18:T439,A fast and flexible general-purpose implementation of Particle Swarm Optimization (PSO) and Differential Evolution (DE) for solving global minimization problems is provided. It is designed to handle complex optimization tasks with nonlinear, non-differentiable, and multi-modal objective functions defined by users. There are five types of PSO variants: Particle Swarm Optimization (PSO, Eberhart & Kennedy, 1995) , Quantum-behaved particle Swarm Optimization (QPSO, Sun et al., 2004) , Locally convergent rotationally invariant particle swarm optimization (LcRiPSO, Bonyadi & Michalewicz, 2014) , Competitive Swarm Optimizer (CSO, Cheng & Jin, 2015) and Double exponential particle swarm optimization (DExPSO, Stehlik et al., 2024) . For the DE algorithm, six types in Storn, R. & Price, K. (1997) are included: DE/rand/1, DE/rand/2, DE/best/1, DE/best/2, DE/rand_to-best/1 and DE/rand_to-best/2.19:T439,A fast and flexible general-purpose implementation of Particle Swarm Optimization (PSO) and Differential Evolution (DE) for solving global minimization problems is provided. It is designed to handle complex optimization tasks with nonlinear, non-differentiable, and multi-modal objective functions defined by users. There are five types of PSO variants: Particle Swarm Optimization (PSO, Eberhart & Kennedy, 1995) , Quantum-behaved particle Swarm Optimization (QPSO, Sun et al., 2004) , Locally convergent rotationally invariant particle swarm optimization (LcRiPSO, Bonyadi & Michalewicz, 2014) , Competitive Swarm Optimizer (CSO, Cheng & Jin, 2015) and Double exponential particle swarm optimization (DExPSO, Stehlik et al., 2024) . For the DE algorithm, six types in Storn, R. & Price, K. (1997

globpso-package