Tareq Hamadneh¹, Belal Batiha², Omar Alsayyed³, Frank Werner^4,*, Zeinab Monrazeri⁵, Mohammad Dehghani^5,*, Kei Eguchi⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2253-2323, 2024, DOI:10.32604/cmes.2024.055171 - 31 October 2024

Abstract This paper introduces the Wolverine Optimization Algorithm (WoOA), a biomimetic method inspired by the foraging behaviors of wolverines in their natural habitats. WoOA innovatively integrates two primary strategies: scavenging and hunting, mirroring the wolverine’s adeptness in locating carrion and pursuing live prey. The algorithm’s uniqueness lies in its faithful simulation of these dual strategies, which are mathematically structured to optimize various types of problems effectively. The effectiveness of WoOA is rigorously evaluated using the Congress on Evolutionary Computation (CEC) 2017 test suite across dimensions of 10, 30, 50, and 100. The results showcase WoOA’s robust… More >

Jian Zhao^1,2,*, Kang Wang^1,2, Jiacun Wang^3,*, Xiwang Guo⁴, Liang Qi⁵

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 603-623, 2024, DOI:10.32604/cmc.2024.050523 - 15 October 2024

Abstract This paper introduces the African Bison Optimization (ABO) algorithm, which is based on biological population. ABO is inspired by the survival behaviors of the African bison, including foraging, bathing, jousting, mating, and eliminating. The foraging behavior prompts the bison to seek a richer food source for survival. When bison find a food source, they stick around for a while by bathing behavior. The jousting behavior makes bison stand out in the population, then the winner gets the chance to produce offspring in the mating behavior. The eliminating behavior causes the old or injured bison to More >

Oluwaseun Peter Ige^1,2, Keng Hoon Gan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1847-1865, 2024, DOI:10.32604/cmes.2024.053373 - 27 September 2024

Abstract Feature selection is a crucial technique in text classification for improving the efficiency and effectiveness of classifiers or machine learning techniques by reducing the dataset’s dimensionality. This involves eliminating irrelevant, redundant, and noisy features to streamline the classification process. Various methods, from single feature selection techniques to ensemble filter-wrapper methods, have been used in the literature. Metaheuristic algorithms have become popular due to their ability to handle optimization complexity and the continuous influx of text documents. Feature selection is inherently multi-objective, balancing the enhancement of feature relevance, accuracy, and the reduction of redundant features. This… More >

Tareq Hamadneh^1,2, Khalid Kaabneh³, Omar Alssayed⁴, Kei Eguchi^5,*, Zeinab Monrazeri⁶, Mohammad Dehghani⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1725-1808, 2024, DOI:10.32604/cmes.2024.053236 - 27 September 2024

Abstract In this article, a novel metaheuristic technique named Far and Near Optimization (FNO) is introduced, offering versatile applications across various scientific domains for optimization tasks. The core concept behind FNO lies in integrating global and local search methodologies to update the algorithm population within the problem-solving space based on moving each member to the farthest and nearest member to itself. The paper delineates the theory of FNO, presenting a mathematical model in two phases: (i) exploration based on the simulation of the movement of a population member towards the farthest member from itself and (ii)… More >

Pouyan Razmjouei¹, Elaheh Moharamkhani², Mohamad Hasanvand³, Maryam Daneshfar⁴, Mohammad Shokouhifar^5,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3855-3880, 2024, DOI:10.32604/cmc.2024.054460 - 12 September 2024

Abstract This study investigates the application of deep learning, ensemble learning, metaheuristic optimization, and image processing techniques for detecting lung and colon cancers, aiming to enhance treatment efficacy and improve survival rates. We introduce a metaheuristic-driven two-stage ensemble deep learning model for efficient lung/colon cancer classification. The diagnosis of lung and colon cancers is attempted using several unique indicators by different versions of deep Convolutional Neural Networks (CNNs) in feature extraction and model constructions, and utilizing the power of various Machine Learning (ML) algorithms for final classification. Specifically, we consider different scenarios consisting of two-class colon… More >

