Efficient evolutionary optimization algorithm : filtered differential evolution

Solving many real-life engineering problems requires often global and efficient (in terms of objective function evaluations) treatment, because function values involved are produced via time consuming simulations. In this study, we consider optimization problems of this type by discussing some drawbacks of the current surrogate assisted methods and then introduce a new population based optimization algorithm, which borrows features of the well-known Differential Evolution algorithm, but improves its efficiency by filtering away ineffective trial points.

Multi-objective actuator placement optimization for local sound control evaluated in a stochastic domain

A method to find optimal locations and properties of anti-noise actuators in local noise control system is considered. The local noise control performance is approximated by a finite element method based approach, that attempts to estimate the average performance of optimal active noise control (ANC) system. The local noise control uses a fixed number of circular actuators that are located on the boundary of a three-dimensional enclosed acoustic space. Actuator signals are used to minimize the known harmonic noise at specified locations. The average noise reduction is maximized at two frequency ranges by adjusting the anti-noise actuator configuration, which is a non-linear multi-objective …

On optimization of simulation based design

Decreasing computational cost of simulation based interactive multiobjective optimization with adjustable solution accuracy

Solving real-life engineering problems can be time-consuming and difficult because problems may have multiple conflicting objectives, functions involved highly nonlinear and containing multiple local minima, and function values are often produced via a time-consuming simulation process. Problems of this type can be solved using global multiobjective optimization methods, preferably with interactive approaches, which allow the designer (or decision maker in general) to learn about the behaviour of the problem during the solution process. In an interactive approach the designer specifies preferences and Pareto optimal solution(s) following these preferences are generated, typically by forming…