Community optimization: Function optimization by a simulated web community

In recent years a number of web-technology supported communities of humans have been developed. Such a web community is able to let emerge a collective intelligence with a higher performance in solving problems than the single members of the community. Based on the successes of collective intelligence systems like Wikipedia, the web encyclopedia, the question arises, whether such a collaborative web community could also be capable of function optimization. This paper introduces an optimization algorithm called Community Optimization (CO), which optimizes a function by simulating a collaborative web community, which edits or improves an article-base, or, more general, a knowledge-base. In order to realize this, CO implements a behavioral model derived from the human behavior that can be observed within certain types of web communities (e.g., Wikipedia or open source communities). The introduced CO method is applied to four well-known benchmark problems. CO significantly outperformed the Fully Informed Particle Swarm Optimization as well as two Differential Evolution approaches in all four cases especially in higher dimensions.