An Efficient Algorithm of Discrete Particle Swarm Optimization for Multi-Objective Task Assignment
In this paper, a discrete particle swarm optimization method is proposed to solve the multi-objective task assignment problem in distributed environment. The objectives of optimization include the makespan for task execution and the budget caused by resource occupation. A two-stage approach is desig...
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| Veröffentlicht in: | IEICE Transactions on Information and Systems 2016/12/01, Vol.E99.D(12), pp.2968-2977 |
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| container_title | IEICE Transactions on Information and Systems |
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| creator | QIAO, Nannan YOU, Jiali SHENG, Yiqiang WANG, Jinlin DENG, Haojiang |
| description | In this paper, a discrete particle swarm optimization method is proposed to solve the multi-objective task assignment problem in distributed environment. The objectives of optimization include the makespan for task execution and the budget caused by resource occupation. A two-stage approach is designed as follows. In the first stage, several artificial particles are added into the initialized swarm to guide the search direction. In the second stage, we redefine the operators of the discrete PSO to implement addition, subtraction and multiplication. Besides, a fuzzy-cost-based elite selection is used to improve the computational efficiency. Evaluation shows that the proposed algorithm achieves Pareto improvement in comparison to the state-of-the-art algorithms. |
| doi_str_mv | 10.1587/transinf.2016PAP0032 |
| format | Article |
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Inf. & Syst.</addtitle><date>2016</date><risdate>2016</risdate><volume>E99.D</volume><issue>12</issue><spage>2968</spage><epage>2977</epage><pages>2968-2977</pages><issn>0916-8532</issn><eissn>1745-1361</eissn><abstract>In this paper, a discrete particle swarm optimization method is proposed to solve the multi-objective task assignment problem in distributed environment. The objectives of optimization include the makespan for task execution and the budget caused by resource occupation. A two-stage approach is designed as follows. In the first stage, several artificial particles are added into the initialized swarm to guide the search direction. In the second stage, we redefine the operators of the discrete PSO to implement addition, subtraction and multiplication. Besides, a fuzzy-cost-based elite selection is used to improve the computational efficiency. 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| subjects | Algorithms Computational efficiency discrete particle swarm optimization multi-objective Multiple objective analysis Occupation Operations research Optimization Swarm intelligence task assignment Tasks two-stage method |
| title | An Efficient Algorithm of Discrete Particle Swarm Optimization for Multi-Objective Task Assignment |
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