Search Strategies in Evolutionary Multi-Agent Systems: The Effect of Cooperation and Reward on Solution Quality

Abstract

Cooperation and reward of strategic agents in an evolutionary optimization framework is explored in order to better solve engineering design problems. Agents in this Evolutionary Multi-Agent Systems (EMAS) framework rely on one another to better their performance, but also vie for the opportunity to reproduce. The level of cooperation and reward is varied by altering the amount of interaction between agents and the fitness function describing their evolution. The effect of each variable is measured using the problem objective function as a metric. Increasing the amount of cooperation in the evolving team is shown to lead to improved performance for several multimodal and complex numerical optimization and three-dimensional layout problems. However, fitness functions that utilize team-based rewards are found to be inferior to those that reward on an individual basis. The performance trends for different fitness functions and levels of cooperation remain when EMAS is applied to the more complex problem of three-dimensional packing as well.