Application of Multi-Core Parallel Ant Colony Optimization in Target Assignment Problem
Ant colony optimization(ACO) provides an effective way to solve combinatorial optimization problem. However, with the complexity of the problem increasing, the ACO algorithm needs considerable computational time and resources to improve the good quality of solution, and this rarely satisfies the requirement of real-time computing in M&S (Modeling and Simulation) area. Parallel implementation of ACO can reduce the computational time obviously for the large scale combinatorial optimization problem, and much of the previous work in this field focuses on parallel implementation using MPI which is executed on clusters. Meanwhile, great emphasis is placed on multi-core computing technology with the development of multi-processor architecture and multi-core architecture. A new parallel ant colony optimization (PACO) algorithm is proposed, which applies two kinds of typical multi-core computing technologies, the well-known OpenMP and the recently introduced TBB (Threading Building Blocks) library by Intel Corporation, to solve target assignment problemfTAP). Effectiveness and efficiency of proposed algorithm is validated by studying the convergence speed, problem size scalability and thread size scalability of it
multi-core parallel programming ant colony optimization target assignment problem
Gao Dongdong Gong Guanghong Han Liang Li Ni
School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 1000191, P.R.China
国际会议
太原
英文
514-518
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）