Application of artificial intelligence techniques in the optimization of single screw polymer extrusion

As with most real optimization problems, polymer processing technologies can be seen as multi-objective optimization problems. Due to the high computation times required by the numerical modelling routines usually available to calculate the values of the objective function, as a function of the decision variables, it is necessary to develop alternative optimization methodologies able to reduce the number of solutions to be evaluated, when compared with the technics normally employed, such as evolutionary algorithms. Therefore, in this work is proposed the use of artificial intelligence based on a data analysis technique designated by DAMICORE surpasses those limitations. An example from single screw polymer extrusion is used to illustrate the efficient use of a methodology proposed.This research was partially funded by NAWA-Narodowa Agencja Wymiany Akademickiej, under grant PPN/ULM/2020/1/00125 and European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No 734205–H2020-MSCA-RISE-2016. The authors also acknowledge the funding by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT (Portuguese Foundation for Science and Technology) under the projects UIDB/05256/2020, and UID-P/05256/2020, the Center for Mathematical Sciences Applied to Industry (CeMEAI) and the support from the São Paulo Research Foundation (FAPESP grant No 2013/07375-0, the Center for Artificial Intelligence (C4AI-USP), the support from the São Paulo Research Foundation (FAPESP grant No 2019/07665-4) and the IBM Corporation

Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems

Surrogate-assisted evolutionary algorithms have been widely developed to solve complex and computationally expensive multi-objective optimization problems in recent years. However, when dealing with high-dimensional optimization problems, the performance of these surrogate-assisted multi-objective evolutionary algorithms deteriorate drastically. In this work, a novel Classifier-assisted rank-based learning and Local Model based multi-objective Evolutionary Algorithm (CLMEA) is proposed for high-dimensional expensive multi-objective optimization problems. The proposed algorithm consists of three parts: classifier-assisted rank-based learning, hypervolume-based non-dominated search, and local search in the relatively sparse objective space. Specifically, a probabilistic neural network is built as classifier to divide the offspring into a number of ranks. The offspring in different ranks uses rank-based learning strategy to generate more promising and informative candidates for real function evaluations. Then, radial basis function networks are built as surrogates to approximate the objective functions. After searching non-dominated solutions assisted by the surrogate model, the candidates with higher hypervolume improvement are selected for real evaluations. Subsequently, in order to maintain the diversity of solutions, the most uncertain sample point from the non-dominated solutions measured by the crowding distance is selected as the guided parent to further infill in the uncertain region of the front. The experimental results of benchmark problems and a real-world application on geothermal reservoir heat extraction optimization demonstrate that the proposed algorithm shows superior performance compared with the state-of-the-art surrogate-assisted multi-objective evolutionary algorithms. The source code for this work is available at https://github.com/JellyChen7/CLMEA

Surrogate-assisted Cooperative Swarm Optimization of High-dimensional Expensive Problems

Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization algorithm and a surrogate-assisted social learning based particle swarm optimization algorithm cooperatively search for the global optimum. The cooperation between the particle swarm optimization and the social learning based particle swarm optimization consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the social learning based particle swarm optimization focuses on exploration while the particle swarm optimization concentrates on local search. Empirical studies on six 50-dimensional and six 100-dimensional benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget

Surrogate-assisted Cooperative Swarm Optimization of High-dimensional Expensive Problems

Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization algorithm and a surrogate-assisted social learning based particle swarm optimization algorithm cooperatively search for the global optimum. The cooperation between the particle swarm optimization and the social learning based particle swarm optimization consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the social learning based particle swarm optimization focuses on exploration while the particle swarm optimization concentrates on local search. Empirical studies on six 50-dimensional and six 100-dimensional benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget

Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

Sun C, Jin Y, Cheng R, Ding J, Zeng J. Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems. IEEE Transactions on Evolutionary Computation. 2017;21(4):644-660.Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization (PSO) algorithm and a surrogate-assisted social learning-based PSO (SL-PSO) algorithm cooperatively search for the global optimum. The cooperation between the PSO and the SL-PSO consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the SL-PSO focuses on exploration while the PSO concentrates on local search. Empirical studies on six 50-D and six 100-D benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget

Robust optimization of well placement and control : enhancing operational applications and computational efficiency

Field optimization aims to find the field design and management strategy that maximizes economic profit while honoring multiple constraints. This results in a complex optimization problem with a large number of correlated variables of various types (e.g. well locations and control settings) and a computationally expensive and uncertain objective function based on a simulated field model. Two practical limitations of the existing optimization workflows are: 1) the forthcoming field production limits are ignored during at least the early field design optimization stages, and 2) they provide a single optimal solution strategy, whereas later operational problems often add unexpected constraints changing this strategy and reducing this inflexible solution to a sub-optimal scenario in the application. This thesis develops efficient field development and control optimization techniques to provide operationally flexible solutions while considering production constraints and geological uncertainty. The first part of the thesis presents a robust, multi-level framework that considers fluid processing capacity constraints during both well placement and control optimization levels. To accomplish this, an integrated model of the reservoir and production network is simulated to impose the production limit while capturing the flow behavior in the integrated system. A systematic realization selection process, tailored to the objective of the subsequent optimization stage, is proposed to select a small representative ensemble of reservoir model realizations to account for the reservoir description uncertainty during robust optimization. The proposed realization selection technique is shown to outperform the alternative approaches. Various scenarios are investigated to identify the impact of field production constraints on the optimal field development and control strategy. The developed integrated optimization workflow is applied to optimize the infill well placement in a real North Sea field. A robust, multi-solution optimization framework is then developed to offer operational flexibility by providing multiple optimal field development and control solutions. The developed workflow is based on the sequential optimization of well placement and control at multiple levels. An ensemble of close-to-optimum solutions is chosen from each level and transferred to the next level of optimization, and this loop continues until no significant improvement is observed in the (expected) objective value. Fit-for-purpose clustering techniques are developed to systematically select an ensemble of solutions, with maximum differences in decision variables but close-to-optimum objective values, at each optimization level. The developed multi-solution optimization framework requires significantly higher computation time especially when aiming to maximize diversity among well placement solutions. Convolutional Neural Networks (CNNs) are used as surrogate models (SMs) to enhance the computational efficiency by partly substituting the time-consuming reservoir simulation runs. An ensemble of CNNs is employed to enhance the robustness of the surrogate modeling as well as to allow estimation of the SM’s prediction quality for new data points. The ensemble of CNNs is adaptively updated during the optimization process using new data points, to improve its prediction accuracy. The efficiency of the developed optimization frameworks is demonstrated using several benchmark case studies