In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks optimization (HHO), the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability. To evaluate the performance of the proposed method, a series of experiments are carried out on various satellite images. Eight advanced thresholding approaches are selected for comparison. Three criteria are adopted to determine the segmentation thresholds, namely Kapur’s entropy, Tsallis entropy, and Otsu between-class variance. Furthermore, four oil pollution images are used to further assess the practicality and feasibility of the proposed method on real engineering problem. The experimental results illustrate that the DHHO/M based thresholding technique is superior to others in the following three aspects: fitness function evaluation, image segmentation effect, and statistical tests.

Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

This communication presents a novel surrogate-assisted quasi-Newton enhanced global optimization (SA-QNEGO) algorithm. In this proposed method, the heuristic hypersphere sampling (HHS) method is used to obtain representative samples. The surrogate model is built based on the low-fidelity model. The quasi-Newton enhanced differential evolution (DE) method is designed to optimize the surrogate model. Finally, the optimal design of a high-fidelity model is obtained through a space mapping procedure. The proposed algorithm is verified through two antenna design examples including a dipole antenna with balun and an SIW cavity-backed slot antenna. The results show that the proposed algorithm finds a more accurate minimum value with less computational time than direct optimization using DE.

Surrogate-Assisted Quasi-Newton Enhanced Global Optimization of Antennas Based on a Heuristic Hypersphere Sampling

A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems

United States Dept of Energy (National Nuclear Security Administration Advanced Simulation and Computing Program Cooperative Agreement under the Predictive Academic Alliance Program DE-NA0002378)

Parallel surrogate-assisted global optimization with expensive functions – a survey

In this study, efficient global optimization (EGO) with a multi-fidelity hybrid surrogate model for multi-objective optimization is proposed to solve multi-objective real-world design problems. In the proposed approach, a design exploration is carried out assisted by surrogate models, which are constructed by adding a local deviation estimated by the kriging method and a global model approximated by a radial basis function. An expected hypervolume improvement is then computed on the basis of the model uncertainty to determine additional samples that could improve the model accuracy. In the investigation, the proposed approach is applied to two-objective and three-objective optimization test functions. Then, it is applied to aerodynamic airfoil design optimization with two objective functions, namely minimization of aerodynamic drag and maximization of airfoil thickness at the trailing edge. Finally, the proposed method is applied to aerodynamic airfoil design optimization with three objective functions, namely minimization of aerodynamic drag at cruising speed, maximization of airfoil thickness at the trialing edge and maximization of lift at low speed assuming a landing attitude. XFOILis used to investigate the low-fidelity aerodynamic force, and a Reynolds-averaged Navier–Stokes simulation is applied for high-fidelity aerodynamics in conjunction with a high-cost approach. For comparison, multi-objective optimization is carried out using a kriging model only with a high-fidelity solver (single fidelity). The design results indicate that the non-dominated solutions of the proposed method achieve greater data diversity than the optimal solutions of the kriging method. Moreover, the proposed method gives a smaller error than the kriging method.

https://www.mdpi.com/2076-3417/7/12/1318/pdf

Multi-Fidelity Multi-Objective Efficient Global Optimization Applied to Airfoil Design Problems

A multi-fidelity optimization technique by an efficient global optimization process using a hybrid surrogate model is investigated for solving real-world design problems The model constructs the local deviation using the kriging method and the global model using a radial basis function The expected improvement is computed to decide additional samples that can improve the model The approach was first investigated by solving mathematical test problems The results were compared with optimization results from an ordinary kriging method and a co-kriging method, and the proposed method produced the best solution The proposed method was also applied to aerodynamic design optimization of helicopter blades to obtain the maximum blade efficiency The optimal shape obtained by the proposed method achieved performance almost equivalent to that obtained using the high-fidelity, evaluation-based single-fidelity optimization Comparing all three methods, the proposed method required the lowest total number 

Hybrid surrogate-model-based multi-fidelity efficient global optimization applied to helicopter blade design

This paper discusses airfoil design optimization using a genetic algorithm (GA) with multi-modal distribution crossover (MMDX). The proposed crossover method creates four segments from four parents, of which two segments are bounded by selected parents and two segments are bounded by one parent and another segment. After these segments are defined, four offsprings are generated. This study applied the proposed optimization to a real-world, multi-objective airfoil design problem using class-shape function transformation parameterization, which is an airfoil representation that uses polynomial function, to investigate the effectiveness of this algorithm. The results are compared with the results of the blend crossover (BLX) and unimodal normal distribution crossover (UNDX) algorithms. The objective of these airfoil design problems is to successfully find the optimal design. The outcome of using this algorithm is superior to that of the BLX and UNDX crossover methods because the proposed method can maintain higher diversity than the BLX and UNDX methods. This advantage is desirable for real-world problems.

Multi-modal distribution crossover method based on two crossing segments bounded by selected parents applied to multi-objective design optimization

Now the class-shape function transformation parameterization method (CST) is a popular method for creating the shape. Disadvantage of the CST method is it hard to design a point close to the design condition and it hard to create the shape for the complicated geometry because it needs to use high order polynomial. This paper deals the multi-segment CST for creating the geometry to reduce the degree of polynomial and controlled the shape at the controlled point. The Genetic Algorithm (GA) was used to test the centerline spacecraft design problem that creates from the multi-segment CST. Four centerline spacecraft design problems were set to search the minimum drag.

Multi-segment Class-Shape-Transformation (CST) for global design exploration

Abstract Conceptual design of a launch vehicle with a hybrid rocket engine (HRE) has been implemented using design informatics approach in order to investigate the feasibility of a single-stage hybrid rocket. Two test design problems were formulated by using two objective functions: maximization of downrange and minimization of initial gross weight, seven design variables which describe geometry and initial conditions, and one constraint relative to target altitude. The optimization result reveals the economical performance of hybrid rocket is limited with HRE in terms of the maximum downrange achievable. Moreover, the data-mining result indicates the mechanism of design-variable behavior.

Atthaphon Ariyarit

Papers

Hybrid surrogate-model-based multi-fidelity efficient global optimization applied to helicopter blade design

Multi-Fidelity Multi-Objective Efficient Global Optimization Applied to Airfoil Design Problems

Multi-modal distribution crossover method based on two crossing segments bounded by selected parents applied to multi-objective design optimization

Multi-segment Class-Shape-Transformation (CST) for global design exploration

Multidisciplinary Design Exploration for Sounding Launch Vehicle using Hybrid Rocket Engine in View of Ballistic Performance