Particle Swarm Optimization with Gaussian Disturbance-based Elite Population for Single-objective Problem

Single-objective optimization, especially with con- straints, is the most common class of problems in biology, society, and energy. Among various optimization algorithms, swarm intelligence algorithms is undoubtedly an effective methods to solve this type of problem. In this study, we propose a novel swarm intelligence optimization method, namely GuLo, which adopts Gaussian random disturbance into elite population-based particle swarm optimization, which leads the improvement of local search. Comprehensive experimental results on a typical single-objective constrained optimization problem benchmark shows that GuLo has the outstanding performance than other state-of-the-art meta-heuristic optimization approaches.