Abstract: To improve the performance of Group Search Optimizer (GSO), a new group search optimizer algorithm based on Chaotic Group Search Optimizer (CGSO) in combination with the global searching characteristic of the chaos method was proposed in the paper. In the method, the good position of producer was updated by chaotic searching, the new position of scrounger was determined by the position of producer and the best position which it had been achieved so far, and the new position of rangers was achieved by chaotic mutation. The global convergent performance of GSO was improved by using the initial sensitivity of the Logistic map to expand the scope of the search and by employing the global ergodicity to search the positions. Four function optimization problems were simulated by CGSO and GSO. The experimental results indicate that CGSO is more effective than the others.

Key words: Logistic map, chaos optimization, Group Search Optimizer (GSO), Chaos Group Search Optimizer (CGSO)

摘要： 为提高组搜索优化(GSO)算法的性能，结合混沌方法的全局搜索特性，提出一种新的基于混沌搜索的组搜索优化（CGSO）算法。此方法中，生产者利用混沌搜索方法不断寻找较好的位置；占领者结合当前生产者的位置和自己运动到目前为止的最好位置对自己当前的位置进行更新；徘徊者采用混沌变异方法探索新的位置。该算法运用Logistic映射的初值敏感性扩大搜索范围，利用其全局遍历性进行位置搜索，有效地提高了算法的全局收敛性。采用CGSO、GSO算法对四个典型的函数优化问题进行了仿真实验，仿真结果验证了方法的有效性。

关键词: Logistic映射, 混沌优化, 组搜索优化, 混沌组搜索优化