Decomposition Based Multi-objectives Evolutionary Algorithms Challenges and Circumvention
| Autor: | Aliaa A. A. Youssif, Sherin M. Omran, Wessam H. El-Behaidy |
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| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Research areas Computer science Evolutionary algorithm 0102 computer and information sciences 02 engineering and technology 01 natural sciences Multi-objective optimization Single objective Application areas 010201 computation theory & mathematics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Adaptation (computer science) Focus (optics) D algorithm |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030522452 SAI (2) |
| DOI: | 10.1007/978-3-030-52246-9_6 |
| Popis: | Decomposition based Multi Objectives Evolutionary Algorithms (MOEA/D) became one of research focus in the last decade. That is due to its simplicity as well as its effectiveness in solving both constrained and unconstrained problems with different Pareto Front (PF) geometries. This paper presents the challenges on different research areas concerning MOEA/D. Firstly, the original MOEA/D algorithm is explained. Its basic idea is to decompose the Multi Objectives Optimization (MOO) problem into multiple single objective optimization sub problems and works concurrently to solve these sub problems. Each sub problem is optimized with the help of the information gained from its neighborhood. Then, two major factors that affect the search ability of decomposition based MOO algorithms: Scalarization Functions (SF) and weight vectors generation and adaptation are discussed. Furthermore, the researches in two categories of different variants of MOEA/D are illustrated. Finally, the real world application areas that applied the decomposition approach are mentioned. |
| Databáze: | OpenAIRE |
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