一种元启发式禁忌搜索优化算法:在化学和环境过程中的应用

C. Venkateswarlu
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引用次数: 5

摘要

随机优化方法越来越多地用于传统技术难以解决的过程优化。这些方法被广泛应用于具有严重非线性的高维过程的优化。这类方法包括遗传算法(GA)、模拟退火(SA)、差分进化(DE)、蚁群优化(ACO)、禁忌搜索(TS)、粒子群优化(PSO)、人工蜂群(ABC)算法和布谷鸟搜索(CS)算法。在这些方法中,禁忌搜索(TS)是一种潜在的工具,用于从有限的解集中找到可行的最优解。TS中使用的内存将记住当前的最佳解决方案,它还使TS能够在指导搜索移动的同时跟踪最后的解决方案。TS的记忆能力和策略适应特性使其既能利用好的解,又能在搜索空间中寻找新的可行区域。TS已经成功地应用于解决不同学科的广泛的优化问题。本章详细介绍了TS算法及其在化学和环境过程中的应用,特别是共聚反应器的动态优化和生物膜反应器的逆建模。在共聚反应器的动态优化中,设计了启发式禁忌搜索算法,并将其应用于确定苯乙烯-丙烯腈(SAN)共聚反应器的最优控制策略。在生物膜反应器的反建模中,利用固定床厌氧生物膜反应器处理制药工业废水的实测数据,通过对数学模型的验证,设计并应用禁忌搜索来确定动力学模型和膜厚模型的参数。对于这两种情况,都采用禁忌搜索进行优化,适当地制定所需的目标函数,并通过使用实浮点数编码变量和参数来解决问题。结果说明了TS在聚合反应器优化控制和生物膜反应器反建模中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Metaheuristic Tabu Search Optimization Algorithm: Applications to Chemical and Environmental Processes
Stochastic optimization methods are increasingly used for optimizing processes that are difficult to solve by conventional techniques. These methods are widely employed to optimize the processes which have higher dimensionality with severe nonlinearities. Different methods of this kind include the genetic algorithm (GA), simulated annealing (SA), differential evolution (DE), ant colony optimization (ACO), tabu search (TS), particle swarm optimization (PSO), artificial bee colony (ABC) algorithm, and cuckoo search (CS) algorithm. Among these methods, tabu search (TS) is a potential tool used to find a feasible optimal solution from a finite set of solutions. The memory used in TS will remember the current best solution and it also enables the TS to track the last solutions while guiding the search moves. The capability of memory and strategic adaptation features of TS enable it to make use of good solutions and also search for new feasible regions in the search space. TS has been successfully applied to solve a wide spectrum of optimization problems in different disciplines. This chapter describes the TS algorithm in detail and its applications to chemical and environmental processes, specifically, dynamic optimization of a copolymerization reactor and inverse modeling of a biofilm reactor. In dynamic optimization of copolymerization reactor, the meta heuristic Tabu search (TS) is designed and applied to determine the optimal control policies of a styrene–acrylonitrile (SAN) copolymerization reactor. In inverse modeling of biofilm reactor, the tabu search is designed and applied to determine the parameters of kinetic and film thickness models as consequence of the validation of the mathematical models of the process with the aid of measured data acquired from an experimental fixed bed anaerobic biofilm reactor used in the treatment of pharmaceutical industry wastewater. For both the cases, optimization by Tabu search is carried out by suitably formulating the desired objective functions and the problems are solved by encoding the variables and parameters using real floating point numbers. The results explain the efficacy of TS for optimal control of polymerization reactor and inverse modeling of biofilm reactor.
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