我们引入了两种新的搜索策略,以进一步提高植被进化(VEGE)的性能,以解决连续优化问题。具体来说,第一个战略,称为动态成熟度策略,允许具有更好的适应性的个体有更高的概率产生更多的种子个体。这里,所有个人将首先被分配以产生固定数量的种子,然后,剩余的可分配种子数量将根据其适合度进行竞争性分配。由于VEGE在摆脱局部最优方面表现不佳,我们提出了多种变异策略作为第二搜索算子,采用几种不同的变异方法来增加种子个体的多样性。换句话说,每个生成的种子个体将随机选择其中一种方法以更低的概率突变。为了评估这两种拟议战略的性能,我们运行我们的建议(VEGE+两种策略),VEGE,以及CEC2013基准测试函数和七个流行的工程问题上的另外七个高级进化算法(EA)。最后,我们分析了这两种策略对VEGE的各自贡献。实验和统计结果证实,在大多数优化问题中,我们的建议可以显着加快收敛速度,并提高常规VEGE的收敛精度。
We introduce two new search strategies to further improve the performance of vegetation evolution (VEGE) for solving continuous optimization problems. Specifically, the first strategy, named the dynamic maturity strategy, allows individuals with better fitness to have a higher probability of generating more seed individuals. Here, all individuals will first become allocated to generate a fixed number of seeds, and then the remaining number of allocatable seeds will be distributed competitively according to their fitness. Since VEGE performs poorly in getting rid of local optima, we propose the diverse mutation strategy as the second search operator with several different mutation methods to increase the diversity of seed individuals. In other words, each generated seed individual will randomly choose one of the methods to mutate with a lower probability. To evaluate the performances of the two proposed strategies, we run our proposal (VEGE + two strategies), VEGE, and another seven advanced evolutionary algorithms (EAs) on the CEC2013 benchmark functions and seven popular engineering problems. Finally, we analyze the respective contributions of these two strategies to VEGE. The experimental and statistical results confirmed that our proposal can significantly accelerate convergence and improve the convergence accuracy of the conventional VEGE in most optimization problems.