A Classification-Based Surrogate-Assisted Multiobjective Evolutionary Algorithm for Production Optimization under Geological Uncertainty

SPE Journal ◽  
2020 ◽  
Vol 25 (05) ◽  
pp. 2450-2469
Author(s):  
Mengjie Zhao ◽  
Kai Zhang ◽  
Guodong Chen ◽  
Xinggang Zhao ◽  
Jun Yao ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Net Present Value ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Production Optimization ◽  
Benchmark Problems ◽  
Geological Uncertainty ◽  
Rbf Network ◽  
Multiobjective Evolutionary Algorithm ◽  
Computationally Expensive

Summary Multiobjective optimization (MOO) is a popular procedure for waterflooding optimization under geological uncertainty that maximizes the expectation of net present value (NPV) over all possible uncertainty models and minimizes the variance simultaneously. However, the optimization process involves a large number of decision variables, and the problem is computationally expensive. Surrogate-assisted evolutionary algorithms (SAEAs), which have proved to be an effective way to solve expensive problems, design computationally inexpensive functions to approximate each objective function. On the basis of characterization, we have designed an efficient multiobjective evolutionary algorithm (MOEA) to effectively deal with computationally expensive simulation-based optimization problems. The uniqueness of this algorithm is that it incorporates a Pareto-rank-learning scheme with surrogate-assisted infill criterion. The first is to introduce a multiclass error-correcting output codes (ECOC) model that directly predicts the dominance relationship between candidate solutions and prescreens, and the second is to train a radial-basis function (RBF) network that predicts the objective functions of prescreened solutions to calculate the hypervolume (HV) improvement that maintains convergence and diversity. Compared with typical surrogate-based methods, the developed method provides a classifier first that can enhance the accuracy in high dimensions and reduce computational complexity. To the best of our knowledge, the proposed method compares with state-of-the-art surrogate frameworks for multiobjective production-optimization problems. In this paper, the proposed approach is applied to two 200D benchmark problems and two synthetic reservoir models. The results show that the proposed method can achieve more comprehensive and efficient reservoir management (RM) with a higher convergence speed compared with traditional MOEAs and surrogate-assisted optimization methods.

Global and Local Surrogate-Model-Assisted Differential Evolution for Waterflooding Production Optimization

SPE Journal ◽  
2019 ◽  
Vol 25 (01) ◽  
pp. 105-118 ◽  
Author(s):  
Guodong Chen ◽  
Kai Zhang ◽  
Liming Zhang ◽  
Xiaoming Xue ◽  
Dezhuang Ji ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Surrogate Model ◽  
Net Present Value ◽  
Channel Model ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Production Optimization ◽  
Optimization Process ◽  
Promising Area ◽  
Global And Local

Summary Surrogate models, which have become a popular approach to oil-reservoir production-optimization problems, use a computationally inexpensive approximation function to replace the computationally expensive objective function computed by a numerical simulator. In this paper, a new optimization algorithm called global and local surrogate-model-assisted differential evolution (GLSADE) is introduced for waterflooding production-optimization problems. The proposed method consists of two parts: (1) a global surrogate-model-assisted differential-evolution (DE) part, in which DE is used to generate multiple offspring, and (2) a local surrogate-model-assisted DE part, in which DE is used to search for the optimum of the surrogate. The cooperation between global optimization and local search helps the production-optimization process become more efficient and more effective. Compared with the conventional one-shot surrogate-based approach, the developed method iteratively selects data points to enhance the accuracy of the promising area of the surrogate model, which can substantially improve the optimization process. To the best of our knowledge, the proposed method uses a state-of-the-art surrogate framework for production-optimization problems. The approach is tested on two 100-dimensional benchmark functions, a three-channel model, and the egg model. The results show that the proposed method can achieve higher net present value (NPV) and better convergence speed in comparison with the traditional evolutionary algorithm and other surrogate-assisted optimization methods for production-optimization problems.

A multi-objective evolutionary algorithm based on niche selection in solving irregular Pareto fronts

2021 ◽  
pp. 1-21
Author(s):  
Xin Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Environmental Selection ◽  
Optimal Solution Set ◽  
Pareto Fronts

The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs.

scholarly journals The Pareto Tracer for General Inequality Constrained Multi-Objective Optimization Problems

2020 ◽  
Vol 25 (4) ◽  
pp. 80
Author(s):  
Fernanda Beltrán ◽  
Oliver Cuate ◽  
Oliver Schütze
Keyword(s):  
Optimization Problems ◽  
Continuation Method ◽  
Optimization Methods ◽  
New Method ◽  
Continuation Methods ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Financial Applications ◽  
General Inequality ◽  
Multi Objective

Problems where several incommensurable objectives have to be optimized concurrently arise in many engineering and financial applications. Continuation methods for the treatment of such multi-objective optimization methods (MOPs) are very efficient if all objectives are continuous since in that case one can expect that the solution set forms at least locally a manifold. Recently, the Pareto Tracer (PT) has been proposed, which is such a multi-objective continuation method. While the method works reliably for MOPs with box and equality constraints, no strategy has been proposed yet to adequately treat general inequalities, which we address in this work. We formulate the extension of the PT and present numerical results on some selected benchmark problems. The results indicate that the new method can indeed handle general MOPs, which greatly enhances its applicability.

