| 多目标粒子群算法用于补料分批生化反应器动态多目标优化 |
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| 引用本文： | 贺益君,俞欢军,成飙,陈德钊.多目标粒子群算法用于补料分批生化反应器动态多目标优化[J].化工学报,2007,58(5):1262-1270. |
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| 作者姓名： | 贺益君 俞欢军 成飙 陈德钊 |
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| 作者单位： | 浙江大学化学工程与生物工程学系 |
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| 摘 要： | 多目标优化是过程系统工程的重要课题，通常以加权或约束方式将其转换为单一目标，未能反映多目标间的复杂关系，不利于随时根据需求作出有效的决策。基于群智能的粒子群算法具有全局优化性能，且易于实现。为使其适于多目标优化，应拓展功能，实施改造。以Pareto支配概念评价种群个体的优劣，设计了确定局部最优点和全局最优点的操作。又利用各粒子的局部最优点信息进行速度更新，以加强种群的多样性，避免因早熟而陷于局部最优。还设置了外部优解库，并通过分散度计算，以适当的策略进行更新，使之逐步均匀地逼近于Pareto最优解集。由此构建一种多目标粒子群优化算法（multi-objective particle swarm optimization，MOPSO），并用于补料分批生化反应器的动态多目标优化，取得了满意的结果。可基于所搜得的Pareto最优解集，分析目标间的关系，为合理决策提供有效的支持。经与NSGA-II比较，MOPSO算法具有更为优良的性能。
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| 关 键 词： | 多目标 粒子群算法 均匀逼近 Pareto最优集 补料分批生化反应器 动态优化 |
| 文章编号： | 0438-1157（2007）05-1262-09 |
| 收稿时间： | 2006-7-7 |
| 修稿时间： | 2006-07-072006-12-19 |
Multi-objective particle swarm optimization approach to solution of fed-batch bioreactor dynamic multi-objective optimization |
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| HE Yijun,YU Huanjun,CHENG Biao,CHEN Dezhao.Multi-objective particle swarm optimization approach to solution of fed-batch bioreactor dynamic multi-objective optimization[J].Journal of Chemical Industry and Engineering(China),2007,58(5):1262-1270. |
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| Authors: | HE Yijun YU Huanjun CHENG Biao CHEN Dezhao |
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| Affiliation: | Department of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China |
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| Abstract: | Multi-objective optimization is an important topic of process systems engineering.Through simply converting to a single goal，it often fails to reflect the more complex relationship between goals，and it is not conductive to effective decision-making at any time on demand.Swarm intelligence based particle swarm optimization（PSO）algorithm has good global optimization performance and can be easily implemented.To make it suitable for multi-objective optimization，PSO should be further rebuilt.Firstly，the concept of Pareto dominance was used to evaluate the fitness of particles，and two kinds of operation for determining the local and global optimal point respectively were designed.Secondly，velocity update strategy，utilizing all particles’ local best information，was used to enhance the ability of global convergence.Thirdly，an external archive technique was set up，and through calculating the degree of dispersion，an appropriate update strategy was adopted to uniformly approximate the Pareto optimal solution set step-by-step.Finally，multi-objective particle swarm optimization（MOPSO）was proposed，and it was applied to dynamic multi-objective optimization of fed-batch bioreactor，the satisfactory solution was obtained.According to the obtained pareto optimal solution set，the relationship between goals could be analyzed further，which could contribute to rational and effective decision-making.Compared with NSGA-Ⅱ，MOPSO showed better performance. |
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| Keywords: | multi-objective particle swarm optimization uniform approximation Pareto optimal solution set fed-batch bioreactor dynamic optimization |
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