Development of efficient particle swarm optimizers by using concepts from evolutionary algorithms

Particle swarm optimization (PSO) has been in practice for more than 10 years now and has gained wide popularity in various optimization tasks. In the context to single objective optimization, this paper studies two aspects of PSO: (i) its ability to approach an 'optimal basin', and (ii) to find the optimum with high precision once it enters the region. of interest. We test standard PSO algorithms and discover their inability in handling both aspects efficiently. To address these issues with PSO, we propose an evolutionary algorithm (EA) which is algorithmically similar to PSO, and then borrow different EA-specific operators to enhance the PSO's performance. Our final proposed PSO contains a parent-centric recombination operator instead of usual particle update rule, but maintains PSO's individualistic trait and has a demonstrated performance comparable to a well-known GA (and outperforms the GA in some occasions). Moreover, the modified PSO algorithm is found to scale up to solve as large as 100-variable problems. This study emphasizes the need for similar such studies in establishing an equivalence between various genetic/evolutionary and other bio-inspired algorithms, a process that may lead us to better understand the scope and usefulness of various operators associated with each algorithm