Global and Local Search Experience-Based Evolutionary Sequential Transfer Optimization

Abstract

Evolutionary sequential transfer optimization (ESTO), which aims to better optimize a target task using the knowledge extracted from a number of previously solved source tasks, has been gaining continually increasing research attention over the years. Particularly, solution-based ESTO (S-ESTO) that transfers task solutions has been receiving much popularity due to its ease of implementation and optimizer independency. However, the existing S-ESTO algorithms put much emphasis on utilizing source optimized solutions standing for global search experience without being aware of the potential of intermediate solutions that represent local optimization experience. Besides, most of them cannot take full advantage of the solution data from evolutionary search. In the light of the above, this study aims to develop a global and local search experience-based solution transfer technique to maximally release the potential of optimization experience hidden in the source tasks. First, a novel transferability metric named landscape encoding-based rank correlation (LERC) is developed. Then, we propose to divide the optimization experience into two classes: 1) global and 2) local search experience. Accordingly, by instantiating LERC into global and local versions, we develop two distinct transfer methods to exploit the global and local search experience, respectively. Finally, by combining the two transfer methods, we propose an S-ESTO algorithm that can transfer the global and local search experience simultaneously for maximum performance enhancement for the target task. Experiments conducted on a set of benchmark problems and a practical case study verify the efficacy of the proposed methods. The source code of our algorithm is available athttps://github.com/ccm831143/GL-LERC.