Abstract
An environmental selection and transfer learning-based dynamic multiobjective optimization evolutionary algorithm
Highlights
A multiobjective optimization problem (MOP), which means an optimization problem with multiple objectives, is a major and widespread problem in both industrial applications and scientific research
The performance of the algorithm must be proved by experiments, so two recently proposed prediction based methods, namely, the population prediction strategy (PPS) [26], and a forward-looking prediction strategy (FPS) [53], for dealing with dynamic multiobjective optimization problems (DMOPs) is compared with the proposed strategy dynamic multiobjective optimization evolutionary algorithms (DMOEAs)-ESTL in the experimental studies
The Pareto Optimal Solution (PS) and Pareto Optimal Frontier (PF) of the testing functions have different shapes and each function belongs to a certain type, figure 6 and figure7 describe the true PS and PF of the six testing functions
Summary
A multiobjective optimization problem (MOP), which means an optimization problem with multiple objectives, is a major and widespread problem in both industrial applications and scientific research. The transfer learning, as an effective method in dealing with these complex problems, has played a significant role in solving of DMOPs. In this paper, we propose a new strategy entitled DOMEA-ESTL, which integrates the environmental selection and transfer leaning schemes. Experimental results show that this new strategy can effectively solve DMOPs. Compared with traditional method, this new scheme can enhance population diversity, and make the population respond quickly to the different degrees of environmental changes, and can accelerate the convergence of population by "experiences". (2) We introduce the transfer learning into this strategy This new scheme make full use of the “experiences”, which is used in memory based method, and it significantly improve the search efficiency in solving the DMOPs. This paper is organized as follows.
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