5. Conclusions

In this chapter, we introduced a PSO variant algorithm called Particle Swarm Optimization with Bio-inspired Aging Model (BAM-PSO) which was compared with other five popular bioinspired optimizers. This test was performed using popular benchmark functions with low and high dimensionality configuration.

We observed that the BAM-PSO algorithm has the potential to solve the premature convergence problem of PSO showing good results for both low and high dimensional problems with statistical relevance according to several non-parametric analyses. Furthermore, according to results shown in Section 4, BAM-PSO performs better than the selected PSO variants.

As shown in results section, BAM-PSO outperforms all other compared swarm-based algorithms with at least a confidence factor as high as P < 0:01. However, the cost of this improved accuracy is found in computation complexity due to the introduction of Eq. (2) and all the lifespan control for the particles; which in turn translates to computing time; this time increase was found to be approximately of at least 9 times the required computation time for the original PSO on the conducted experiments of section 5 and 1.5 times the required computation time for ALC-PSO. However, this increase in time is not fixed, as it depends on how early the premature convergence occurs and how many particles are replaced after senescence.

Finally, these experimental results provide support on the important role of aging mechanisms during the selection process in bio-inspired optimization algorithms, because the population-broad aging mechanism implemented in BAM-PSO allows the algorithm to provide better results than some other popular optimizers that does not implement aging.
