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In this article, the design sensitivity is examined using the material derivative concept of the shape sensitivity analysis to use a gradient-based algorithm for the multi-objective benchmark testing electromagnetic analysis methods (TEAMs) problem. To calculate the shape sensitivity, the original objective functions are replaced with the definite integral form of the functionals, and the p-norm expression is used to handle the minimax problem. The computed sensitivities are validated using the finite-difference numerical sensitivities. The validity of the modified objectives is demonstrated using the results of the single-objective optimization. The results show that the analytical sensitivities agree well with the numerical ones, and the modified objective functions are valid for the benchmark optimization problem. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-objective optimization method using an adaptive weight determination scheme and tunneling method. To find the optimum designs that are evenly distributed on the Pareto front, the weights of different objective functions are adaptively determined by using the concept of a hyperplane so that new solutions can be gradually found in the objective space. In addition, to avoid locally optimized solutions that are dominated by other Pareto optimum designs, a tunneling method is employed in the optimization process. To confirm the effectiveness of the proposed method, topology optimization of the magnetic actuator is performed. The magnetic force and volume of the actuator are considered as two conflicting objective functions in the optimization problem, and Pareto optimum solutions that are evenly distributed in the objective space could be obtained. [ABSTRACT FROM AUTHOR]

Summary: Finding an optimum design that satisfies all performances in a design problem is very challenging. To overcome this problem, multiobjective optimization methods have been researched to obtain Pareto optimum solutions. Among the different methods, the weighted sum method is widely used for its convenience. However, since the different weights do not always guarantee evenly distributed solutions on the Pareto front, the weights need to be determined systematically. Therefore, this paper presents a multiobjective optimization using a new adaptive weight determination scheme. Solutions on the Pareto front are gradually found with different weights, and the values of these weights are adaptively determined by using information from the previously obtained solutions' positions. For an n‐objective problem, a hyperplane is constructed in n ‐dimensional space, and new weights are calculated to find the next solutions. To confirm the effectiveness of the proposed method, benchmarking problems that have different types of Pareto front are tested, and a topology optimization problem is performed as an engineering problem. A hypervolume indicator is used to quantitatively evaluate the proposed method, and it is confirmed that optimized solutions that are evenly distributed on the Pareto front can be obtained by using the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective topology optimization method for a magnetic actuator design considering both magnetic force and actuator volume. We use a gradient-based optimization algorithm and the multi-objective optimization problem is formulated using a weighted sum of the objective functions. An adaptive weight determination scheme is iteratively employed to update the weighting coefficients of the objective functions to obtain non-dominated solutions. To ensure that the optimal solutions are evenly distributed in the objective domain, $\varepsilon $ -equality constraints are applied in the empty regions of the non-dominated solutions. The values of the constraints are determined by considering the distance ratios of the objective functions. The optimization problem is formulated to maximize the magnetic force and minimize the volume of the actuator. A level set function is used as a topological design variable to obtain optimal designs that have clear structural boundaries. [ABSTRACT FROM AUTHOR]

Complex systems like combat vehicles contain numerous subsystems and components. Therefore, simultaneous consideration of hierarchy of a system, subsystems, and components is necessary for optimization. Multidisciplinary design optimization techniques have been researched to design the complex system. However most of these techniques premise integration process of total system which requires great time and cost. To reduce time and cost for the integration process, we introduce a target cascading technique that optimizes complex hierarchy system with several subproblems of each subsystem and component. Another challenge is to improve the firing accuracy of combat vehicle under various uncertainties. Robust design is therefore necessary to improve the firing accuracy of combat vehicles. To utilize these two concepts in optimization process, statistical information of firing angle is used as linking variables for problem formulation of robust target cascading. Furthermore, analysis of variance, surrogate modeling and statistical approach evaluating firing accuracy are employed to enhance efficiency of optimization. Finally, optimum design of a combat vehicle is achieved by using robust target cascading while improving firing accuracy. [ABSTRACT FROM AUTHOR]