A Distributed RBF-Assisted Differential Evolution for Distributed Expensive Constrained Optimization

Abstract

With the development of Internet of things and distributed computing techniques, distributed and expensive constrained optimization problems (DECOPs) have emerged in the industry. DECOPs refer to optimization problems with objective and constraint functions that are computationally expensive and can only be evaluated on multiple agents of distributed networks. In DECOPs, the raw data of each agent cannot be transmitted to other agents, but only objective or constraint value of a solution can be evaluated, resulting in the incomplete data on each agent. This paper proposes a distributed RBF-assisted differential evolution (DRADE) algorithm for solving DECOPs. In DRADE, we added a master agent to the distributed networks of DECOPs, connecting work agents that can evaluate objective or constraint values of candidate solutions to the master agent in a star topology. The proposed algorithm is composed of candidate generation and selection on master agent and radial basis function (RBF) management on work agents. In candidate generation and selection, differential evolution serves as an optimizer to generate candidate solutions assisted by RBF models received from work agents to replace expensive evaluations of candidate solutions in the master agent. In RBF management, each work agent constructs and updates a RBF model with its own data, which are updated by samples selected from candidate solutions received from the master agent and their expensively evaluated values. Statistical results and analysis of experiments carried out on benchmark test functions and engineering problems show that DRADE has superior performance than compared state-of-the-art SAEAs.