City group refers to a collection of cities. Through the development and growth, and these cities form a chain of metropolitan areas. In a city group, cities are divided into central cities and subordinate cities. Generally, central cities have greater chances to develop. However, subordinate cities may not have great chances to develop unless they are adjacent to central cities. Thus, a city is more likely to develop well if it is near a central city. In the process, the spatial distribution of cities changes all the time. Urbanologists call the above phenomena as the evolution of city groups. In this paper, the city group optimization algorithm is presented, which is based on urbanology and mimics the evolution of city groups. The robustness and evolutionary process of the proposed city group optimization algorithm are validated by testing it on 15 benchmark functions. The comparative results show that the proposed algorithm is effective for solving complexly continuous problems due to a stronger ability to escape from local optima.
['Yijun Yang', 'Haibin Duan']
City Group Optimization: An Optimizer for Continuous Problems