A New Multitask Joint Learning Framework for Expensive Multi-Objective Optimization Problems

Abstract

In this paper, we propose a multi-objective optimization algorithm based on multitask conditional neural processes (MTCNPs) to deal with expensive multi-objective optimization problems (MOPs). In the proposed algorithm, an MOP is decomposed into several subproblems. Several related subproblems are assigned to a task group and jointly handled using an MTCNPs surrogate model, in which multi-task learning is incorporated to exploit the similarity across the subproblems via joint surrogate model learning. Each subproblem in a task group is modeled by a conditional neural processes (CNPs) instead of a Gaussian Process (GP), thus avoiding the calculation of the GP covariance matrix. In addition, multiple subproblems are jointly learned through a multi-layer similarity network with activation function, which can measure and utilize the similarity and useful information among subproblems more effectively and improve the accuracy and robustness of the surrogate model. Experimental studies under several scenarios indicate that the proposed algorithm performs better than several state-of-the-art multi-objective evolutionary algorithms for expensive MOPs. The parameter sensitivity and effectiveness of the proposed algorithm are analyzed in detail.