An Efficient Asynchronous Parallel Evolutionary Algorithm Based on Message Passing Model for Solving Complex Nonlinear Constrained Optimization

Abstract This study presents an asynchronous parallel evolutionary algorithm based on message passing model (MAPEA) for solving complex function optimization problems with constraints. The MAPEA combines a local search into the global search. The local search is based on Tabu search, and the radius of neighborhood is self-adaptive. The MAPEA is implemented in Parallel Virtual Machine (PVM) programming environment and used to solve two widely applied complex optimization problems. The speedup and parallel efficiency of MAPEA are analyzes and comparisons with other published results are made. Numerical experiments show that MAPEA exhibits good performance and can handle complex constrained optimization problems.