Evolutionary Dynamic Multi-objective Optimization via Regression Transfer Learning

Abstract

Dynamic multi-objective optimization problems (DMOPs) remain a challenge to be settled, because of conflicting objective functions change over time. In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs. In this paper, a novel transfer learning based dynamic multi-objective optimization algorithm (DMOA) is proposed called regression transfer learning prediction based DMOA (RTLP-DMOA). The algorithm aims to generate an excellent initial population to accelerate the evolutionary process and improve the evolutionary performance in solving DMOPs. When an environmental change is detected, a regression transfer learning prediction model is constructed by reusing the historical population, which can predict objective values.Then, with the assistance of this prediction model, some high-quality solutions with better predicted objective values are selected as the initial population, which can improve the performance of the evolutionary process. We compare the proposed algorithm with three state-of-the-art algorithms on benchmark functions. Experimental results indicate that the proposed algorithm can significantly enhance the performance of static multi-objective optimization algorithms and is competitive in convergence and diversity. Keywords evolutionary algorithm, dynamic multi-objective optimization, transfer learning, regression prediction I. INTRODUCTION Many optimization problems in the real world [1] [2] involve multiple optimization functions which conflict with each other and change over time. These dynamic optimization problems are called Dynamic Multi-objective Optimization Problems (DMOPs) [3]. For example, in the design of job scheduling systems [4], a number of decision variables, such as procedures, components, and operation time, are involved, which determine objective functions of energy consumption, production, and stability. These conflicting objective functions always change with time. Hence, efficient DMOAs should rapidly arrange scheduling schemes according to the changing environments, and this ability is critical to robust scheduling systems. Z.WANG and M. JIANG and X.GAO and W.HU are with the school of informatics, Xiamen University, China, Fujian, 361005. Min JIANG and Xing GAO are corresponding authors and

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

“…Experimental results confirmed that this method outperformed three other prediction-based DMOAs. Wang et al [165] introduced a regression transfer learning model that incorporates SVR to predict the PS of DMOPs. They compared the algorithm with three state-of-the-art algorithms using benchmark functions.…”

Evolutionary Dynamic Multi-objective Optimisation: A Survey

“…Nonlinear models are developed to capture the nonlinearity of environmental changes. In [149], an regression transfer learning model embedding a support vector regressor is proposed to predict the PS of DMOPs over time.…”

“…3) Machine learning models improve change response. Transfer learning [73,149] is one of the machine learning techniques that have proven to learn from dynamic environments efectively and respond to environmental changes to a great standard. 4) Preference/reference-driven approaches simplify the search for solutions to DMOPs and therefore alleviate the complexity of change response by focusing on special points of interest to decision makers [175].…”