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 >

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 >

V. G. Saranya^*, S. Karthik

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 127-150, 2024, DOI:10.32604/cmes.2024.053825 - 20 August 2024

Abstract Wireless Sensor Networks (WSNs) are a collection of sensor nodes distributed in space and connected through wireless communication. The sensor nodes gather and store data about the real world around them. However, the nodes that are dependent on batteries will ultimately suffer an energy loss with time, which affects the lifetime of the network. This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability. The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization (MFOA-EACO), where the Mayfly Optimization Algorithm (MFOA) is used 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 >

Yuanjun Dai^1,4,*, Dong Wang¹, Xiongfei Liu², Weimin Wu³

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.7, pp. 1665-1665, 2024, DOI:10.32604/fdmp.2024.053146 - 23 July 2024

Abstract This article has no abstract. More >

Yuanjun Dai^1,4,*, Dong Wang¹, Xiongfei Liu², Weimin Wu³

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.7, pp. 1647-1664, 2024, DOI:10.32604/fdmp.2024.046158 - 23 July 2024

Abstract To enhance the aerodynamic performance of wind turbine blades, this study proposes the adoption of a bionic airfoil inspired by the aerodynamic shape of an eagle. Based on the blade element theory, a non-uniform extraction method of blade elements is employed for the optimization design of the considered wind turbine blades. Moreover, Computational Fluid Dynamics (CFD) is used to determine the aerodynamic performances of the eagle airfoil and a NACA2412 airfoil, thereby demonstrating the superior aerodynamic performance of the former. Finally, a mathematical model for optimizing the design of wind turbine blades is introduced and More >

Ibraheem Abu Falahah¹, Osama Al-Baik², Saleh Alomari³, Gulnara Bektemyssova⁴, Saikat Gochhait^5,6, Irina Leonova⁷, Om Parkash Malik⁸, Frank Werner^9,*, Mohammad Dehghani¹⁰

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3631-3678, 2024, DOI:10.32604/cmc.2024.053189 - 20 June 2024

Abstract This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard’s sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard’s retreat to the treetops after feeding. To assess FLO’s efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two… More >

Hala AlShamlan^*, Halah AlMazrua^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 675-694, 2024, DOI:10.32604/cmc.2024.048146 - 25 April 2024

Abstract In this study, our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization (GWO) with Harris Hawks Optimization (HHO) for feature selection. The motivation for utilizing GWO and HHO stems from their bio-inspired nature and their demonstrated success in optimization problems. We aim to leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification. We selected leave-one-out cross-validation (LOOCV) to evaluate the performance of both two widely used classifiers, k-nearest neighbors (KNN) and support vector machine… More >