A Parallel Implementation of Multiobjective Particle Swarm Optimization Algorithm Based on Decomposition

Multiobjective particle swarm optimization based on decomposition (MOPSO/D) is an effective algorithm for multiobjective optimization problems (MOPs). This paper proposes a parallel version of MOPSO/D algorithm using both message passing interface (MPI) and OpenMP, which is abbreviated as MO-MOPSO/D. It adopts an island model and divides the whole population into several subspecies. Based on the hybrid of distributed and shared-memory programming models, the proposed algorithm can fully use the processing power of today's multicore processors and even a cluster. The experimental results show that MO-MOPSO/D can achieve speedups of 2× on a personal computer equipped with a dual-core four-thread CPU. In terms of the quality of solutions, it can perform similarly to the serial MOPSO/D but greatly outperform NSGA-II. An additional experiment is done on a cluster, and the results show the speedup is not obvious for small-scale MOPs and it is more suitable for solving highly complex problems.

[1]  E. Hughes.  Multiple single objective Pareto sampling , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[2]  Tong Heng Lee,et al.  Multiobjective Evolutionary Algorithms and Applications , 2005, Advanced Information and Knowledge Processing.

[3]  You Zhou,et al.  GPU-Based Parallel Multi-objective Particle Swarm Optimization , 2011 .

[4]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[5]  Alan D. George,et al.  A Parallel Particle Swarm Optimizer , 2003 .

[6]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[7]  Kusum Deep,et al.  Modified parallel particle swarm optimization for global optimization using Message Passing Interface , 2010, 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA).

[8]  Andrzej Jaszkiewicz,et al.  On the performance of multiple-objective genetic local search on the 0/1 knapsack problem - a comparative experiment , 2002, IEEE Trans. Evol. Comput..

[9]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[10]  Thomas Stützle,et al.  A Two-Phase Local Search for the Biobjective Traveling Salesman Problem , 2003, EMO.

[11]  Vincent Roberge,et al.  Comparison of Parallel Particle Swarm Optimizers for Graphical Processing Units and Multicore Processors , 2013, Int. J. Comput. Intell. Appl..

[12]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[13]  Weichung Wang,et al.  Accelerating parallel particle swarm optimization via GPU , 2012, Optim. Methods Softw..

[14]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[15]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[16]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[17]  Bernhard Sendhoff,et al.  Adapting Weighted Aggregation for Multiobjective Evolution Strategies , 2001, EMO.

[18]  S. N. Omkar,et al.  MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures , 2012, Eng. Appl. Artif. Intell..

[19]  Kuo-Yang Tu,et al.  Parallel computation models of particle swarm optimization implemented by multiple threads , 2011, Expert Syst. Appl..

[20]  Tong Heng Lee,et al.  Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing) , 2005 .