Spiral Gaussian mutation sine cosine algorithm: Framework and comprehensive performance optimization

Zhou, Wei; Wang, Pengjun; Heidari, Ali Asghar; Zhao, Xuehua; Chen, Huiling

doi:10.1016/j.eswa.2022.118372

Cited by 30 publications

(5 citation statements)

References 130 publications

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“…where µ represents the mean or expected value of the distribution, and σ represents the standard deviation. Specifically, in the standard Gaussian probability density function, µ and σ are set to 0 and 1, respectively [51].…”

Section: Optimal Per-dimension Gaussian Mutation Strategymentioning

confidence: 99%

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Li,

Liu,

Shao

et al. 2023

Electronics

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The Dung Beetle Optimization (DBO) algorithm is a powerful metaheuristic algorithm that is widely used for optimization problems. However, the DBO algorithm has limitations in balancing global exploration and local exploitation capabilities, often leading to getting stuck in local optima. To overcome these limitations and address global optimization problems, this study introduces the Multi-Strategy and Improved DBO (MSIDBO) Algorithm. The MSIDBO algorithm incorporates several advanced computational techniques to enhance its performance. Firstly, it introduces a random reverse learning strategy to improve population diversity and mitigate early convergence or local stagnation issues present in the DBO algorithm. Additionally, a fitness-distance balancing strategy is employed to better manage the trade-off between diversity and convergence within the population. Furthermore, the algorithm utilizes a spiral foraging strategy to enhance precision, promote strong exploratory capabilities, and prevent being trapped in local optima. To further enhance the global search ability and particle utilization of the MSIDBO algorithm, it combines the Optimal Dimension-Wise Gaussian Mutation strategy. By minimizing premature convergence, population diversity is increased, and the convergence of the algorithm is accelerated. This expansion of the search space reduces the likelihood of being trapped in local optima during the evolutionary process. To demonstrate the effectiveness of the MSIDBO algorithm, extensive experiments are conducted using benchmark test functions, comparing its performance against other well-known metaheuristic algorithms. The results highlight the feasibility and superiority of MSIDBO in solving optimization problems. Moreover, the MSIDBO algorithm is applied to path planning simulation experiments to showcase its practical application potential. A comparison with the DBO algorithm shows that MSIDBO generates shorter and faster paths, effectively addressing real-world application problems.

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Section: Optimal Per-dimension Gaussian Mutation Strategymentioning

confidence: 99%

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Li,

Liu,

Shao

et al. 2023

Electronics

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“…The SCADL-RWDC technique applied the SCA-FS model to choose features optimally in this work. SCA is a metaheuristic approach developed with shallow efficiency [19] and a population-based method that begins with searching for random solutions. Thus, each random optimization highlights the exploitation and exploration of the problem.…”

Section: Algorithmic Design Of Sca-fs Techniquementioning

confidence: 99%

“…Eqs. (11), (12) are utilized for evaluating distinct factors in ( 14)- (19). Now, X alpha , X beta , and X delta denote the better position at the t iteration, leading the remaining population to the optimum solution with the best searching capability.…”

Section: A Pho = { *mentioning

confidence: 99%

Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment

Alohali¹,

Elsadig²,

Al‐Wesabi³

et al. 2023

Computer Systems Science and Engineering

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With the advent of the Internet of Things (IoT), several devices like sensors nowadays can interact and easily share information. But the IoT model is prone to security concerns as several attackers try to hit the network and make it vulnerable. In such scenarios, security concern is the most prominent. Different models were intended to address these security problems; still, several emergent variants of botnet attacks like Bashlite, Mirai, and Persirai use security breaches. The malware classification and detection in the IoT model is still a problem, as the adversary reliably generates a new variant of IoT malware and actively searches for compromise on the victim devices. This article develops a Sine Cosine Algorithm with Deep Learning based Ransomware Detection and Classification (SCADL-RWDC) method in an IoT environment. In the presented SCADL-RWDC technique, the major intention exists in recognizing and classifying ransomware attacks in the IoT platform. The SCADL-RWDC technique uses the SCA feature selection (SCA-FS) model to improve the detection rate. Besides, the SCADL-RWDC technique exploits the hybrid grey wolf optimizer (HGWO) with a gated recurrent unit (GRU) model for ransomware classification. A widespread experimental analysis is performed to exhibit the enhanced ransomware detection outcomes of the SCADL-RWDC technique. The comparison study reported the enhancement of the SCADL-RWDC technique over other models.

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“…Researchers have made improvements to SCA. Zhou et al [33] used Gaussian mutation to increase the diversity of SCA, and the proposed FGSCA became a powerful tool. Ma et al [34] used a nonlinear strategy and added a hill-climbing strategy in the local search part.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Sine Cosine and Particle Swarm Optimization Algorithm for High-Dimensional Global Optimization Problem and Its Application

Wang,

Gao,

2024

Mathematics

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Particle Swarm Optimization (PSO) is facing more challenges in solving high-dimensional global optimization problems. In order to overcome this difficulty, this paper proposes a novel PSO variant of the hybrid Sine Cosine Algorithm (SCA) strategy, named Velocity Four Sine Cosine Particle Swarm Optimization (VFSCPSO). The introduction of the SCA strategy in the velocity formulation ensures that the global optimal solution is found accurately. It increases the flexibility of PSO. A series of experiments are conducted on the CEC2005 test suite with compositional algorithms, algorithmic variants, and good intelligent algorithms. The experimental results show that the algorithm effectively improves the overall performance of compositional algorithms; the Friedman test proves that the algorithm has good competitiveness. The algorithm also performs better in PID parameter tuning. Therefore, the VFSCPSO is able to solve the high-dimensional global optimization problems in a better way.

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Spiral Gaussian mutation sine cosine algorithm: Framework and comprehensive performance optimization

Cited by 30 publications

References 130 publications

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment

Hybrid Sine Cosine and Particle Swarm Optimization Algorithm for High-Dimensional Global Optimization Problem and Its Application

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