期刊
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
卷 39, 期 6, 页码 1348-1361出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2009.2015281
关键词
Adaptive dominant replacement scheme; dynamic optimization problem (DOP); genetic algorithm (GA); Lamarckian learning; primal-dual mapping (PDM)
类别
资金
- National Nature Science Foundation of China (NSFC) [70431003, 70671020]
- National Innovation Research Community Science Foundation of China [60521003]
- National Support Plan of China [2006BAH02A09]
- Engineering and Physical Sciences Research Council (EPSRC) of U. K [EP/E060722/1]
- Hong Kong Polytechnic University Research [G-YH60]
- Engineering and Physical Sciences Research Council [EP/E060722/1] Funding Source: researchfish
- EPSRC [EP/E060722/1] Funding Source: UKRI
Recently, there has been an increasing interest in applying genetic algorithms (GAs) in dynamic environments. Inspired by the complementary and dominance mechanisms in nature, a primal-dual GA (PDGA) has been proposed for dynamic optimization problems (DOPs). In this paper, an important operator in PDGA, i.e., the primal-dual mapping (PDM) scheme, is further investigated to improve the robustness and adaptability of PDGA in dynamic environments. In the improved scheme, two different probability-based PDM operators, where the mapping probability of each allele in the chromosome string is calculated through the statistical information of the distribution of alleles in the corresponding gene locus over the population, are effectively combined according to an adaptive Lamarckian learning mechanism. In addition, an adaptive dominant replacement scheme, which can probabilistically accept inferior chromosomes, is also introduced into the proposed algorithm to enhance the diversity level of the population. Experimental results on a series of dynamic problems generated from several stationary benchmark problems show that the proposed algorithm is a good optimizer for DOPs.
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