Many-objective control optimization of high-rise building structures using replicator dynamics and neural dynamics model

Recently the authors presented a single-agent Centralized Replicator Controller (CRC) and a decentralized Multi-Agent Replicator Controller (MARC) for vibration control of high-rise building structures. It was shown that the use of agents and a decentralized approach enhances the vibration control system. Two key parameters in the proposed control methodologies using replicator dynamics are the total population (total available resources or the sum of actuators forces) and the growth rate. In the previous research, a sensitivity analysis was performed to determine the appropriate values for the population size and growth rate. In this paper, the aforementioned control methodologies are integrated with a multi-objective optimization algorithm in order to find Pareto optimal values for growth rates with the goal of achieving maximum structural performance with minimum energy consumption. A modified neural dynamic model of Adeli and Park is used in this research to solve the many-objective optimization problem where the Normal Boundary Intersection method is employed to find Pareto optimality. Sample results are presented using a 20-story steel benchmark structure subjected to historical and artificial accelerograms.