Background: Mathematical optimization aims to make a system or design as effective or functional as possible, computing the quality of the different alternatives using a mathematical model. Most models in systems biol...
详细信息
Background: Mathematical optimization aims to make a system or design as effective or functional as possible, computing the quality of the different alternatives using a mathematical model. Most models in systems biology have a dynamic nature, usually described by sets of differential equations. dynamicoptimization addresses this class of systems, seeking the computation of the optimal time-varying conditions (control variables) to minimize or maximize a certain performance index. dynamicoptimization can solve many important problems in systems biology, including optimal control for obtaining a desired biological performance, the analysis of network designs and computer aided design of biological units. Results: Here, we present a software toolbox, DOTcvpSB, which uses a rich ensemble of state-of-the-art numerical methods for solving continuous and mixed-integer dynamicoptimization (MIDO) problems. The toolbox has been written in MATLAB and provides an easy and user friendly environment, including a graphical user interface, while ensuring a good numerical performance. problems are easily stated thanks to the compact input definition. The toolbox also offers the possibility of importing SBML models, thus enabling it as a powerful optimization companion to modelling packages in systems biology. It serves as a means of handling generic black-box models as well. Conclusion: Here we illustrate the capabilities and performance of DOTcvpSB by solving several challenging optimizationproblems related with bioreactor optimization, optimal drug infusion to a patient and the minimization of intracellular oscillations. The results illustrate how the suite of solvers available allows the efficient solution of a wide class of dynamic optimization problems, including challenging multimodal ones. The toolbox is freely available for academic use.
A novel fault diagnosis procedu r e is proposed in this paper to estimate faults using a linear parameter varying (LPV) model whose scheduling parameters depend on the fault. A wrong determination of the operating con...
详细信息
A novel fault diagnosis procedu r e is proposed in this paper to estimate faults using a linear parameter varying (LPV) model whose scheduling parameters depend on the fault. A wrong determination of the operating conditions could lead the system to an undesired performance or even to an unstable situation, when classical fault diagnosis approaches are applied. This paper addresses this issue by formulating fault diagnosis as a dynamic optimization problem, solved by using a novel hybrid technique that combines a Luenberger-based observer with artificial intelligent (AI) optimization-based algorithms. The observer supervises the health of the system, while AI-based algorithms are able to reconstruct the faulty signal in real-time when the observer determines that the system is under a fault. The efficiency of the proposed fault diagnosis scheme, the three AI-based algorithms based on artificial bee colony and particle swarm optimization, and the gradient-based algorithm developed in this paper, are assessed using a numerical example.
The defects of a slab, such as shrinkage and crack, are related to non-uniform cooling in secondary cooling zone (SCZ) of continuous casting, hence setting the value of water flow rate in the SCZ plays a vital role fo...
详细信息
The defects of a slab, such as shrinkage and crack, are related to non-uniform cooling in secondary cooling zone (SCZ) of continuous casting, hence setting the value of water flow rate in the SCZ plays a vital role for the quality of slab. The traditional scheme can lead to the great fluctuation of temperature when the casting speed changes. Therefore, based on 3-dimensional transient nonlinear convective heat transfer equation of a slab, this paper develops a dynamicoptimization method for setting the value of water flow rate. However, it is difficult to realize this method due to the following two causes. 1. It costs too much time to calculate the 3-dimensional transient nonlinear convective heat transfer equation. 2. It is not easy to select a suitable step size for the optimization algorithm. So this paper presents a modified adaptive step size quasi-Newton parallel iterative algorithm, which can not only reduce the computing time but also select a suitable step size. Actual data in a steel plant are used to demonstrate its validity, and the results clearly show that this scheme can greatly reduce the running time and give a more stable temperature distribution.
Quantification of the performance of algorithms that solve dynamic optimization problems (DOPs) is challenging, since the fitness landscape changes over time. Popular performance measures for DOPs do not adequately ac...
