Network characteristics for neighborhood field algorithms
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Evolutionary algorithms (EAs) have been successfully applied to solve numerous optimization problems. Neighborhood field optimization algorithm (NFO) has been proposed to integrate the neighborhood field in EAs, which utilizes local cooperation behaviors to explore new solutions. In this paper, certain new NFO variants are proposed based on the cooperation of descendent neighbors. The competitive and cooperative behaviors of NFO variants provide a remarkable ability to accelerate information exchanges and achieve global search. Experimental results show that NFO variants perform better than basic and other state-of-the-art EAs under different benchmark functions. For NFO and other EAs, it is difficult to quantify benefits of local cooperation in the optimization process. For this purpose, the cooperation behaviors are analyzed in a new network approach in this paper. In the proposed NFO variants, population graph shows a scale-free network with power-law distribution. Network characteristics, i.e., degree distribution, cluster coefficient and average degree, are used to quantify the cooperation behaviors. Experimental results show that network characteristics can effectively indicate the optimization performance of NFO variants in terms of convergence rate and population diversity. NFO variants with large cluster coefficients and significant heterogeneous characteristics can achieve a significant performance improvement on numerous problems.
KeywordsEvolutionary algorithm Neighborhood field Cooperation behavior Scale-free network Cluster coefficient
This work was supported by the National Natural Science Foundation of China (61803054, 61673190), the Fundamental Research Funds for the Central Universities (2019CDQYZDH030, 106112017CDJXY170003) and the Graduate Scientific Research and Innovation Foundation of Chongqing, China (CYB18064).
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Conflict of interest
The authors declare that they have no conflict of interest. The article is considered for publication on the understanding that the article has neither been published nor will be published anywhere else before being published in the journal of Neural Computing and Applications.
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