Distributed agent based cooperative differential evolution: A master-slave model

Abstract

The paper proposes a distributed computing framework that integrates parallel differential evolution (DE) and multi-agents. Given a complex high-dimensional optimization problem, our approach decomposes the problem into a set of subcomponents, which are evolved by a set of Slave agents concurrently, and the results are synthesized and further evolved by a Master agent. As top-level agents of the framework, the Master and Slave agents can be divided into asynchronous teams of sub-agents including Constructors for solution initialization, Improvers for solution evolution, Repairers for constraint handling, Destroyers for keeping the quality and size of the population, etc., which share populations of solution vectors and cooperate to solve the problem efficiently. The proposed approach is highly parallelized, flexible, and scalable, and its efficiency is demonstrated by comparison with some state-of-the-art approaches.