EBCRO: Hybrid chemical reaction with employed bee operator

Li, Zhiyong; Yuan, Tingkun; Yang, Bo; Jiang, Shengming; Xie, Yan

doi:10.1109/fskd.2017.8393061

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…By combining the CRO with other heuristic algorithms, many novel CRO-based algorithms have been proposed, including random molecule-based chemical reaction optimization (RMCRO) [17], particle swarm and CRO (HP-CRO) [18], employed bee operator-based CRO (EBCRO) [19] and bat mutation-based CRO (BMCRO) [20].…”

Section: Related Workmentioning

confidence: 99%

An Opposition-Based Learning CRO Algorithm for Solving the Shortest Common Supersequence Problem

Luo

Chen

Fuentes

et al. 2022

Entropy

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As a non-deterministic polynomial hard (NP-hard) problem, the shortest common supersequence (SCS) problem is normally solved by heuristic or metaheuristic algorithms. One type of metaheuristic algorithms that has relatively good performance for solving SCS problems is the chemical reaction optimization (CRO) algorithm. Several CRO-based proposals exist; however, they face such problems as unstable molecular population quality, uneven distribution, and local optimum (premature) solutions. To overcome these problems, we propose a new approach for the search mechanism of CRO-based algorithms. It combines the opposition-based learning (OBL) mechanism with the previously studied improved chemical reaction optimization (IMCRO) algorithm. This upgraded version is dubbed OBLIMCRO. In its initialization phase, the opposite population is constructed from a random population based on OBL; then, the initial population is generated by selecting molecules with the lowest potential energy from the random and opposite populations. In the iterative phase, reaction operators create new molecules, where the final population update is performed. Experiments show that the average running time of OBLIMCRO is more than 50% less than the average running time of CRO_SCS and its baseline algorithm, IMCRO, for the desoxyribonucleic acid (DNA) and protein datasets.

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