EVOLUTIONARY MULTITASKING: A NEW OPTIMIZATION TECHNIQUE
In the last decades, evolutionary algorithms (EAs) have been successfully applied to solve various optimization problems in science and technology. These issues are usually categorized into two groups: i) Single-objective optimization (SOO), where each point in the search space of the problem is map...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Dalat University
2018-09-01
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| Series: | Tạp chí Khoa học Đại học Đà Lạt |
| Subjects: | |
| Online Access: | http://tckh.dlu.edu.vn/index.php/tckhdhdl/article/view/428 |
| Summary: | In the last decades, evolutionary algorithms (EAs) have been successfully applied to solve various optimization problems in science and technology. These issues are usually categorized into two groups: i) Single-objective optimization (SOO), where each point in the search space of the problem is mapped to a target value scalar; and ii) Multi-objective optimization (MOO), where each point in the search space of the problem is mapped to a target vector. In this paper, we will introduce a completely new kind of third-party evolutionary multitasking, which allows simultaneous optimization of different optimization problems on a single population and is called multifactorial optimization (MFO). |
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| ISSN: | 0866-787X 0866-787X |