ETMO: Evolutionary Transfer Multiobjective Optimization

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Deep Learningetmo
Overview

ETMO: Evolutionary Transfer Multiobjective Optimization

To promote the research on ETMO, benchmark problems are of great importance to ETMO algorithm analysis, which helps designers or practitioners to better understand the merit and demerit of ETMO algorithms. However, although there are many areas that ETMO can cover, there are few types of benchmark problems that exist. Thus, a new test function suite (called ETMOF) is designed here, covering diverse types and properties in the case of multi-task, such as various formulation models, various PS geometries and PF shapes, large-scale variables, dynamically changed environment, etc. Specifically, the proposed test suite has 40 benchmark problems, which can be classified as the following five types:

(1) Evolutionary Transfer Multiobjective Optimization Problems: ETMOF1 to ETMOF8;

(2) Evolutionary Transfer Many-objective Optimization Problems: ETMOF9 to ETMOF16;

(3) Evolutionary Transfer Large-scale Multiobjective Optimization Problems: ETMOF17 to ETMOF24;

(4) Evolutionary Transfer Many-Task Optimization Problems: ETMOF25 to ETMOF32;

(5) Evolutionary Transfer Dynamic Multiobjective Optimization Problems: ETMOF33 to ETMOF40;

The detail definitions of these 40 benchmark problems can be found in technical report, which can be downloaded in https://www.scholat.com/vpost.html?pid=160180, and all benchmark functions have been implemented in JAVA code.

Please unzip the file Data_ETMO.rar before you use it, which contains other datas, e.g., the true Pareto fronts of the related problems.

Owner
Songbai Liu
Songbai Liu is currently a PhD student in Department of Computer Science, City University of Hong Kong, Hong Kong.
Songbai Liu
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