Multi-Objective Bacterial Foraging Optimization Algorithm Based on Parallel Cell Entropy for Aluminum Electrolysis Production Process
Multi-Objective Bacterial Foraging Optimization Algorithm Based on Parallel Cell Entropy for Aluminum Electrolysis Production Process
复制标题
基于并行胞熵的铝电解生产过程多目标细菌觅食优化算法
DOI：
10.1109/tie.2015.2510977
复制
发表时间：
2016-04
影响因子：
7.7
通讯作者：
易军
中科院分区：
文献类型：
--
作者：
易军
由于铝电解生产过程中大量的非线性、强耦合参数难以优化，因此环境友好铝电解生产过程一直是一个具有挑战性的工业问题。本文提出了一种多目标细菌觅食优化（MOBFO）算法，以寻求最大化电流效率、最小化能耗和全氟化碳（PFC）产生量的最优解。我们的方法可以被看作是一个增强版本的细菌觅食优化（BFO）在解决多目标优化（MOO）问题（MOPs）。首先提出了一种面向任务的优化框架和模型，然后在一个新的目标空间--平行胞元坐标系（PCCS）中引入平行胞元熵及其差来评价Pareto解的演化状态.特别是，帕累托存档进化方法（PAEA）和自适应觅食策略（AFS）的应用，以平衡的收敛性和多样性的帕累托前沿的优化过程。与传统方法相比，MOBFO不仅加快了向Pareto前沿的收敛速度，而且提高了解的多样性。大量的基准问题和实际铝电解生产过程的实验结果验证了我们提出的方法的有效性。
Environment-friendly aluminum electrolysis production process has long been a challenging industrial issue due to its built-in difficulty in optimizing numerous highly coupled and nonlinear parameters. This paper presents a multi-objective bacterial foraging optimization (MOBFO) algorithm to find optimal solutions that maximize the current efficiency and minimize the energy consumption and the production of perfluorocarbons (PFCs). Our method can be viewed as an enhanced version of the bacterial foraging optimization (BFO) in solving multi-objective optimization (MOO) problems (MOPs). We first propose a task-oriented optimization framework and model, and then parallel cell entropy and its difference are introduced to evaluate the evolutionary status of the Pareto solutions in a new objective space called parallel cell coordinate system (PCCS). In particular, the Pareto-archived evolution approach (PAEA) and the adaptive foraging strategy (AFS) are applied to balance the convergence and diversity of the Pareto front in the optimization procedure. Compared with traditional approaches, MOBFO not only increases speed of convergence toward the Pareto front, but also improves the diversity of the obtained solutions. Extensive experiment results on numerous benchmark problems and real-world aluminum electrolysis production process validated our proposed method's effectiveness.
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