Investigating a Socially Inspired Heterogeneous System of Problem Solving Agents

Social interactions have recently been used as an inspiration for novel agent-based problem-solving models. Particle Swarm Optimization and Memetic Networks are two such algorithms. Although they draw inspiration from different real-world social systems, they both rely on the concept of a social network to regulate the internal information flow in a structured way. In this paper, we systematically investigate how a heterogeneous population composed of individuals from these two models behave as the system seeks the solution to the benchmark problems. We report on extensive numerical simulations, showing that this heterogeneous model is able to converge faster in two highly multimodal scenarios while being otherwise statistically equivalent to the original homogeneous models. Our results provide supportive evidence for the hypothesis that higher diversity in populations of problem-solvers can be beneficial and also adds a new dimension to previous heterogeneous problem-solving models.