Improving Particle Swarm Optimization with Self-adaptive Parameters, Rotational Invariance, and Diversity Control - Vasconcelos, Matheus; Flexa, Caio; Moreira, Igor; Santos, Reginaldo; Sales, Claudomiro (Anais da Brazilian Conference on Intelligent Systems (BRACIS); 2021: Anais da X Brazilian Conference on Intelligent Systems, 2021)
Particle Swarm Optimization (PSO) algorithms are swarm intelligence methods that are effective in solving optimization problems. However, current techniques have some drawbacks: the particles of some PSO implementations are sensible to their input hyper-parameters, lack direction diversity in their movement, have rotational variance, and might prematurely converge due to rapid swarm diversity loss. This article addresses these issues by introducing Rotationally Invariant Attractive and Repulsive eXpanded PSO (RI-AR-XPSO) and Rotationally Invariant Semi-Autonomous eXpanded PSO (RI-SAXPSO) as improvements of Rotationally Invariant Semi-Autonomous PSO (RI-SAPSO) and eXpanded PSO (XPSO). Their swarm behavior was evaluated with classic functions in the literature and their accuracy was tested with the Congress on Evolutionary Computation (CEC) 2017 optimization problems, in whose results a statistical significance test was applied. The results obtained attest that strategies such as diversity control, automatic hyper-parameter adjustment, directional diversity, and rotational invariance improve performance without accuracy loss when adequately implemented.