An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning

Abstract

Artificial bee colony is a powerful optimization method, which has strong search abilities to solve many optimization problems. However, some studies proved that ABC has poor exploitation abilities in complex optimization problems. To overcome this issue, an improved ABC variant based on elite strategy and dimension learning (called ABC-ESDL) is proposed in this paper. The elite strategy selects better solutions to accelerate the search of ABC. The dimension learning uses the differences between two random dimensions to generate a large jump. In the experiments, a classical benchmark set and the 2013 IEEE Congress on Evolutionary (CEC 2013) benchmark set are tested. Computational results show the proposed ABC-ESDL achieves more accurate solutions than ABC and five other improved ABC variants.

Artificial Bee Colony for Affine and Perspective Template Matching

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Knowledge Inferencing Using Artificial Bee Colony and Rough Set for Diagnosis of Hepatitis Disease

“…Further, two major conflicting objectives of artificial bee colony optimization on feature selection is also discussed in the literature (Hancer et al, 2018). In 2019, an improved artificial bee colony technique based on elite strategy and dimension learning is introduced (Xiao et al, 2019). The improved binary artificial bee colony algorithm is analyzed over microarray data for feature selection (Wang & Dong, 2019).…”

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