Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …

The simplicity, transparency, reliability, high efficiency and robust nature of PID controllers
are some of the reasons for their high popularity and acceptance for control in process …

This study proposes a new nature-inspired metaheuristic that mimics the behaviour of the
prairie dogs in their natural habitat called the prairie dog optimization (PDO). The proposed …

Metaheuristics play a crucial role in solving optimization problems. The majority of such
algorithms are inspired by collective intelligence and foraging of creatures in nature. In this …

针对变压器故障诊断精度低的问题, 提出了一种多策略改进麻雀算法(MISSA)
与双向长短时记忆网络(BiLSTM) 的变压器故障诊断模型. 基于油中溶解气体分析(DGA) 技术 …

In the last few years, the formulation of real-world optimization problems and their efficient
solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In …

The economic development of numerous countries is defined by maritime supply chains to a
great extent. Substantial volumes of containerized cargoes delivered by ships are handled …

This paper proposes a new population-based optimization algorithm called hunter–prey
optimizer (HPO). This algorithm is inspired by the behavior of predator animals such as …

Breast cancer is the second leading cause of death in women; therefore, effective early
detection of this cancer can reduce its mortality rate. Breast cancer detection and …

Research in metaheuristics for global optimization problems are currently experiencing an
overload of wide range of available metaheuristic-based solution approaches. Since the …