Simulation-based optimization for design parameter exploration in hybrid system: a defense system example

Abstract

This paper presents a method for solving the optimization problems that arise in hybrid systems. These systems are characterized by a combination of continuous and discrete event systems. The proposed method aims to find optimal design configurations that satisfy a goal performance. For exploring design parameter space, the proposed method integrates a metamodel and a metaheuristic method. The role of the metamodel is to give good initial candidates and reduced search space to the metaheuristic optimizer. On the other hand, the metaheuristic method improves the quality of the given candidates. This proposal also demonstrates a defense system that illustrates the practical application of the presented method. The optimization objective of the case study is to find the required operational capability configurations of a decoy that meet the desired measure of effectiveness. Through a comparison with a full search method, two metamodeling methods without the aid of metaheuristics and a metaheuristic method without the support of metamodels, we confirmed that the proposed method provides same high-quality solutions as those of the full search method at a small computational cost.

Simulation-Optimisation of a Granularity Controlled Consumer Supply Network Using Genetic Algorithms

“…Also, obtaining an absolute global optimum is not guaranteed, however providing good solutions within a reasonable time is generally expected [41,42]. Also, GA may not be effective if the starting point in search space was at a great distance from optimal solutions [43]. This deficiency limits the use of GA in real-time applications.…”

Simulation metamodeling approach to complex design of garment assembly lines

“…The most commonly used approaches for metamodel construction are statistic-based and machine-learning. The former solely depends on the data received from the simulation experiments which includes linear (polynomial) regression, support vector regression, multivariate adaptive regression spline, Gaussian process regression (kriging), and radial basis function [19,[27][28][29][30][31]. The latter is based on neural networking, rule learning, and fuzzy logic [16,18,32,33].…”

Simulation-Based Optimization on the System-of-Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network-Centric Warfare

“…The SoS-based NCW simulation offers the advantage of enabling the conduct of the "what-if" analysis of the combat power against various combat scenarios with related parameters in the combat model, by reflecting detailed communication effects from the network model [16,17]. However, the high complexity of the network model and the interface connecting the two models paradoxically increase the execution time per simulation trial [18][19][20].…”

“…One way to reduce the enormous simulation runs is to apply the optimization method for the discrete-event stochastic simulation model [17,23,24]. However, this process also requires the considerable time because it takes a long time per run of the SoS-based simulation model.…”