基于ε占优的自适应多目标粒子群算法
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1. 遵义师范学院数学系,2. 山东师范大学管理与
经济学院3. 深圳大学管理学院

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刘衍民

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Self-adaptive Multi-objective Particle Swarm Optimizer Based on ε-domination
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1. Department of Mathematics,Zunyi Normal College;2. School of Management and
Economics,Shandong Normal University;3. College of Management,Shenzhen University,

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    摘要:

    针对粒子群算法求解多目标问题极易收敛到伪Pareto 前沿(等价于单目标优化问题中的局部最优解), 并且
    收敛速度较慢的问题, 提出一种?? 占优的自适应多目标粒子群算法(??DMOPSO). 在??DMOPSO算法中, 每个粒子的
    邻居根据粒子的运行动态地组建, 且粒子的速度不由其邻居中运行最好的粒子来调整, 而是由其所有邻居共同调整.
    同时, 采用外部存档保存非劣解, 并利用?? 占优更新非劣解. 模拟结果表明了??DMOPSO算法的有效性.

    Abstract:

    Multi-objective particle swarm optimizers(MOPSOs) easily converge to a false Pareto front (the equivalent of a
    local optimum in single objective optimization), and converge slowly when applied to solve multi-objective optimization
    problems(MOPs). Therefore, this paper presents a self-adaptive multiobjective particle swarm optimizer based on ??-
    domination(??DMOPSO) to handle MOPs. In the ??DMOPSO algorithm, the neighborhood of each particle is dynamically
    changed in terms of the performances of the particles, and the velocity of each particle is not adjusted by the best performing
    particle in its neighborhood, but by all particles in its neighborhood including itself. Finally, external archive is employed
    to store the nondominated solutions and ??-dominance is applied to update non-dominated solutions in external archive.
    Simulation results show the effectiveness of the proposed ??DMOPSO algorithm.

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刘衍民, 赵庆祯, 牛奔,等.基于ε占优的自适应多目标粒子群算法[J].控制与决策,2011,26(1):89-95

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历史
  • 收稿日期:2009-10-16
  • 最后修改日期:2010-03-21
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  • 在线发布日期: 2011-01-20
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