Cyber-Physical Optimization-Based Fuzzy Control Strategy for Plug-in Hybrid Electric Buses Using Iterative Modified Particle Swarm Optimization

Metropolis criterion pigeon-inspired optimization for multi-UAV swarm controller

“…The base Metropolis criterion has a reduced convergence speed due to the particle staying at the last position. Therefore, an IMC proposed by Yang et al is introduced and applied to IMCPIO [8] . For the method of updating the position 𝑋 𝑁 𝑐 𝑖 and velocity 𝑉 𝑁 𝑐 𝑖 of each pigeon in the first iteration, the improvement steps are shown in Figure 4.…”

“…As the temperature decreases, the algorithm is more likely to accept solutions that are slightly worse than the current solution, which helps the algorithm converge to the global optimum; (3) It adds a speed halving strategy, which controls the range of particle movement, improves search accuracy, and makes particles more stable near the global optimum. This reduces unnecessary jumps and is conducive to fine-tuning the solution; (4) It adopts a correction strategy from the IMPSO algorithm, proposed by Yang et al, which fixes the defect that particles easily fall into local optima and hover near the optimal position [8] .…”