A Novel Metaheuristic Approach: Spiral Cloud Optimization Algorithm DOI Creative Commons
Iman Shafieenejad,

Mohammadamin Nourian Pour

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 24, 2024

Abstract This study introduces a novel meta-heuristic algorithm named Spiral Cloud Optimization Algorithm (SCOA), inspired by the movement patterns of clouds. SCOA is mathematically modeled based on optimal motion clouds in nature to perform optimization across wide range search spaces. The core concept this derived from spiral behavior and Fibonacci sequence. distinguished its high-speed performance, simplicity implementation, impressive convergence. Moreover, golden ratio, mathematical principle, incorporated into algorithm. efficiency attributed streamlined processes, making it particularly suitable for tasks that require rapid execution reliable combination speed makes an appealing choice scenarios with limited computational resources or need quick results. proposed evaluated using 68 benchmark functions two engineering problems. results demonstrate provides superior performance terms precision convergence when solving complex problems, outperforming other algorithms such as Artificial Bee Colony (ABC), Gray Wolf Optimizer (GWO), Fire Hawks (FHO), Flying Fox (FFO), among others.

Language: Английский

Path Planning of Unmanned Aerial Vehicles Based on an Improved Bio-Inspired Tuna Swarm Optimization Algorithm DOI Creative Commons
Qinyong Wang, M. H. Xu,

Zhongyi Hu

et al.

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

Citations

8
Heuristic Optimization Algorithm of Black-Winged Kite Fused with Osprey and Its Engineering Application DOI Creative Commons
Zheng Zhang, Xiangkun Wang, Yinggao Yue

et al.

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

Citations

7
FOX Optimization Algorithm Based on Adaptive Spiral Flight and Multi-Strategy Fusion DOI Creative Commons
Zheng Zhang, Xiangkun Wang, Li Cao

et al.

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

Citations

4
Application and Optimization of Intelligent Image Processing Technology in Cross-Border E-commerce DOI

W.G. Liang,

Jiahui Liang

Learning and analytics in intelligent systems, Journal Year: 2025, Volume and Issue: unknown, P. 401 - 412

Published: Jan. 1, 2025

Language: Английский

Citations

0
Snake Optimization Algorithm Augmented by Adaptive t-Distribution Mixed Mutation and Its Application in Energy Storage System Capacity Optimization DOI Creative Commons
Yinggao Yue, Li Cao,

Changzu Chen

et al.

Biomimetics, Journal Year: 2025, Volume and Issue: 10(4), P. 244 - 244

Published: April 16, 2025

To address the drawbacks of traditional snake optimization method, such as a random population initialization, slow convergence speed, and low accuracy, an adaptive t-distribution mixed mutation strategy is proposed. Initially, Tent-based chaotic mapping quasi-reverse learning approach are utilized to enhance quality initial solution initialization process original method. During evolution stage, novel foraging introduced substitute stage This perturbs mutates at optimal position generate new solutions, thereby improving algorithm’s ability escape local optima. The mating mode in replaced with opposite-sex attraction mechanism, providing algorithm more opportunities for global exploration exploitation. improved method accelerates improves accuracy while balancing exploitation capabilities. experimental results demonstrate that outperforms other methods, including standard technique, terms robustness accuracy. Additionally, each improvement technique complements amplifies effects others.

Language: Английский

Citations

0
A comprehensive survey of the application of swarm intelligent optimization algorithm in photovoltaic energy storage systems DOI Creative Commons
Shuxin Wang, Yinggao Yue, Shaotang Cai

et al.

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

Citations

2
Hybrid Multi-Objective Chameleon Optimization Algorithm Based on Multi-Strategy Fusion and Its Applications DOI Creative Commons

Yaodan Chen,

Li Cao, Yinggao Yue

et al.

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

Citations

1
A Novel Metaheuristic Approach: Spiral Cloud Optimization Algorithm DOI Creative Commons
Iman Shafieenejad,

Mohammadamin Nourian Pour

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 24, 2024

Abstract This study introduces a novel meta-heuristic algorithm named Spiral Cloud Optimization Algorithm (SCOA), inspired by the movement patterns of clouds. SCOA is mathematically modeled based on optimal motion clouds in nature to perform optimization across wide range search spaces. The core concept this derived from spiral behavior and Fibonacci sequence. distinguished its high-speed performance, simplicity implementation, impressive convergence. Moreover, golden ratio, mathematical principle, incorporated into algorithm. efficiency attributed streamlined processes, making it particularly suitable for tasks that require rapid execution reliable combination speed makes an appealing choice scenarios with limited computational resources or need quick results. proposed evaluated using 68 benchmark functions two engineering problems. results demonstrate provides superior performance terms precision convergence when solving complex problems, outperforming other algorithms such as Artificial Bee Colony (ABC), Gray Wolf Optimizer (GWO), Fire Hawks (FHO), Flying Fox (FFO), among others.

Language: Английский

Citations

0