Multi‐Objective Service Composition Using Enhanced Multi‐Objective Differential Evolution Algorithm
Shunshun Peng, Taolin Guo · Computational Intelligence and Neuroscience · 2023
In recent years, the optimization of multi‐objective service composition in distributed systems has become an important issue. Existing work makes a smaller set of Pareto‐optimal solutions to represent the Pareto Front (PF). However, they do not support complex mapping of the Pareto‐optimal solutions to quality of service (QoS) objective space, thus having limitations in providing a representative set of solutions. We propose an enhanced multi‐objective differential evolution algorithm to seek a representative set of solutions with good proximity and distributivity. Specially, we propose a dual strategy to adjust the usage of different creation operators, to maintain the evolutionary pressure toward the true PF. Then, we propose a reference vector neighbor search to have a fine‐grained search. The proposed approach has been tested on a real‐world dataset that locates a representative set of solutions with proximity and distributivity.