Exploration and exploitation are contradictory in differential evolution (DE) algorithm. In order to balance the search behavior between exploitation and exploration better, a novel self-adaptive dual-strategy differential evolution algorithm (SaDSDE) is proposed. Firstly, a dual-strategy mutation operator is presented based on the “DE/best/2” mutation operator with better global exploration ability and “DE/rand/2” mutation operator with stronger local exploitation ability. Secondly, the scaling factor self-adaption strategy is proposed in an individual-dependent and fitness-dependent way without extra parameters. Thirdly, the exploration ability control factor is introduced to adjust the global exploration ability dynamically in the evolution process. In order to verify and analyze the performance of SaDSDE, we compare SaDSDE with 7 state-of-art DE variants and 3 non-DE based algorithms by using 30 Benchmark test functions of 30-dimensions and 100-dimensions, respectively. The experiments results demonstrate that SaDSDE could improve global optimization performance remarkably. Moreover, the performance superiority of SaDSDE becomes more significant with the increase of the problems’ dimension.
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机译:在差分进化算法中,探索与开发是矛盾的。为了更好地平衡开发和勘探之间的搜索行为,提出了一种新型的双策略差分进化算法(SaDSDE)。首先,提出了一种具有较好全局勘探能力的“ DE / best / 2”突变算子和具有较强局部开发能力的“ DE / rand / 2”突变算子的双策略突变算子。其次,提出了比例因子自适应策略,以个体依赖和适应度为前提,无需额外参数。第三,引入勘探能力控制因素,在演化过程中动态调整全局勘探能力。为了验证和分析SaDSDE的性能,我们分别使用30个30维和100个维的基准测试函数,将SaDSDE与7种最新的DE变量和3种基于非DE的算法进行比较。实验结果表明,SaDSDE可以显着提高全局优化性能。此外,随着问题规模的增加,SaDSDE的性能优势变得更加重要。
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