期刊
MATHEMATICS
卷 10, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/math10050761
关键词
stochastic cognitive dominance leading; multimodal problems; particle swarm optimization; global optimization; evolutionary algorithm
类别
资金
- National Natural Science Foundation of China [62006124, U20B2061]
- Natural Science Foundation of Jiangsu Province [BK20200811]
- Natural Science Foundation of the Jiangsu Higher Education Institutions of China [20KJB520006]
- National Research Foundation of Korea [NRF-2021H1D3A2A01082705]
- Startup Foundation for Introducing Talent of NUIST
- National Research Foundation of Korea [2021H1D3A2A01082705] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper proposes a new optimization algorithm, SCDLPSO, which introduces a stochastic cognitive dominance leading mechanism and shows good performance in solving optimization problems in the era of big data and Internet of Things.
Optimization problems become increasingly complicated in the era of big data and Internet of Things, which significantly challenges the effectiveness and efficiency of existing optimization methods. To effectively solve this kind of problems, this paper puts forward a stochastic cognitive dominance leading particle swarm optimization algorithm (SCDLPSO). Specifically, for each particle, two personal cognitive best positions are first randomly selected from those of all particles. Then, only when the cognitive best position of the particle is dominated by at least one of the two selected ones, this particle is updated by cognitively learning from the better personal positions; otherwise, this particle is not updated and directly enters the next generation. With this stochastic cognitive dominance leading mechanism, it is expected that the learning diversity and the learning efficiency of particles in the proposed optimizer could be promoted, and thus the optimizer is expected to explore and exploit the solution space properly. At last, extensive experiments are conducted on a widely acknowledged benchmark problem set with different dimension sizes to evaluate the effectiveness of the proposed SCDLPSO. Experimental results demonstrate that the devised optimizer achieves highly competitive or even much better performance than several state-of-the-art PSO variants.
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