期刊
APPLIED SOFT COMPUTING
卷 35, 期 -, 页码 161-174出版社
ELSEVIER
DOI: 10.1016/j.asoc.2015.06.022
关键词
Multi-objective optimization; Double-module framework; Immune algorithm; Differential evolution
资金
- National Natural Science Foundation of China [61402291, 61170283]
- National High-Technology Research and Development Program (863 Program) of China [2013AA01A212]
- Ministry of Education in the New Century Excellent Talents Support Program [NCET-12-0649]
- Foundation for Distinguished Young Talents in Higher Education of Guangdong [2014KQNCX129]
- Natural Science Foundation of Guangdong Province [S2013040011789]
- Shenzhen Technology Plan [JCYJ20130401095947219, JCYJ20140418095735608]
- Natural Science Foundation of SZU [201531]
Multi-objective optimization problems (MOPs) have become a research hotspot, as they are commonly encountered in scientific and engineering applications. When solving some complex MOPs, it is quite difficult to locate the entire Pareto-optimal front. To better settle this problem, a novel double-module immune algorithm named DMMO is presented, where two evolutionary modules are embedded to simultaneously improve the convergence speed and population diversity. The first module is designed to optimize each objective independently by using a sub-population composed with the competitive individuals in this objective. Differential evolution crossover is performed here to enhance the corresponding objective. The second one follows the traditional procedures of immune algorithm, where proportional cloning, recombination and hyper-mutation operators are operated to concurrently strengthen the multiple objectives. The performance of DMMO is validated by 16 benchmark problems, and further compared with several multi-objective algorithms, such as NSGA-II, SPEA2, SMSEMOA, MOEA/D, SMPSO, NNIA and MIMO. Experimental studies indicate that DMMO performs better than the compared targets on most of test problems and the advantages of double modules in DMMO are also analyzed. (C) 2015 Elsevier B.V. All rights reserved.
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