Cooperative Optimization Algorithm for the 100-Digit Challenge

Geng Zhang, Yuhui Shi, Jun Steed Huang

Abstract: As for optimization algorithms, the local optima of multimodal problems always bring difficulties of finding global optimum. This paper proposes a cooperative optimization method that combines differential evolution (DE), univariate sampling, harmony search (HS) and particle swarm optimizer (PSO) to deal with the 100-Digit Challenge of finding global optimum of multimodal problems. The basic strategy is to use some effective algorithms such as DE and univariate sampling to obtain several local optimal solutions, and then to explore a promising search space represented by these local solutions to try to find the global optimum. Experimental tests demonstrate the effectiveness of the proposed cooperative method.