A novel optimization method based on opinion formation in complex networks

In this paper we introduce a novel population-based binary optimization technique, which works based on consensus of interacting multi-agent systems. The agents, each associated with an opinion vector, are connected through a network. They can influence each other, and thus their opinions can be updated. The agents work collectively with their neighbors to solve an optimization task. Here we consider a specific opinion update rule and various topologies for the connection network. Our experiments on a number of benchmark non-convex cost functions show that ring topology results in the best performance as compared to others. We also compare the performance of the proposed method with a number of well-known optimizers (genetic algorithms, binary particle swarm optimizer, and binary differential evolution) and show its outperformance over them. The proposed optimizer also shows rather fast convergence to the optimal solution.