A New Selection Mechanism Based on Hypervolume and its Locality Property


In this paper, we propose a new selection mechanism based on the hypervolume indicator and on its "locality property", which is incorporated into the SMS-EMOA, giving rise to the so-called improved SMS-EMOA (iSMS-EMOA). Our proposed selection mechanism is validated using standard test functions taken from the specialized literature, having three to six objective functions. iSMS-EMOA is compared with respect to its predecessor SMS-EMOA and with respect to another version of SMS-EMOA that uses the approximation of the hypervolume indicator, instead of its exact calculation. Our preliminary results indicate that our proposed selection mechanism outperforms the selection mechanisms based on the hypervolume indicator that have been proposed in recent years, since it significantly reduces the computational time required by the algorithm without sacrificing quality in the approximation generated.