A Novel Sampling Method with Lévy Flight for Distribution-Based Discrete Particle Swarm Optimization
Koya Ihara, Shōhei Kato · 2021
We have proposed a novel sampling method (NS) for controlling step sizes and incorporating Lévy flight to distribution-based discrete particle swarm optimizations (DDP-SOs), which are discrete extended variants of particle swarm optimizations handling the continuous parameters of probability distributions over the variable values instead of directly handling discrete variables. Our previous work demonstrated that NS improved all DDPSOs on function optimization problems. However, on categorical problems, the NS did not improve DDPSOs designed for integer problems. For more detailed investigations, we conducted optimization experiments on NK landscapes. The results show that NS improves the DDPSOs' efficiency and robustness to large solution space and non-separable problems. Besides, we found that NS is effective on integer DDPSOs even for categorical optimization in cases where decision variables have a few states. In addition, the proposed methods were tested on feature selection experiments and achieved superior results compared to some evolutionary algorithms designed for feature selection.