A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(7)

Published: June 11, 2024

Abstract The application of optimization theory and the algorithms that are generated from it has increased along with science technology's continued advancement. Numerous issues in daily life can be categorized as combinatorial issues. Swarm intelligence have been successful machine learning, process control, engineering prediction throughout years shown to efficient handling An intelligent system called chicken swarm algorithm (CSO) mimics organic behavior flocks chickens. In benchmark problem's objective function, outperforms several popular methods like PSO. concept advancement flock algorithm, comparison other meta-heuristic algorithms, development trend reviewed order further enhance search performance quicken research algorithm. fundamental model is first described, enhanced based on parameters, chaos quantum optimization, learning strategy, population diversity then summarized using both domestic international literature. use group areas feature extraction, image processing, robotic engineering, wireless sensor networks, power. Second, evaluated terms benefits, drawbacks, algorithms. Finally, direction anticipated.

Language: Английский

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Path Planning of Unmanned Aerial Vehicles Based on an Improved Bio-Inspired Tuna Swarm Optimization Algorithm

Biomimetics, Journal Year: 2024, Volume and Issue: 9(7), P. 388 - 388

Published: June 26, 2024

The Sine-Levy tuna swarm optimization (SLTSO) algorithm is a novel method based on the sine strategy and Levy flight guidance. It presented as solution to shortcomings of (TSO) algorithm, which include its tendency reach local optima limited capacity search worldwide. This updates locations using technique greedy approach generates initial solutions an elite reverse learning process. Additionally, it offers individual location called golden sine, enhances algorithm's explore widely steer clear optima. To plan UAV paths safely effectively in complex obstacle environments, SLTSO considers constraints such geographic airspace obstacles, along with performance metrics like environment, space, distance, angle, altitude, threat levels. effectiveness verified by simulation creation path planning model. Experimental results show that displays faster convergence rates, better precision, shorter smoother paths, concomitant reduction energy usage. A drone can now map route far more thanks these improvements. Consequently, proposed demonstrates both efficacy superiority applications.

Language: Английский

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Improved exponential distribution optimizer: enhancing global numerical optimization problem solving and optimizing machine learning paramseters

Cluster Computing, Journal Year: 2024, Volume and Issue: 28(2)

Published: Nov. 26, 2024

Language: Английский

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A comprehensive survey of the application of swarm intelligent optimization algorithm in photovoltaic energy storage systems

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

Published: Aug. 2, 2024

With the rapid development of renewable energy, photovoltaic energy storage systems (PV-ESS) play an important role in improving efficiency, ensuring grid stability and promoting transition. As part micro-grid system, system can realize stable operation through design optimization scheduling system. The structure characteristics are summarized. From perspective objectives constraints discussed, current main algorithms for compared evaluated. challenges future briefly described, research results methods This paper summarizes application swarm intelligence algorithm systems, including principles, goals, practical cases, directions, providing new ideas better promotion valuable reference.

Language: Английский

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Assessment of Water Hydrochemical Parameters Using Machine Learning Tools

Sustainability, Journal Year: 2025, Volume and Issue: 17(2), P. 497 - 497

Published: Jan. 10, 2025

Access to clean water is a fundamental human need, yet millions of people worldwide still lack access safe drinking water. Traditional quality assessments, though reliable, are typically time-consuming and resource-intensive. This study investigates the application machine learning (ML) techniques for analyzing river in Barnaul area, located on Ob River Altai Krai. The research particularly highlights use Water Quality Index (WQI) as key factor feature engineering. WQI, calculated using Horton model, integrates nine hydrochemical parameters: pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, turbidity. primary objective was demonstrate contribution WQI enhancing predictive performance analysis. A dataset 2465 records analyzed, with missing values parameters (pH, trihalomethanes) addressed imputation via neural network (NN) architectures optimized genetic algorithms (GAs). Models trained without achieved moderate accuracy, but incorporating dramatically improved across all tasks. For trihalomethanes R2 score increased from 0.68 (without WQI) 0.86 (with WQI). Similarly, 0.35 0.74, 0.27 0.69 after including set.

Language: Английский

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Two-phase optimization modelling with swarm computation and biomimetic intelligence learning for neural network training

Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 111058 - 111058

Published: March 1, 2025

Language: Английский

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Comprehensive screening, separation, extraction optimization, and bioactivity evaluation of xanthine oxidase inhibitors from Ganoderma leucocontextum

Arabian Journal of Chemistry, Journal Year: 2025, Volume and Issue: 0, P. 1 - 14

Published: April 11, 2025

Language: Английский

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Hybrid optimization driven fake news detection using reinforced transformer models

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

Published: April 28, 2025

Language: Английский

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ADVCSO: Adaptive Dynamically Enhanced Variant of Chicken Swarm Optimization for Combinatorial Optimization Problems

Biomimetics, Journal Year: 2025, Volume and Issue: 10(5), P. 303 - 303

Published: May 9, 2025

High-dimensional complex optimization problems are pervasive in engineering and scientific computing, yet conventional algorithms struggle to meet collaborative requirements due computational complexity. While Chicken Swarm Optimization (CSO) demonstrates an intuitive understanding straightforward implementation for low-dimensional problems, it suffers from limitations including a low convergence precision, uneven initial solution distribution, premature convergence. This study proposes Adaptive Dynamically Enhanced Variant of (ADVCSO) algorithm. First, address the distribution original algorithm, we design elite perturbation initialization strategy based on good point sets, combining low-discrepancy sequences with Gaussian perturbations significantly improve search space coverage. Second, targeting exploration–exploitation imbalance caused by fixed role proportions, dynamic allocation mechanism is developed, integrating cosine annealing strategies adaptively regulate flock proportions update cycles, thereby enhancing exploration efficiency. Finally, mitigate induced single rules, hybrid mutation introduced through phased operators dimension inheritance mechanisms, effectively reducing risks. Experiments demonstrate that ADVCSO outperforms state-of-the-art 27 29 CEC2017 benchmark functions, achieving 2–3 orders magnitude improvement precision over basic CSO. In composite scenarios, its accuracy approaches championship algorithm JADE within 10−2 difference. For multi-subproblem optimization, exhibits superior performance both Multiple Traveling Salesman Problems (MTSPs) Knapsack (MKPs), maximum path length MTSPs 6.0% 358.27 units while MKP optimal success rate 62.5%. The proposed exceptional combinatorial holds significant application value.

Language: Английский

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A Comprehensive Analysis of Load Balancing in Cloud Computing: Examining Methodologies and Research Practices for an Effective Hybrid Approach

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: May 16, 2025

Over the last several years, cloud computing (CC) has become a unique paradigm. Cloud aims to deliver and resources over internet through dynamic provision of services. Using comes with variety challenges obstacles. This study examines load balancing (LB), one primary issues computing. The goal is evenly distribute power servers, preventing any host from experiencing overwork or underload. Numerous load-balancing algorithms have been implemented in literature provide efficient management, fulfill customer requirements for appropriate nodes, enhance overall effectiveness services, improve end-user satisfaction. An effective algorithm distributes workload among system nodes maximize efficiency asset utilization. research paper critically analyze latest approaches. It will cover various attributes such as resource utilization, scalability, fault tolerance (FT), savings, throughput performance, migration time, reaction time. report also discusses environments emphasizes necessity technique that utilizes machine learning criteria balancing. found traditional perform poorly do not consider reliability. Hence, identifies need reliability algorithms, which main concerns environments. A new hybrid method proposed,

Language: Английский

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