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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Heuristic Optimization Algorithm of Black-Winged Kite Fused with Osprey and Its Engineering Application

Biomimetics, Journal Year: 2024, Volume and Issue: 9(10), P. 595 - 595

Published: Oct. 1, 2024

Swarm intelligence optimization methods have steadily gained popularity as a solution to multi-objective issues in recent years. Their study has garnered lot of attention since problems hard high-dimensional goal space. The black-winged kite algorithm still suffers from the imbalance between global search and local development capabilities, it is prone even though combines Cauchy mutation enhance algorithm's ability. heuristic fused with osprey (OCBKA), which initializes population by logistic chaotic mapping fuses improve performance algorithm, proposed means enhancing ability (BKA). By using numerical comparisons CEC2005 CEC2021 benchmark functions, along other swarm solutions three engineering problems, upgraded strategy's efficacy confirmed. Based on experiment findings, revised OCBKA very competitive because can handle complicated high convergence accuracy quick time when compared comparable algorithms.

Language: Английский

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FOX Optimization Algorithm Based on Adaptive Spiral Flight and Multi-Strategy Fusion

Biomimetics, Journal Year: 2024, Volume and Issue: 9(9), P. 524 - 524

Published: Aug. 30, 2024

Adaptive spiral flight and multi-strategy fusion are the foundations of a new FOX optimization algorithm that aims to address drawbacks original method, including weak starting individual ergodicity, low diversity, an easy way slip into local optimum. In order enhance population, inertial weight is added along with Levy variable strategy once population initialized using tent chaotic map. To begin process implementing fox position created Tent map in provide more ergodic varied beginning locations. improve quality solution, second place. The random walk mode then updated updating approach. Subsequently, algorithm’s global searches balanced, flying method greedy approach incorporated update location. enhanced technique thoroughly contrasted various swarm intelligence algorithms engineering application issues CEC2017 benchmark test functions. According simulation findings, there have been notable advancements convergence speed, accuracy, stability, as well jumping out optimum, upgraded algorithm.

Language: Английский

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Threshold-sensitive energy efficient routing for precision agriculture

Peer-to-Peer Networking and Applications, Journal Year: 2025, Volume and Issue: 18(3)

Published: April 22, 2025

Language: Английский

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Comprehensive Adaptive Enterprise Optimization Algorithm and Its Engineering Applications

Biomimetics, Journal Year: 2025, Volume and Issue: 10(5), P. 302 - 302

Published: May 9, 2025

In this study, a brand-new algorithm called the Comprehensive Adaptive Enterprise Development Optimizer (CAED) is proposed to overcome drawbacks of (ED) in complex optimization tasks. particular, it aims tackle problems slow convergence and low precision. To enhance algorithm’s ability break free from local optima, lens imaging reverse learning approach incorporated. This creates solutions by utilizing concepts optical imaging. As result, expands search range boosts probability finding superior beyond optima. Moreover, an environmental sensitivity-driven adaptive inertial weight developed. dynamically modifies equilibrium between global exploration, which enables for new promising areas solution space, development, centered on refining close currently best-found areas. evaluate efficacy CAED, 23 benchmark functions CEC2005 are chosen testing. The performance CAED contrasted with that nine other algorithms, such as Particle Swarm Optimization (PSO), Gray Wolf (GWO), Antlion (AOA). Experimental findings show unimodal functions, standard deviation almost 0, reflects its high accuracy stability. case multimodal optimal value obtained notably better than those further emphasizing outstanding performance. also applied engineering challenges, like design cantilever beams three-bar trusses. For beam problem, achieved 13.3925, merely 0.0098. truss 259.805047, extremely small 1.11 × 10−7. These results much traditional ED comparative algorithms. Overall, through coordinated implementation multiple strategies, exhibits precision, strong robustness, rapid when searching spaces. such, offers efficient solving various problems.

Language: Английский

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Multi-objective Transit Algorithm Based on Density Sorting and Cylindrical Grid Mechanism for Layout Optimization of Wireless Sensor Networks

Journal of Network and Computer Applications, Journal Year: 2025, Volume and Issue: unknown, P. 104217 - 104217

Published: May 1, 2025

Language: Английский

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Hybrid Sand Cat Swarm Optimization Algorithm-based reliable coverage optimization strategy for heterogeneous wireless sensor networks

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 9, 2024

Language: Английский

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Hybrid Multi-Objective Chameleon Optimization Algorithm Based on Multi-Strategy Fusion and Its Applications

Biomimetics, Journal Year: 2024, Volume and Issue: 9(10), P. 583 - 583

Published: Sept. 25, 2024

Aiming at the problems of chameleon swarm algorithm (CSA), such as slow convergence speed, poor robustness, and ease falling into local optimum, a multi-strategy improved optimization (ICSA) is herein proposed. Firstly, logistic mapping was introduced to initialize population improve diversity initial population. Secondly, in prey-search stage, sub-population spiral search strategy global ability accuracy algorithm. Then, considering blindness chameleon's eye turning find prey, Lévy flight with cosine adaptive weight combined greed enhance guidance random exploration eyes' rotation stage. Finally, nonlinear varying update position prey-capture refraction reverse-learning used activity later stage so jump out optimum. Eighteen functions CEC2005 benchmark test set were selected an experimental set, performance ICSA tested compared five other intelligent algorithms. The analysis results 30 independent runs showed that has stronger ability. applied UAV path-planning problem. simulation algorithms, paths generated by different terrain scenarios are shorter more stable.

Language: Английский

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Plasma Breakdown Optimization Calculation Based on Improved Particle Swarm Algorithm for TT-1 Device

Journal of Fusion Energy, Journal Year: 2024, Volume and Issue: 43(2)

Published: June 26, 2024

Language: Английский

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Transit search algorithm based on oscillation exploitation factor and Roche limit for wireless sensor network deployment optimization

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 58(1)

Published: Nov. 27, 2024

To optimize the deployment of nodes in Wireless Sensor Networks (WSN) and effectively control network node energy consumption, thereby improving quality perception services, a Transit search algorithm based on oscillation exploitation factor Roche limit is proposed. The limit-inspired approach enhances stellar phase algorithm, accelerating convergence rate mid-to-late stages iteration while ensuring adequate exploration solution space. Subsequently, five weakening development factors are introduced to refine algorithm's improve its fine-tuning accuracy. validate effectiveness these strategies, various approaches applied coverage, waste consumption two models WSN deployment, with connectivity recorded. comparison reveals optimal improved SEROTS, which coverage by 1.34% obstacle-free model compared original TS rates reduced 2.05% 0.00016%, respectively. In obstacle model, increases 1.49%, decrease 6.96% 0.0004%, demonstrate efficiency optimizing SEROTS four optimization algorithms: Egret Swarm Optimization Algorithm (ESOA), Honey Badger (HBA), Sparrow Search (SSA) Differential Evolution (DE). Two selected, integrating three objectives into single objective function. Simulation results indicate that performs best both models, an improvement 0.53% 0.79% over second-best Furthermore, proposed strategies simulation from other studies, achieving higher 1.57%, 3.33%, 0.87%, 3.81% 0.21%, Finally, experiments discuss application large-scale scenarios, verifying feasibility optimization.

Language: Английский

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