.... Conventional fixed-limit or static fuzzy-control methods cannot effectively balance safety and throughput when multiple disturbances occur simultaneously. Design/methodology/approach Therefore, this research develops a dynamic-inertia fuzzy control with particle swarm optimization framework to realize self...

...Orhan Ali Nooruldeen; Shahram Azizifar; Ahmed Ghareeb; Ali Hussein Abdulkarim; Mohamed M. Awad Purpose To develop a hybrid machine-learning framework that integrates a Physics-Informed Neural Network (PINN) with Particle Swarm Optimization (PSO)–tuned regression models to deliver fast, accurate...

..., particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence...

... using average percentage of fault detected metric. Particle swarm optimization Concolic testing MC/DC Test suite prioritization Test suite prioritization is a technique of regression testing process that is used to detect faults early in the testing cycle. It is not feasible to execute all...

...) . During the last decade, nature-inspired algorithms for optimization problems continue to increase, such as ant colony optimization, genetic algorithms (GA), artificial bee colony optimization and particle swarm optimization (PSO); these algorithms have been used to solve the CLRP; a memetic algorithm...

...Jehad Ababneh Purpose – The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm. Design/methodology/approach – The BBO and the PSO algorithms are jointly used in to order to combine...