Parallel Global Optimization Algorithm Research Articles

On Using the Decision Trees to Identify the Local Extrema in Parallel Global Optimization Algorithm

On Using the Decision Trees to Identify the Local Extrema in Parallel Global Optimization Algorithm

Dynamic Machine Learning Global Optimization Algorithm and Its Application to Aerodynamics

A dynamic extreme gradient boosting (XGBoost) and MaxLIPO trust region parallel global optimization algorithm is proposed in this paper, and it is applied to the turbomachinery blade aerodynamic optimization coupled with an in-house graphics processing unit (GPU) heterogeneous accelerated compressible flow solver, AeroWhale. The algorithm combines an accurate machine learning regression model, an efficient nongradient optimization method with no hyperparameters, a dynamic update regression strategy, and double convergence criteria to achieve high optimization accuracy and efficiency. The optimization results on the test function indicate that the number of objective function calls is less than 2% of that required by a traditional genetic algorithm, which greatly reduces the optimization time. The dynamic XGBoost model ensures that the regression model accuracy near the optimum is relatively high, which is attributed to the update strategy. The error between the optimal value identified by the proposed algorithm and the theoretical value is only 0.52% after several objective function calls. Finally, the aerodynamic optimization algorithm is applied to the LS89 high-pressure turbine, and the total pressure loss is reduced by 13.16%. The sensitivity of each optimization feature to the objective function is determined, showing that the blade suction surface control point near the trailing edge has the greatest impact on aerodynamic performance.

Robust optimization of SVM hyper-parameters for spillway type selection

Spillways, which play a vital role in dams, can be built in various types. Although several studies have been conducted on hydraulic calculations of spillways, studies on type selection that require heuristics knowledge were limited. The tuning of the hyperparameters in machine learning algorithms is still an open problem. In this paper, a parallel global optimization algorithm is proposed optimizing the hyperparameters of a Support Vector Machine (SVM) classification model for providing accurate spillway type selection (STS). The random forest method is used to obtain the relative importance of input variables. Besides, a novel spillway dataset was introduced and a novel STS software tool has been developed based on different machine learning algorithms. Several experiments are carried out to demonstrate the effectiveness of the proposed tool and the reliability of data. The hyper-parameters optimized SVM was achieved the best results with 93.81% classification accuracy.

Asynchronous Parallel Surrogate Optimization Algorithm for Quantitative Strategy Parameter Tuning

Surrogate-model based optimization algorithms can be applied to solve expensive black-box function optimization problem. With the introduction of ensemble model, surrogate-model based algorithms can be automatically adjusted to adapt to various specific problems with different parameter spaces and no need for manual design of surrogate model. However, introduction of ensemble model significantly increases the computational load of surrogate-model based algorithms for training and updating of ensemble model. In this article, parallel computing technology is utilized to speed up the weight updating related computation for the ensemble surrogate model built by Dempster-Shafer theory, and a novel parallel sampling mechanism based on stochastic response surface method is developed to implement asynchronous parameter optimization, based on witch an asynchronous parallel global optimization algorithm is proposed. Furthermore, the parallel algorithm proposed is applied to quantitative trading strategy tuning in financial market and shows both feasibility and effectiveness in actual application. Experiments demonstrates that, the algorithms can achieve high speedup ratio and scalability with no degradation of optimization performance.

Robust optimal control problem with multiple characteristic time points in the objective for a batch nonlinear time-varying process using parallel global optimization

In this paper, we consider a nonlinear time-varying dynamical (NTVD) system with uncertain system parameters assigned to their nominal values in batch culture of glycerol bioconversion to 1,3-propanediol induced by Klebsiella pneumoniae. Some important properties of the NTVD system are discussed. Our goal is to choose a time-varying function for the NTVD system. Thus, an optimal control problem (OCP) governed by the NTVD system and subject to continuous state inequality constraints arising from engineering specifications is proposed, where the time-varying function is the control function to be chosen such that system cost (the relative error between experimental data and the simulated output of the system) and system robustness (robustness of the system with respect to uncertain system parameters) is optimized. Based on the actual fermentation process, the time-varying function is specified by a four-piecewise linear function with unknown kinetic parameters and switching instants. The resulting OCP is approximated as a sequence of nonlinear mathematical programming subproblems by the time-scaling transformation, the constraint transcription and the locally smoothing approximation techniques. A parallel global optimization algorithm, based on a novel combination of limited information particle swarm optimization and local search strategy, is then developed to solve these subproblems. Numerical results show the effectiveness and applicability of our proposed algorithm.

