Cone Dominance-Based Interactive Evolutionary Multiobjective Algorithm for QoS-Driven Service Selection Problem

Abstract

QoS-driven Service Selection (QSS) problem can be modeled as an optimization problem which possesses multiple objectives. The QSS problem is a NP-hard problem in the combinatorial optimization field. The existing approaches for solving this problem are inefficient for multi-objective scenarios. To address the multiple objective scenario, we propose an interactive evolutionary multi-objective optimization (EMO) algorithm based on the cone dominance. This algorithm requires periodical interaction from the user to get their preference information. The preference information drives the search process toward a preferred part of the search space. The performance of the proposed algorithm is assessed for two and three objectives problem sets and the results demonstrate its ability to converge to the most preferred point. The evaluation of results indicate that the proposed approach is more efficient compared to NSGA-II in terms of the number of iterations required to reach the preferred point.