An adaptive PSO based on motivation mechanism and acceleration restraint operator

To obtain precise solutions in optimization problems and decrease the risk of being trapped in local optima, researchers have studied on various improved particle swarm optimizations (PSO) and made a series of achievements. However, these methods focus on artificially altering the physical rules of motion, rather than strengthening the individual self-learning and adjustment during the optimization process, which is the original motive of the swarm-based evolutionary algorithms. In this paper, we propose a fresh self-adaptive variant, MMARO-PSO, which employs motivation mechanism to simulate the behavior of intelligent organisms more vividly. We manage to simplify the update formulas and give each term a definite bio-psychic sense. Furthermore, we introduce a vectorized operator to restrain particle´s acceleration, instead of the inertia weight parameter in conventional methods. Large number of experiments were conducted and the results illustrate that these innovations make the technique perform more consistently to find a better balance between global exploration and local exploitation, compared with the existing versions, e.g. SPSO, e1-PSO, ARFPSO, and (k, l)PSO.