Pareto evolutionary algorithm based on data mining for a kind of task assignment problem considering agent reliability

Abstract

The task assignment problem (TAP) is a well-known combinatorial optimization problem with rich applications. Most existing TAPs have minimized cost criteria, and are often unsuitable for complex decision-making. This work focuses on a specific multi-objective TAP considering the reliability of agents (MOTAP/RA). The objective is to optimize the total assignment cost and total agent reliability. To solve the problem, we present a Pareto evolutionary algorithm based on data mining (PEADM). The primary contributions of this work are two-fold: a mathematical model of MOTAP/RA and a novel search model of PEADM. For the mathematical model, we propose for the first time the MOTAP/RA model based on which problem-specific heuristics are introduced. For the PEADM framework, we introduce two populations to capture valuable algorithm knowledge: a main population (MP) and an auxiliary population (AP). We update MP using an improved nondominated sorting method while extracting pertinent frequent patterns from MP through a dedicated data mining approach. Then, we generate new individuals of AP based on the mined frequent patterns and so improve MP based on AP. Results of experiments on 180 instances with up to 310 tasks show that PEADM competes favorably with state-of-the-art solution methods.