An Adaptive Multipopulation Differential Evolution With Dynamic Population Reduction
An Adaptive Multipopulation Differential Evolution With Dynamic Population Reduction
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DOI：
10.1109/tcyb.2016.2617301
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发表时间：
2017-09-01
影响因子：
11.8
通讯作者：
Reynolds, Robert G.
中科院分区：
文献类型：
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作者：
Ali, Mostafa Z.;Awad, Noor H.;Reynolds, Robert G.
由于许多实际应用中存在优化问题，开发高效的进化算法吸引了许多研究人员。提出了一种新的差分进化算法sTDE-dR，以提高搜索质量，避免早熟收敛和停滞。种群聚集在多个部落中，并采用不同的突变和交叉策略。在该算法中，引入了基于竞争成功的方案来确定每个部落的生命周期及其对下一代的参与比例。在每个部落中，使用不同的自适应方案来控制缩放因子和交叉率。每个子组的平均成功率用于计算其下一代的参与比例。这保证了具有最佳自适应方案的成功部落只是引导搜索走向最佳解决方案的部落。使用动态减少方法动态减少种群规模。将所提出的启发式方法与来自 CEC2014 实参数单目标竞赛的一组具有挑战性的基准与几种最先进的算法进行了全面比较。与其他最先进的算法相比，结果证实了所提出的方法的鲁棒性。
Developing efficient evolutionary algorithms attracts many researchers due to the existence of optimization problems in numerous real-world applications. A new differential evolution algorithm, sTDE-dR, is proposed to improve the search quality, avoid premature convergence, and stagnation. The population is clustered in multiple tribes and utilizes an ensemble of different mutation and crossover strategies. In this algorithm, a competitive success-based scheme is introduced to determine the life cycle of each tribe and its participation ratio for the next generation. In each tribe, a different adaptive scheme is used to control the scaling factor and crossover rate. The mean success of each subgroup is used to calculate the ratio of its participation for the next generation. This guarantees that successful tribes with the best adaptive schemes are only the ones that guide the search toward the optimal solution. The population size is dynamically reduced using a dynamic reduction method. Comprehensive comparison of the proposed heuristic over a challenging set of benchmarks from the CEC2014 real parameter single objective competition against several state-of-the-art algorithms is performed. The results affirm robustness of the proposed approach compared to other state-of-the-art algorithms.