论文标题
用于动态环境的各种聚类粒子群优化器：定位和跟踪多个Optima
A Diverse Clustering Particle Swarm Optimizer for Dynamic Environment: To Locate and Track Multiple Optima
论文作者
论文摘要
在现实生活中，大多数问题是动态的。已经提出了许多算法来解决静态问题，但是这些算法并不能解决或无法解决动态环境问题。虽然，已经提出了许多算法来处理动态问题，但是仍然存在有关粒子多样性以及已经发现的Optima的每种算法的局限性或缺点。为了克服这些局限性/缺点，我们提出了一种新的有效算法，通过跟踪和定位多个Optima并提高算法的多样性和收敛速度来有效地处理动态环境。在该算法中，已经提出了一种新方法，该方法探讨了未发现的搜索空间领域以增加算法的多样性。该算法还使用一种方法来有效处理重叠和拥挤的颗粒。布兰克（Branke）提出了移动峰值基准，该基准是文献中常用的MBP。我们还在移动峰值基准方面进行了不同的实验。在将实验结果与不同的最先进算法进行比较之后，可以看到我们的算法的性能更有效。
In real life, mostly problems are dynamic. Many algorithms have been proposed to handle the static problems, but these algorithms do not handle or poorly handle the dynamic environment problems. Although, many algorithms have been proposed to handle dynamic problems but still, there are some limitations or drawbacks in every algorithm regarding diversity of particles and tracking of already found optima. To overcome these limitations/drawbacks, we have proposed a new efficient algorithm to handle the dynamic environment effectively by tracking and locating multiple optima and by improving the diversity and convergence speed of algorithm. In this algorithm, a new method has been proposed which explore the undiscovered areas of search space to increase the diversity of algorithm. This algorithm also uses a method to effectively handle the overlapped and overcrowded particles. Branke has proposed moving peak benchmark which is commonly used MBP in literature. We also have performed different experiments on Moving Peak Benchmark. After comparing the experimental results with different state of art algorithms, it was seen that our algorithm performed more efficiently.
