Hao Chen¹, Tong Xu¹, Yutian Huang², Dabo Xin^1,*, Changting Zhong^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.075792 - 29 January 2026

Abstract Optimization problems are prevalent in various fields of science and engineering, with several real-world applications characterized by high dimensionality and complex search landscapes. Starfish optimization algorithm (SFOA) is a recently optimizer inspired by swarm intelligence, which is effective for numerical optimization, but it may encounter premature and local convergence for complex optimization problems. To address these challenges, this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm (MCPSFOA). The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA, which integrates the exploratory mechanisms of SFOA with the diverse search capacity of… More >

Weicong Tan^1,#, Qiwu Wu^2,3,#,*, Lingzhi Jiang¹, Tao Tong², Yunchen Su²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3621-3652, 2025, DOI:10.32604/cmc.2025.068781 - 23 September 2025

Abstract This study introduces a novel algorithm known as the dung beetle optimization algorithm based on bounded reflection optimization and multi-strategy fusion (BFDBO), which is designed to tackle the complexities associated with multi-UAV collaborative trajectory planning in intricate battlefield environments. Initially, a collaborative planning cost function for the multi-UAV system is formulated, thereby converting the trajectory planning challenge into an optimization problem. Building on the foundational dung beetle optimization (DBO) algorithm, BFDBO incorporates three significant innovations: a boundary reflection mechanism, an adaptive mixed exploration strategy, and a dynamic multi-scale mutation strategy. These enhancements are intended to… More >

Hui Lv^1,#, Yuer Yang^2,3,4,#, Yifeng Lin^2,3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 925-953, 2025, DOI:10.32604/cmc.2025.065709 - 29 August 2025

Abstract It is evident that complex optimization problems are becoming increasingly prominent, metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional, nonlinear problems. However, the traditional Sparrow Search Algorithm (SSA) suffers from limited global search capability, insufficient population diversity, and slow convergence, which often leads to premature stagnation in local optima. Despite the proposal of various enhanced versions, the effective balancing of exploration and exploitation remains an unsolved challenge. To address the previously mentioned problems, this study proposes a multi-strategy collaborative improved SSA, which systematically integrates four complementary strategies: (1) the Northern Goshawk Optimization (NGO) mechanism… More >

Libo Han^1,2, Sha Tao^1,2, Wen Xia³, Weixin Sun³, Li Yan³, Wanlin Gao^1,2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2431-2449, 2025, DOI:10.32604/cmc.2024.057833 - 17 February 2025

Abstract At present, salient object detection (SOD) has achieved considerable progress. However, the methods that perform well still face the issue of inadequate detection accuracy. For example, sometimes there are problems of missed and false detections. Effectively optimizing features to capture key information and better integrating different levels of features to enhance their complementarity are two significant challenges in the domain of SOD. In response to these challenges, this study proposes a novel SOD method based on multi-strategy feature optimization. We propose the multi-size feature extraction module (MSFEM), which uses the attention mechanism, the multi-level feature… More >

Chunhui Li^1,2, Xiaoying Wang^1,2,*, Qingjie Zhang^1,2, Jiaye Liang¹, Aijing Zhang¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 645-674, 2025, DOI:10.32604/cmc.2024.058081 - 03 January 2025

Abstract Previous studies have shown that deep learning is very effective in detecting known attacks. However, when facing unknown attacks, models such as Deep Neural Networks (DNN) combined with Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) combined with LSTM, and so on are built by simple stacking, which has the problems of feature loss, low efficiency, and low accuracy. Therefore, this paper proposes an autonomous detection model for Distributed Denial of Service attacks, Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention (MSCNN-BiGRU-SHA), which is based on a Multi-strategy Integrated Zebra Optimization Algorithm (MI-ZOA). The… More >

Liang Zeng^1,2, Mai Hu¹, Chenning Zhang¹, Quan Yuan¹, Shanshan Wang^1,2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1677-1709, 2024, DOI:10.32604/cmc.2024.049717 - 18 July 2024

Abstract Optimization algorithms play a pivotal role in enhancing the performance and efficiency of systems across various scientific and engineering disciplines. To enhance the performance and alleviate the limitations of the Northern Goshawk Optimization (NGO) algorithm, particularly its tendency towards premature convergence and entrapment in local optima during function optimization processes, this study introduces an advanced Improved Northern Goshawk Optimization (INGO) algorithm. This algorithm incorporates a multifaceted enhancement strategy to boost operational efficiency. Initially, a tent chaotic map is employed in the initialization phase to generate a diverse initial population, providing high-quality feasible solutions. Subsequently, after… More >

Deng Yang¹, Chong Zhou^1,*, Xuemeng Wei², Zhikun Chen³, Zheng Zhang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1563-1593, 2024, DOI:10.32604/cmes.2024.048049 - 20 May 2024

Abstract In classification problems, datasets often contain a large amount of features, but not all of them are relevant for accurate classification. In fact, irrelevant features may even hinder classification accuracy. Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate. Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter, but the results obtained depend on the value of the parameter. To eliminate this parameter’s influence, the problem can be reformulated as a multi-objective optimization problem. The… More >

Sultan Alahmari¹, Saud Yonbawi², Suneetha Racharla³, E. Laxmi Lydia⁴, Mohamad Khairi Ishak⁵, Hend Khalid Alkahtani^6,*, Ayman Aljarbouh⁷, Samih M. Mostafa⁸

Computer Systems Science and Engineering, Vol.47, No.1, pp. 375-391, 2023, DOI:10.32604/csse.2023.036362 - 26 May 2023

Abstract Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes. Much spatial information and spectral signatures of hyperspectral images (HSIs) present greater potential for detecting and classifying fine crops. The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging (RSI) has become an indispensable application in the agricultural domain. It is significant for the prediction and growth monitoring of crop yields. Amongst the deep learning (DL) techniques, Convolution Neural Network (CNN) was the best method for classifying HSI for their incredible local contextual modeling ability, enabling spectral and spatial… More >

Jianguo Zheng, Shuilin Chen^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3619-3635, 2023, DOI:10.32604/csse.2023.038025 - 03 April 2023

Abstract To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm, this paper presents a new algorithm based on multi-strategy (ISMO). First, the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity. Second, this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency. Then, using the crisscross strategy, using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum. At More >

Xiangyan Tang^1,2, Dengfang Feng^2,*, KeQiu Li¹, Jingxin Liu², Jinyang Song³, Victor S. Sheng⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2789-2802, 2023, DOI:10.32604/cmc.2023.031304 - 31 October 2022

Abstract Data prediction can improve the science of decision-making by making predictions about what happens in daily life based on natural law trends. Back propagation (BP) neural network is a widely used prediction method. To reduce its probability of falling into local optimum and improve the prediction accuracy, we propose an improved BP neural network prediction method based on a multi-strategy sparrow search algorithm (MSSA). The weights and thresholds of the BP neural network are optimized using the sparrow search algorithm (SSA). Three strategies are designed to improve the SSA to enhance its optimization-seeking ability, leading More >