Abstract
In this paper, a multi-agent optimization algorithm (MAOA) is proposed for solving the resource-constrained project scheduling problem (RCPSP). In the MAOA, multiple agents work in a grouped environment where each agent represents a feasible solution. The evolution of agents is achieved by using four main elements in the MAOA, including social behavior, autonomous behavior, self-learning, and environment adjustment. The social behavior includes the global one and the local one for performing exploration. Through the global social behavior, the leader agent in every group is guided by the global best leader. Through the local social behavior, each agent is guided by its own leader agent. Through the autonomous behavior, each agent exploits its own neighborhood. Through the self-learning, the best agent performs an intensified search to further exploit the promising region. Meanwhile, some agents perform migration among groups to adjust the environment dynamically for information sharing. The implementation of the MAOA for solving the RCPSP is presented in detail, and the effect of key parameters of the MAOA is investigated based on the Taguchi method of design of experiment. Numerical testing results are provided by using three sets of benchmarking instances. The comparisons to the existing algorithms demonstrate the effectiveness of the proposed MAOA for solving the RCPSP.