An Investigation of Logarithm Decreasing Inertia Weight Particle Swarm Optimization in Global Optimization Problem

ABSTRACT This research investigates Logarithm Decreasing Inertia Weight (LogDIW) to improve the performance of Particle Swarm Optimization (PSO). The general problem of PSO algorithm is premature convergence when solving complex optimization problem. Some researchers try to solve the problem by modifying the PSO or proposing another PSO variants. Some PSO variants proved to have a better performance than the original PSO. The purpose of this research is to obtain some experimental facts to prove the efficiency of LogDIWPSO if the parameters are tuned correctly and to show that the LogDIWPSO performs better compared to the other PSO variants. In the early step of the experiment, a percentage value of search space boundary is obtained. This step is important to compute the velocity threshold of LogDIW based on the optimization problem. The next experiment is done to measure the performance of LogDIWPSO using six benchmark functions in optimization problems and to prove the superiority of LogDIWPSO compared to the other PSO variants. The experiment result shows that LogDIW achieves better performance than the other PSO variants