期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 25, 期 4, 页码 779-793出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2021.3065659
关键词
Adaptive region search (ARS); evolutionary computation (EC); large-scale optimization problems (LSOPs); region encoding scheme (RES); social learning particle swarm optimization (SLPSO)
资金
- National Key Research and Development Program of China [2019YFB2102102]
- Outstanding Youth Science Foundation [61822602]
- National Natural Science Foundations of China (NSFC) [61772207, 61873097]
- Key-Area Research and Development of Guangdong Province [2020B010166002]
- Guangdong Natural Science Foundation Research Team [2018B030312003]
- Guangdong-Hong Kong Joint Innovation Platform [2018B050502006]
In this study, a new region encoding scheme (RES) is proposed to extend the solution representation from a single point to a region, helping EC algorithms evolve faster. The SLPSO-ARS algorithm, combining RES and adaptive region search (ARS), maintains diversity while accelerating the convergence speed.
In the last decade, many evolutionary computation (EC) algorithms with diversity enhancement have been proposed to solve large-scale optimization problems in big data era. Among them, the social learning particle swarm optimization (SLPSO) has shown good performance. However, as SLPSO uses different guidance information for different particles to maintain the diversity, it often results in slow convergence speed. Therefore, this article proposes a new region encoding scheme (RES) to extend the solution representation from a single point to a region, which can help EC algorithms evolve faster. The RES is generic for EC algorithms and is applied to SLPSO. Based on RES, a novel adaptive region search (ARS) is designed to on the one hand keep the diversity of SLPSO and on the other hand accelerate the convergence speed, forming the SLPSO with ARS (SLPSO-ARS). In SLPSO-ARS, each particle is encoded as a region so that some of the best (e.g., the top P) particles can carry out region search to search for better solutions near their current positions. The ARS strategy offers the particle a greater chance to discover the nearby optimal solutions and helps to accelerate the convergence speed of the whole population. Moreover, the region radius is adaptively controlled based on the search information. Comprehensive experiments on all the problems in both IEEE Congress on Evolutionary Computation 2010 (CEC 2010) and 2013 (CEC 2013) competitions are conducted to validate the effectiveness and efficiency of SLPSO-ARS and to investigate its important parameters and components. The experimental results show that SLPSO-ARS can achieve generally better performance than the compared algorithms.
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