论文标题
基于新颖搜索的粒子群优化
Particle Swarm Optimization based on Novelty Search
论文作者
论文摘要
在本文中，我们提出了一种粒子群优化算法与新颖性搜索相结合。新颖的搜索找到了在搜索域中搜索的新颖位置，然后粒子群优化严格地搜索该区域以找到全局最佳解决方案。该方法在本地Optima中永远不会被阻止，因为它是由无客观的新颖搜索控制的。对于那些还有更多本地Optima和第二个全局最佳的功能远非真正的最佳功能，目前的方法成功地工作了。在搜索整个搜索区域之前，本算法永远不会停止。一系列实验试验证明了本算法对复杂优化测试函数的鲁棒性和有效性。
In this paper we propose a Particle Swarm Optimization algorithm combined with Novelty Search. Novelty Search finds novel place to search in the search domain and then Particle Swarm Optimization rigorously searches that area for global optimum solution. This method is never blocked in local optima because it is controlled by Novelty Search which is objective free. For those functions where there are many more local optima and second global optimum is far from true optimum, the present method works successfully. The present algorithm never stops until it searches entire search area. A series of experimental trials prove the robustness and effectiveness of the present algorithm on complex optimization test functions.
