To DE or Not to DE? Multi-objective Differential Evolution Revisited from a Component-Wise Perspective

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• 作者：Leonardo C. T. Bezerra (16)
  Manuel L贸pez-Ib谩帽ez (16)
  Thomas St眉tzle (16)
  
  16. IRIDIA ; Universit茅 Libre de Bruxelles (ULB) ; Brussels ; Belgium
  
• 关键词：Multi ; objective optimization ; Evolutionary algorithms ; Differential evolution ; Component ; wise design
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9018
• 期：1
• 页码：48-63
• 全文大小：303 KB
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  3. Bezerra, LCT, L贸pez-Ib谩帽ez, M, St眉tzle, T Automatic design of evolutionary algorithms for multi-objective combinatorial optimization. In: Bartz-Beielstein, T, Branke, J, Filipi膷, B, Smith, J eds. (2014) Parallel Problem Solving from Nature 鈥?PPSN XIII. Springer, Heidelberg, pp. 508-517 CrossRef
  4. Bezerra, LCT, L贸pez-Ib谩帽ez, M, St眉tzle, T Deconstructing multi-objective evolutionary algorithms: An iterative analysis on the permutation flowshop. In: Pardalos, PM, Resende, MGC, Vogiatzis, C, Walteros, JL eds. (2014) LION 2014. Springer, Heidelberg, pp. 57-172
  5. Bezerra, L.C.T., L贸pez-Ib谩帽ez, M., St眉tzle, T.: To DE or not to DE? Multi-objective differential evolution revisited from a component-wise perspective, (2015). http://iridia.ulb.ac.be/supp/IridiaSupp2015-001/
  6. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), (2011)
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  11. L贸pez-Ib谩帽ez, M., Dubois-Lacoste, J., St眉tzle, T., Birattari, M.: The irace package, iterated race for automatic algorithm configuration. Tech. Rep. TR/IRIDIA/2011-004, IRIDIA, Universit茅 Libre de Bruxelles, Belgium (2011)
  12. L贸pez-Ib谩帽ez, M, St眉tzle, T (2012) The automatic design of multi-objective ant colony optimization algorithms. IEEE Trans. Evol. Comput. 16: pp. 861-875 CrossRef
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• 作者单位：Evolutionary Multi-Criterion Optimization
• 丛书名：978-3-319-15933-1
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Differential evolution (DE) research for multi-objective optimization can be divided into proposals that either consider DE as a stand-alone algorithm, or see DE as an algorithmic component that can be coupled with other algorithm components from the general evolutionary multiobjective optimization (EMO) literature. Contributions of the latter type have shown that DE components can greatly improve the performance of existing algorithms such as NSGA-II, SPEA2, and IBEA. However, several experimental factors have been left aside from that type of algorithm design, compromising its generality. In this work, we revisit the research on the effectiveness of DE for multi-objective optimization, improving it in several ways. In particular, we conduct an iterative analysis on the algorithmic design space, considering DE and environmental selection components as factors. Results show a great level of interaction between algorithm components, indicating that their effectiveness depends on how they are combined. Some designs present state-of-the-art performance, confirming the effectiveness of DE for multi-objective optimization.