详细信息
Quantification of the performance of algorithms that solve dynamic optimization problems (DOPs) is challenging, since the fitness landscape changes over time. Popular performance measures for DOPs do not adequately account for ongoing fitness landscape scale changes, and often yield a confounded view of performance. Similarly, most popular measures do not allow for fair performance comparisons across multiple instances of the same problem type nor across different types of problems, since performance values are not normalized. Many measures also assume normally distributed input data values, while in reality the necessary conditions for data normality are often not satisfied. The majority of measures also fail to capture the notion of performance variance over time. This paper proposes a new performance measure for DOPs, namely the relative error distance. The measure shows how close to optimal an algorithm performs by considering the multi-dimensional distance between the vector comprising the normalized performance scores for specific algorithm iterations of interest, and the theoretical point of best possible performance. The new measure does not assume normally distributed performance data across fitness landscape changes, is resilient against fitness landscape scale changes, better incorporates performance variance across fitness landscape changes into a single scalar value, and allows easier algorithm comparisons using established nonparametric statistical methods.
Artificial immune systems (AIS), as nature-inspired algorithms, have been developed to solve various types of problems, ranging from machine learning to optimization. This paper proposes a novel hybrid model of AIS th...
详细信息
Artificial immune systems (AIS), as nature-inspired algorithms, have been developed to solve various types of problems, ranging from machine learning to optimization. This paper proposes a novel hybrid model of AIS that incorporates cellular automata (CA), known as the cellular automata-based artificial immune system (CaAIS), specifically designed for dynamic optimization problems where the environment changes over time. In the proposed model, antibodies, representing nominal solutions, are distributed across a cellular grid that corresponds to the search space. These antibodies generate hyper-mutation clones at different times by interacting with neighboring cells in parallel, thereby producing different solutions. Through local interactions between neighboring cells, near-best parameters and near-optimal solutions are propagated throughout the search space. Iteratively, in each cell and in parallel, the most effective antibodies are retained as memory. In contrast, weak antibodies are removed and replaced with new antibodies until stopping criteria are met. The CaAIS combines cellular automata computational power with AIS optimization capability. To evaluate the CaAIS performance, several experiments have been conducted on the Moving Peaks Benchmark. These experiments consider different configurations such as neighborhood size and re-randomization of antibodies. The simulation results statistically demonstrate the superiority of the CaAIS over other artificial immune system algorithms in most cases, particularly in dynamic environments.
This paper focuses on the effect of population diversity to environment identification-based memory scheme (EI-MMS) which heuristically compensates population diversity through the storage and retrieving process of hi...
详细信息
This paper focuses on the effect of population diversity to environment identification-based memory scheme (EI-MMS) which heuristically compensates population diversity through the storage and retrieving process of historic *** introduced several diversity compensation measures and combined them with EI-MMS based univariate marginal distribution algorithm(UMDA) from two ***,a basic diversity compensation measure was used to fight against the inherent diversity loss of ***,two environment-triggered compensation measures were added in the sense of dynamic *** on the experimental results on three dynamic test problems,the dynamics of population diversity of the corresponding EI-MMS based UMDAs were analyzed and several conclusions about how does the population diversity affect the performance of the algorithm in dynamic environments were drawn.
This paper introduces a benchmark dataset to the research article entitled "Ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue - a case study of dynamicoptimization pro...
详细信息
This paper introduces a benchmark dataset to the research article entitled "Ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue - a case study of dynamic optimization problems", by Zhang et al. [7] . Rescue ensemble that consists of rescue simulator and rescue algorithm is characterized by supporting the dynamic simulation of forest fire rescue. The purpose of rescue algorithm is to minimize the longest flight time of aircraft group II and the newly-increased burnt forest cost in one period, simultaneously. The map information in our dataset is from Google map and relevant parameters are also from the actual situation data. The benchmark contains 10 different maps that researchers can use to evaluate their own algorithms and compare their performance with our algorithm. (C) 2020 The Author(s). Published by Elsevier Inc.
暂无评论