its simplicity and easy implementation.In paper, we propose multi-strategy assisted WOA (MSMOWOA) address feature selection.To enhance algorithm's search ability, integrate multiple strategies such Levy flight, Grey Wolf Optimizer, adaptive mutation into it.Additionally, utilize external repository store non-dominant solution sets grid technology maintain diversity.Results fourteen University California Irvine (UCI) demonstrate that our proposed method effectively removes redundant improves performance.The source code accessed from website: https://github.com/zc0315/MSMOWOA.

Language: Английский

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A Novel Hybrid Algorithm Based on Beluga Whale Optimization and Harris Hawks Optimization for Optimizing Multi-Reservoir Operation

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(12), P. 4883 - 4909

Published: June 19, 2024

Language: Английский

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Targeting wastewater quality variables prediction: Improving sparrow search algorithm towards optimizing echo state network

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 65, P. 105717 - 105717

Published: July 13, 2024

Language: Английский

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