Competing leaders grey wolf optimizer and its application for training multi-layer perceptron classifier

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 239, P. 122349 - 122349

Published: Oct. 29, 2023

Language: Английский

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A novel hippo swarm optimization: for solving high-dimensional problems and engineering design problems

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(3), P. 12 - 42

Published: April 10, 2024

Abstract In recent years, scholars have developed and enhanced optimization algorithms to tackle high-dimensional engineering challenges. The primary challenge of lies in striking a balance between exploring wide search space focusing on specific regions. Meanwhile, design problems are intricate come with various constraints. This research introduces novel approach called Hippo Swarm Optimization (HSO), inspired by the behavior hippos, designed address real-world HSO encompasses four distinct strategies based hippos different scenarios: starvation search, alpha margination, competition. To assess effectiveness HSO, we conducted experiments using CEC2017 test set, featuring highest dimensional problems, CEC2022 constrained problems. parallel, employed 14 established as control group. experimental outcomes reveal that outperforms well-known algorithms, achieving first average ranking out them CEC2022. Across classical consistently delivers best results. These results substantiate highly effective algorithm for both

Language: Английский

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NOx emission predicting for coal-fired boilers based on ensemble learning methods and optimized base learners

Energy, Journal Year: 2022, Volume and Issue: 264, P. 126171 - 126171

Published: Nov. 23, 2022

Language: Английский

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Multi-strategy enhanced Grey Wolf Optimizer for global optimization and real world problems

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(8), P. 10671 - 10715

Published: May 9, 2024

Language: Английский

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Learning search algorithm: framework and comprehensive performance for solving optimization problems

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(6)

Published: May 9, 2024

Abstract In this study, the Learning Search Algorithm (LSA) is introduced as an innovative optimization algorithm that draws inspiration from swarm intelligence principles and mimics social learning behavior observed in humans. The LSA optimizes search process by integrating historical experience real-time information, enabling it to effectively navigate complex problem spaces. By doing so, enhances its global development capability provides efficient solutions challenging tasks. Additionally, improves collective capacity incorporating teaching active behaviors within population, leading improved local capabilities. Furthermore, a dynamic adaptive control factor utilized regulate algorithm’s exploration abilities. proposed rigorously evaluated using 40 benchmark test functions IEEE CEC 2014 2020, compared against nine established evolutionary algorithms well 11 recently algorithms. experimental results demonstrate superiority of algorithm, achieves top rank Friedman rank-sum test, highlighting power competitiveness. Moreover, successfully applied solve six real-world engineering problems 15 UCI datasets feature selection problems, showcasing significant advantages potential for practical applications problems.

Language: Английский

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A novel multi-strategy combined whale optimization algorithm for cascade reservoir operation of complex engineering optimization.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112917 - 112917

Published: Feb. 1, 2025

Language: Английский

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LSEWOA: An Enhanced Whale Optimization Algorithm with Multi-Strategy for Numerical and Engineering Design Optimization Problems

Sensors, Journal Year: 2025, Volume and Issue: 25(7), P. 2054 - 2054

Published: March 25, 2025

The Whale Optimization Algorithm (WOA) is a bio-inspired metaheuristic algorithm known for its simple structure and ease of implementation. However, WOA suffers from issues such as premature convergence, low population diversity in the later stages iteration, slow convergence rate, accuracy, an imbalance between exploration exploitation. In this paper, we proposed enhanced whale optimization with multi-strategy (LSEWOA). LSEWOA employs Good Nodes Set Initialization to generate uniformly distributed individuals, newly designed Leader-Followers Search-for-Prey Strategy, Spiral-based Encircling Prey strategy inspired by concept Spiral flight, Enhanced Updating Strategy. Additionally, redesigned update mechanism factor better balance effectiveness was evaluated using CEC2005, impact each improvement analyzed. We also performed quantitative analysis compare it other state-of-the-art algorithms 30/50/100 dimensions. Finally, applied nine engineering design problems verify capability solving real-world challenges. Experimental results demonstrate that outperformed than successfully addressed shortcomings classic WOA.

Language: Английский

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Renewable energy hosting capacity assessment in distribution networks based on multi‐strategy improved whale optimization algorithm

IET Renewable Power Generation, Journal Year: 2024, Volume and Issue: unknown

Published: April 4, 2024

Abstract The increasing penetration of distributed renewable energy poses significant challenges to the safety and stability distribution system, rendering hosting capacity networks a major concern. To effectively evaluate in networks, this paper introduces comprehensive assessment model considering various constraints. Given numerous non‐linear constraints inherent for multi‐strategy improved Whale Optimization Algorithm is proposed. Improvements are made population initialization position update strategies, mutation strategy introduced prevent premature convergence, thereby enhancing algorithm's performance. Finally, effectiveness proposed method validated through utilization three systems. Moreover, key factors that restrict improvement revealed from perspective network topology.

Language: Английский

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A Reinforced Whale Optimization Algorithm for Solving Mathematical Optimization Problems

Biomimetics, Journal Year: 2024, Volume and Issue: 9(9), P. 576 - 576

Published: Sept. 22, 2024

The whale optimization algorithm has several advantages, such as simple operation, few control parameters, and a strong ability to jump out of the local optimum, been used solve various practical problems. In order improve its convergence speed solution quality, reinforced (RWOA) was designed. Firstly, an opposition-based learning strategy is generate other optima based on best optimal found during algorithm’s iteration, which can increase diversity accelerate speed. Secondly, dynamic adaptive coefficient introduced in two stages prey bubble net, balance exploration exploitation. Finally, kind individual information-reinforced mechanism utilized encircling stage quality. performance RWOA validated using 23 benchmark test functions, 29 CEC-2017 12 CEC-2022 functions. Experiment results demonstrate that exhibits better accuracy stability than WOA 20 21 8 separately. Wilcoxon’s rank sum shows there are significant statistical differences between algorithms

Language: Английский

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CWOA: A novel complex-valued encoding whale optimization algorithm

Mathematics and Computers in Simulation, Journal Year: 2022, Volume and Issue: 207, P. 151 - 188

Published: Dec. 30, 2022

Language: Английский

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