共查询到10条相似文献,搜索用时 125 毫秒
1.
Optimal genetic manipulations in batch bioreactor control 总被引:2,自引:0,他引:2
Kapil G. Gadkar Author Vitae Author Vitae Francis J. Doyle III Author Vitae 《Automatica》2006,42(10):1723-1733
Advances in metabolic engineering have enabled bioprocess optimization at the genetic level. Large-scale systematic models are now available at a genome level for many biological processes. There is, thus, a motivation to develop advanced control algorithms, using these complex models, to identify optimal performance strategies both at the genetic and bioreactor level. In the present paper, the bilevel optimization framework previously developed by the authors is coupled with control algorithms to determine the genetic manipulation strategies in practical bioprocess applications. The bilevel optimization includes a linear programming problem in the inner level and a nonlinear optimization problem in the outer level. Both gradient-based and stochastic methods are used to solve the nonlinear optimization problem. Ethanol production in an anaerobic batch fermentation of Escherichia coli is considered in case studies that demonstrate optimization of ethanol production, batch time, and multi-batch scheduling. 相似文献
2.
《Information and Software Technology》2001,43(14):817-831
An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and real-valued representations are discussed for parameter optimization problems. Parallel Island models are also reviewed, and the evaluation of evolutionary algorithms is discussed. 相似文献
3.
一种自适应多策略行为粒子群优化算法 总被引:1,自引:0,他引:1
针对粒子群优化算法收敛速度慢、局部搜索能力差等缺点,提出一种自适应多策略行为粒子群优化算法.算法中每个粒子拥有4种行为进化策略,在迭代过程中通过计算每种进化策略的立即价值、未来价值和综合奖励来决定粒子的进化行为,并通过策略行为概率变异算法提升个体寻优速度或避免陷入局部最优解.在经典的基准测试函数上,对新算法与其他7个群智能进化算法的测试结果进行比较分析,结果表明所提出算法具有很好的求解精度和收敛速度,尤其适合应用于一些高维优化问题. 相似文献
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Large scale structural optimization: Computational methods and optimization algorithms 总被引:3,自引:1,他引:2
M. Papadrakakis N. D. Lagaros Y. Tsompanakis V. Plevris 《Archives of Computational Methods in Engineering》2001,8(3):239-301
Summary The objective of this paper is to investigate the efficiency of various optimization methods based on mathematical programming
and evolutionary algorithms for solving structural optimization problems under static and seismic loading conditions. Particular
emphasis is given on modified versions of the basic evolutionary algorithms aiming at improving the performance of the optimization
procedure. Modified versions of both genetic algorithms and evolution strategies combined with mathematical programming methods
to form hybrid methodologies are also tested and compared and proved particularly promising. Furthermore, the structural analysis
phase is replaced by a neural network prediction for the computation of the necessary data required by the evolutionary algorithms.
Advanced domain decomposition techniques particularly tailored for parallel solution of large-scale sensitivity analysis problems
are also implemented. The efficiency of a rigorous approach for treating seismic loading is investigated and compared with
a simplified dynamic analysis adopted by seismic codes in the framework of finding the optimum design of structures with minimum
weight. In this context a number of accelerograms are produced from the elastic design response spectrum of the region. These
accelerograms constitute the multiple loading conditions under which the structures are optimally designed. The numerical
tests presented demonstrate the computational advantages of the discussed methods, which become more pronounced in large-scale
optimization problems. 相似文献
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This paper introduces a coevolutionary method developed for solving constrained optimization problems. This algorithm is based on the evolution of two populations with opposite objectives to solve saddle-point problems. The augmented Lagrangian approach is taken to transform a constrained optimization problem to a zero-sum game with the saddle point solution. The populations of the parameter vector and the multiplier vector approximate the zero-sum game by a static matrix game, in which the fitness of individuals is determined according to the security strategy of each population group. Selection, recombination, and mutation are done by using the evolutionary mechanism of conventional evolutionary algorithms such as evolution strategies, evolutionary programming, and genetic algorithms. Four benchmark problems are solved to demonstrate that the proposed coevolutionary method provides consistent solutions with better numerical accuracy than other evolutionary methods 相似文献
8.
Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization 下载免费PDF全文
Ye Tian Haowen Chen Haiping Ma Xingyi Zhang Kay Chen Tan Yaochu Jin 《IEEE/CAA Journal of Automatica Sinica》2022,9(10):1801-1817
Large-scale multi-objective optimization problems (LSMOPs) pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces. While evolutionary algorithms are good at solving small-scale multi-objective optimization problems, they are criticized for low efficiency in converging to the optimums of LSMOPs. By contrast, mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems, but they have difficulties in finding diverse solutions for LSMOPs. Currently, how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains unexplored. In this paper, a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient method. On the one hand, conjugate gradients and differential evolution are used to update different decision variables of a set of solutions, where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto front. On the other hand, objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions, and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its parent. In comparison with state-of-the-art evolutionary algorithms, mathematical programming methods, and hybrid algorithms, the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs. 相似文献
9.
随机时变背包问题(RTVKP)是一种新的动态背包问题,也是一种新的动态组合优化问题,目前它的求解算法主要是动态规划的精确算法、近似算法和遗传算法.本文首先利用动态规划提出了一个求解RTVKP问题的新精确算法,对算法时间复杂度的比较结果表明:它比已有的精确算法更适于求解背包载重较大的一类RTVKP实例.然后,分别基于差分演化和粒子群优化与贪心修正策略相结合,提出了求解RTVKP问题的两个进化算法.对5个RTVKP实例的数值计算结果比较表明: 精确算法一般不宜求解大规模的RTVKP实例,而基于差分演化、粒子群优化和遗传算法与贪心修正策略相结合的进化算法却不受实例规模与数据大小的影响,对于振荡频率大且具有较大数据的大规模RTVKP实例均能求得的一个极好的近似解. 相似文献
10.
Iterative algorithms for continuous numerical optimization typically need to adapt their step lengths in the course of the search. While some strategies employ fixed schedules, others attempt to adapt dynamically in response to the outcome of trial steps or the history of the search process. Evolutionary algorithms are of the latter kind. A control strategy that is commonly used in evolution strategies is the cumulative step length adaptation approach. This paper presents a theoretical analysis of that adaptation strategy. The analysis includes the practically relevant case of noise interfering in the optimization process. Recommendations are made with respect to choosing appropriate population sizes. 相似文献