Properties and numerical testing of a parallel global optimization algorithm

In the framework of multistart and local search algorithms that find the global minimum of a real function f(x), $ x \in S\subseteq R^n$ , Gaviano et alias proposed a rule for deciding, as soon as a local minimum has been found, whether to perform or not a new local minimization. That rule was designed to minimize the average local computational cost $eval_1(\cdotp)$ required to move from the current local minimum to a new one. In this paper the expression of the cost $eval_2(\cdotp)$ of the entire process of getting a global minimum is found and investigated; it is shown that $eval_2(\cdotp)$ has among its components $eval_1(\cdotp)$ and can be only monotonically increasing or decreasing; that is, it exhibits the same property of $eval_1(\cdotp)$ . Moreover, a counterexample is given that shows that the optimal values given by $eval_1(\cdotp)$ and $eval_2(\cdotp)$ might not agree. Further, computational experiments of a parallel algorithm that uses the above rule are carried out in a MatLab environment.

On the impact of the migration topology on the Island Model

Parallel Global Optimization Algorithms (PGOA) provide an efficient way of dealing with hard optimization problems. One method of parallelization of GOAs that is frequently applied and commonly found in the contemporary literature is the so-called Island Model (IM). In this paper, we analyze the impact of the migration topology on the performance of a PGOA which uses the Island Model. In particular we consider parallel Differential Evolution and Simulated Annealing with Adaptive Neighborhood and draw first conclusions that emerge from the conducted experiments.

A parallel simulated annealing algorithm for the design of magnetic structures

A new parallel global optimization algorithm is presented and evaluated. It is based on simulated annealing and on speculative computation. It is particularly suited for parallel architectures from some units to some tens of processors and for problems characterized by a cost function whose evaluation takes a long time, features that fit well with the design of magnetic structures. Comparative tests with respect to traditional sequential SA is presented on the design of a magnet for Magnetic Resonance Imaging. >

A parallel algorithm for global optimization

A parallel global optimization algorithm based on the density clustering technique by Boendeb is described. Two versions, the synchronous and the asynchronous one, are proposed and numerical experiments are carried out using a simulation environment of a parallel MIMD machine. A comparison between the sequential and parallel versions of the algorithm, both in term of time and of number of function evaluations, shows that the asynchronous one is very promising for machines based on MIMD architecture

Mathematical modeling of a parallel global optimization algorithm

We describe the formation of a mathematical model of a reasonably complex parallel global optimization program, and the use of this model to assist in the development and understanding of the underlying parallel algorithm. First we discuss the formation of a model that accurately matched execution times of the parallel program on an Intel hypercube. Then we discuss the use of this model to simulate the behavior of our parallel algorithm in a variety of new situations, in order to detect weaknesses in the parallel algorithm and analyze possible improvements to it. We believe that this combination of parallel computer implementation and mathematical modeling is a useful approach in parallel algorithm development.

A parallel global optimization algorithm for rational separable-factorable functions

This paper concludes that we can separately globally minimize every component of a rational separable-factorable function instead of the globally minimizing the whole objective function. This will largely reduce the amount of computation, not only by reducing the size of every subproblem, but also by reducing the number of local minimizers which might be found during the global minimization process. Furthermore, parallel processors can be used in this case to shorten the computing time greatly.

A parallel global optimization algorithm inspired by quantum physics

A parallel global optimization algorithm inspired by quantum physics