HYBRID-FITNESS FUNCTION EVOLUTIONARY ALGORITHM BASED ON SIMPLEX CROSSOVER AND PSO MUTATION FOR CONSTRAINED OPTIMIZATION PROBLEMS

2009 ◽  
Vol 23 (01) ◽  
pp. 115-127 ◽  
Author(s):  
YIBO HU
Keyword(s):  
Constrained Optimization ◽  
Evolutionary Algorithm ◽  
Penalty Function ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Fitness Function ◽  
Benchmark Problems ◽  
Crossover Operator ◽  
Hybrid Fitness ◽  
Constrained Optimization Problems

For constrained optimization problems, evolutionary algorithms often utilize a penalty function to deal with constraints, even if it is difficult to control the penalty parameters. To overcome this shortcoming, this paper presents a new penalty function which has no parameter and can effectively handle constraint first, after which a hybrid-fitness function integrating this penalty function into the objective function is designed. The new fitness function can properly evaluate not only feasible solution, but also infeasible one, and distinguish any feasible one from an infeasible one. Meanwhile, a new crossover operator based on simplex crossover operator and a new PSO mutation operator are also proposed, which can produce high quality offspring. Based on these, a new evolutionary algorithm for constrained optimization problems is proposed. The simulations are made on ten widely used benchmark problems, and the results indicate the proposed algorithm is effective.

scholarly journals Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems

2021 ◽  
Author(s):  
Qinghua Gu ◽  
Qian Wang ◽  
Neal N. Xiong ◽  
Song Jiang ◽  
Lu Chen
Keyword(s):  
Evolutionary Algorithm ◽  
Discrete Optimization ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Multi Objective ◽  
Random Forest Models ◽  
Probability Operator ◽  
Expensive Optimization ◽  
Optimization Efficiency ◽  
Discrete Optimization Problems

AbstractSurrogate-assisted optimization has attracted much attention due to its superiority in solving expensive optimization problems. However, relatively little work has been dedicated to addressing expensive constrained multi-objective discrete optimization problems although there are many such problems in the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed in this paper for this kind of problem. Specifically, random forest models are embedded in the framework of the evolutionary algorithm as surrogates to improve approximate accuracy for discrete optimization problems. To enhance the optimization efficiency, an improved stochastic ranking strategy based on the fitness mechanism and adaptive probability operator is presented, which also takes into account both convergence and diversity to advance the quality of candidate solutions. To validate the proposed algorithm, it is comprehensively compared with several well-known optimization algorithms on several benchmark problems. Numerical experiments are demonstrated that the proposed algorithm is very promising for the expensive constrained multi-objective discrete optimization problems.

scholarly journals A Decomposition-Based Multiobjective Evolutionary Algorithm with Adaptive Weight Adjustment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Cai Dai ◽  
Xiujuan Lei
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Uniform Design ◽  
Weight Vector ◽  
Selection Strategy ◽  
Main Role ◽  
Multiobjective Evolutionary Algorithm ◽  
Adjustment Strategy ◽  
Multiobjective Optimization Problems ◽  
Adaptive Weight

Recently, decomposition-based multiobjective evolutionary algorithms have good performances in the field of multiobjective optimization problems (MOPs) and have been paid attention by many scholars. Generally, a MOP is decomposed into a number of subproblems through a set of weight vectors with good uniformly and aggregate functions. The main role of weight vectors is to ensure the diversity and convergence of obtained solutions. However, these algorithms with uniformity of weight vectors cannot obtain a set of solutions with good diversity on some MOPs with complex Pareto optimal fronts (PFs) (i.e., PFs with a sharp peak or low tail or discontinuous PFs). To deal with this problem, an improved decomposition-based multiobjective evolutionary algorithm with adaptive weight adjustment (IMOEA/DA) is proposed. Firstly, a new method based on uniform design and crowding distance is used to generate a set of weight vectors with good uniformly. Secondly, according to the distances of obtained nondominated solutions, an adaptive weight vector adjustment strategy is proposed to redistribute the weight vectors of subobjective spaces. Thirdly, a selection strategy is used to help each subobjective space to obtain a nondominated solution (if have). Comparing with six efficient state-of-the-art algorithms, for example, NSGAII, MOEA/D, MOEA/D-AWA, EMOSA, RVEA, and KnEA on some benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence.

Sign in / Sign up

Export Citation Format

Share Document