Milos Dobrojevic^1,4, Luka Jovanovic¹, Lepa Babic³, Miroslav Cajic⁵, Tamara Zivkovic⁶, Miodrag Zivkovic², Suresh Muthusamy⁷, Milos Antonijevic², Nebojsa Bacanin^2,4,8,9,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4997-5027, 2024, DOI:10.32604/cmc.2024.054459 - 12 September 2024

Abstract Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones, computers, or tablets. It can occur through various channels, such as social media, text messages, online forums, or gaming platforms. Cyberbullying involves using technology to intentionally harm, harass, or intimidate others and may take different forms, including exclusion, doxing, impersonation, harassment, and cyberstalking. Unfortunately, due to the rapid growth of malicious internet users, this social phenomenon is becoming more frequent, and there is a huge need to address this issue. Therefore, the main goal of the research… More >

Adel Sabry Eesa^*

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 697-723, 2024, DOI:10.32604/iasc.2024.053192 - 06 September 2024

Abstract While many metaheuristic optimization algorithms strive to address optimization challenges, they often grapple with the delicate balance between exploration and exploitation, leading to issues such as premature convergence, sensitivity to parameter settings, and difficulty in maintaining population diversity. In response to these challenges, this study introduces the Chase, Pounce, and Escape (CPE) algorithm, drawing inspiration from predator-prey dynamics. Unlike traditional optimization approaches, the CPE algorithm divides the population into two groups, each independently exploring the search space to efficiently navigate complex problem domains and avoid local optima. By incorporating a unique search mechanism that integrates More >

Farouq Zitouni^1,*, Saad Harous², Abdulaziz S. Almazyad³, Ali Wagdy Mohamed^4,5, Guojiang Xiong⁶, Fatima Zohra Khechiba¹, Khadidja Kherchouche¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 219-265, 2024, DOI:10.32604/cmes.2024.052001 - 20 August 2024

Abstract Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems. This approach aims to leverage the strengths of multiple algorithms, enhancing solution quality, convergence speed, and robustness, thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks. In this paper, we introduce a hybrid algorithm that amalgamates three distinct metaheuristics: the Beluga Whale Optimization (BWO), the Honey Badger Algorithm (HBA), and the Jellyfish Search (JS) optimizer. The proposed hybrid algorithm will be referred to as BHJO. Through this fusion, the BHJO algorithm aims to… More >

Tareq Hamadneh¹, Khalid Kaabneh², Ibraheem AbuFalahah³, Gulnara Bektemyssova^4,*, Galymzhan Shaikemelev⁴, Dauren Umutkulov⁴, Sayan Omarov⁵, Zeinab Monrazeri⁶, Frank Werner⁷, Mohammad Dehghani^6,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2721-2741, 2024, DOI:10.32604/cmc.2024.054317 - 15 August 2024

Abstract This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization (MFO), inspired by the unique behaviors observed in magnificent frigatebirds in their natural habitats. The foundation of MFO is based on the kleptoparasitic behavior of these birds, where they steal prey from other seabirds. In this process, a magnificent frigatebird targets a food-carrying seabird, aggressively pecking at it until the seabird drops its prey. The frigatebird then swiftly dives to capture the abandoned prey before it falls into the water. The theoretical framework of MFO is thoroughly detailed and mathematically represented, mimicking the frigatebird’s… More >

Tareq Hamadneh¹, Khalid Kaabneh², Omar Alssayed³, Gulnara Bektemyssova^4,*, Galymzhan Shaikemelev⁴, Dauren Umutkulov⁴, Zoubida Benmamoun⁵, Zeinab Monrazeri⁶, Mohammad Dehghani^6,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2005-2030, 2024, DOI:10.32604/cmc.2024.052401 - 15 August 2024

Abstract The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Management (SCM), which is characterized by elevated risks due to inadequate accountability and transparency. To address these challenges and improve operations in green manufacturing, optimization algorithms play a crucial role in supporting decision-making processes. In this study, we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms, notably the Stork Optimization Algorithm (SOA). The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature. The theoretical framework of SOA is… More >