Evolutionary Multimodal Optimization Research Articles

Simulation Optimization in Uncertain Environments: An Evolutionary Approach

The design of manufacturing systems often involves simulation optimization approaches to search for the best system performance. Metaheuristic approaches are more and more used to optimize simulation models. In most existing approaches, the optimization is performed with fixed environmental conditions (e. g., part demand, breakdown rates are assumed to be perfectly known). However, in practice the actual data about the system may differ from those used in the simulation model (e. g., modification of the part mix). To cope with this, the candidate solutions, in a simulation optimization process, can be compared on various possible environments, using such principles as those proposed by Taguchi. Unfortunately, when metaheuristics are used, simulation optimization can be very much time consuming, since each solution has to be compared on a number of different environments. In order to provide robust solutions in a more reasonable time, we propose a two stage heuristic search. First, a restricted set of n promising solutions is identified using an evolutionary multimodal simulation optimization process, using the concept of base environmental scenario, recently published. Then robustness evaluation on many environments is performed only on these n promising solutions and the most robust can be chosen. This approach is illustrated on a supply chain problem where several parameters have to be defined. As a result, 13 solutions are found. The most robust solution is not the one that yields the best results in the environment assumed by the decision maker. This result shows how important it is to be able to consider several environments in the simulation optimization process.

A population global optimization algorithm to solve the image alignment problem in electron crystallography

Knowledge of the structure of biological specimens is critical to understanding their function. Electron crystallography is an electron microscopy (EM) approach that derives the 3D structure of specimens at high-resolution, even at atomic detail. Prior to the tomographic reconstruction, the images taken from the microscope have to be properly aligned. Traditional alignment methods in electron crystallography are based on a phase residual function to be minimized by inefficient exhaustive search procedures. This work addresses this minimization problem from an evolutionary perspective. Universal Evolutionary Global Optimizer (UEGO), an evolutionary multimodal optimization algorithm, has been applied and evaluated for the task of image alignment in this field. UEGO has turned out to be a promising technique alternative to the standard methodology. The alignments found out by UEGO show high levels of accuracy, while reducing the number of function evaluations by a significant factor with respect to the standard method.