Robust Optimization with Interval Uncertainties Using Hybrid State Transition Algorithm

Author:

Zhang Haochuan1,Han Jie1,Zhou Xiaojun1,Zheng Yuxuan1

Affiliation:

1. School of Automation, Central South University, Changsha 410083, China

Abstract

Robust optimization is concerned with finding an optimal solution that is insensitive to uncertainties and has been widely used in solving real-world optimization problems. However, most robust optimization methods suffer from high computational costs and poor convergence. To alleviate the above problems, an improved robust optimization algorithm is proposed. First, to reduce the computational cost, the second-order Taylor series surrogate model is used to approximate the robustness indices. Second, to strengthen the convergence, the state transition algorithm is studied to explore the whole search space for candidate solutions, while sequential quadratic programming is adopted to exploit the local area. Third, to balance the robustness and optimality of candidate solutions, a preference-based selection mechanism is investigated which effectively determines the promising solution. The proposed robust optimization method is applied to obtain the optimal solutions of seven examples that are subject to decision variables and parameter uncertainties. Comparative studies with other robust optimization algorithms (robust genetic algorithm, Kriging metamodel-assisted robust optimization method, etc.) show that the proposed method can obtain accurate and robust solutions with less computational cost.

Funder

National Key Research and Development Program of China

Hunan Natural Science Foundation

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

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