Transfer Learning based Dynamic Multiobjective Optimization Algorithms

TL;DR

This paper introduces Tr-DMOEA, a transfer learning framework integrated with population-based evolutionary algorithms to efficiently solve dynamic multiobjective optimization problems by reusing past experiences.

Contribution

It proposes a novel transfer learning-based framework that enhances existing multiobjective evolutionary algorithms without extensive modifications.

Findings

Tr-DMOEA improves convergence speed on benchmark functions.

The approach effectively reuses past experience for dynamic optimization.

Experimental results outperform some state-of-the-art methods.

Abstract

One of the major distinguishing features of the dynamic multiobjective optimization problems (DMOPs) is the optimization objectives will change over time, thus tracking the varying Pareto-optimal front becomes a challenge. One of the promising solutions is reusing the "experiences" to construct a prediction model via statistical machine learning approaches. However most of the existing methods ignore the non-independent and identically distributed nature of data used to construct the prediction model. In this paper, we propose an algorithmic framework, called Tr-DMOEA, which integrates transfer learning and population-based evolutionary algorithm for solving the DMOPs. This approach takes the transfer learning method as a tool to help reuse the past experience for speeding up the evolutionary process, and at the same time, any population based multiobjective algorithms can benefit from this integration without any extensive modifications. To verify this, we incorporate the proposed approach into the development of three well-known algorithms, nondominated sorting genetic algorithm II (NSGA-II), multiobjective particle swarm optimization (MOPSO), and the regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA), and then employ twelve benchmark functions to test these algorithms as well as compare with some chosen state-of-the-art designs. The experimental results confirm the effectiveness of the proposed method through exploiting machine learning technology.