An alternative approach for particle swarm optimisation using serendipity

In the study of metaheuristic techniques, it is very common to deal with a problem known as premature convergence. This problem is widely studied in swarm intelligence algorithms such as particle swarm optimisation (PSO). Most approaches to the problem consider the generation and/or positioning of individuals in the search space randomly. This paper approaches the issue using the concept of serendipity and its adaptation in this new context. Several strategies that implement serendipity were evaluated in order to develop a PSO variant based on this concept. The results were compared with the traditional PSO considering the quality of the solutions and the ability to find global optimum. The new algorithm was also compared with a PSO variant of the literature. The experiments showed promising results related to the criteria mentioned above, but there is the need for additional adjustments to decrease the runtime.