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

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 >

Zhiwei Ye¹, Tao Zhao¹, Chun Liu^1,*, Daode Zhang¹, Wanfang Bai²

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1479-1495, 2023, DOI:10.32604/cmc.2023.038787

Abstract The Honey Badger Algorithm (HBA) is a novel meta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers. The dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA, which has been applied in photovoltaic systems and optimization problems effectively. However, HBA tends to suffer from the local optimum and low convergence. To alleviate these challenges, an improved HBA (IHBA) through fusing multi-strategies is presented in the paper. It introduces Tent chaotic mapping and composite mutation factors to HBA, meanwhile, the random control parameter is More >