Application of evolutionary algorithms to optimise one- and two-dimensional gradient chromatographic separations.

Optimal sensor network deployment in built environments for tracking, surveillance, and monitoring of dynamic phenomena is one of the most challenging issues in sensor network design and applications (e.g., people movement). Most of the current methods for sensor network deployment and optimization are empirical and they often result in important coverage gaps in the monitored areas. To overcome these limitations, several optimization methods have been proposed in the recent years. However, most of these methods oversimplify the environment and do not consider the complexity of 3D architectural nature of the built environments specially for indoor applications (e.g., indoor navigation, evacuation, etc.). In this paper, we propose a novel local optimization algorithm based on a 3D Voronoi diagram, which allows a clear definition of the proximity relations between sensors in 3D indoor environments. This proposed structure is integrated with an IndoorGML model to efficiently manage indoor environment components and their relations as well as the sensors in the network. To evaluate the proposed method, we compared our results with the Genetic Algorithm (GA) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithms. The results show that the proposed method achieved 98.86% coverage which is comparable to GA and CMA-ES algorithms, while also being about six times more efficient.

Optimization of the profile and distribution of absorption material in sonic black holes

A competitive variable-fidelity surrogate-assisted CMA-ES algorithm using data mining techniques

The reconstruction problem in diffuse optical tomography can be formulated as an optimization problem, in which an objective function has to be minimized. Current model-based iterative image reconstruction schemes commonly use information about the gradient of the objective function to locate the minimum. These gradient-based search algorithms often find local minima close to an initial guess, or do not converge if the gradient is very small. If the initial guess is too far from the solution, gradient-based schemes prove inefficient for finding the global minimum. In this work we introduce evolution-strategy (ES) algorithms for diffuse optical tomography. These algorithms seek to find the global minimum and are less sensitive to initial guesses and regions with small gradients. We illustrate the fundamental concepts by comparing the performance of gradient-based schemes and ES algorithms in finding optical properties (absorption coefficient microa , scattering coefficient micros , and anisotropy factor g) of a homogenous medium.

Optimum implementation of non-conventional wells allows us to increase considerably hydrocarbon recovery. By considering the high drilling cost and the potential improvement in well productivity, well placement decision is an important issue in field development. Considering complex reservoir geology and high reservoir heterogeneities, stochastic optimization methods are the most suitable approaches for optimum well placement. This paper proposes an optimization methodology to determine optimal well location and trajectory based upon the Covariance Matrix Adaptation-Evolution Strategy (CMA-ES) which is a variant of Evolution Strategies recognized as one of the most powerful derivative-free optimizers for continuous optimization. To improve the optimization procedure, two new techniques are investigated: (1). Adaptive penalization with rejection is developed to handle well placement constraints. (2). A meta-model, based on locally weighted regression, is incorporated into CMA-ES using an approximate ranking procedure. Therefore, we can reduce the number of reservoir simulations, which are computationally expensive. Several examples are presented. Our new approach is compared with a Genetic Algorithm incorporating the Genocop III technique. It is shown that our approach outperforms the genetic algorithm: it leads in general to both a higher NPV and a significant reduction of the number of reservoir simulations. ECMOR XII – 12 th European Conference on the Mathematics of Oil Recovery

Handling bound constraints in CMA-ES: An experimental study

This paper introduces a multi-stage dynamic transmission network expansion planning (MSDTNEP) model considering the N-1 reliability constraint. The integrated planning problem of N-1 security and transmission expansion planning is essential because a single line outage could be a triggering event to rolling blackouts. Two suggested scenarios were developed to obtain the optimal configuration of the Egyptian West Delta Network’s realistic transmission (WDN) to meet the demand of the potential load growth and ensure the system reliability up to the year 2040. The size of a blackout, based on the amount of expected energy not supplied, was calculated to evaluate both scenarios. The load forecasting (up to 2040) was obtained based on an adaptive neuro-fuzzy inference system because it gives excellent results compared to conventional methods. The linear population size reduction—Success-History-based Differential Evolution with semi-parameter adaptation (LSHADE-SPA) hybrid—covariance matrix adaptation evolution strategy (CMA-ES) algorithm (LSHADE-SPACMA)—is applied to solve the problem. The semi-adaptive nature of LSHADE-SPACMA and the hybridization between LSHADE and CMA-ES are able to solve complex optimization problems. The performance of LSHADE-SPACMA in solving the problem is compared to other well-established methods using three testing systems to validate its superiority. Then, the MSDTNEP of the Egyptian West Delta Network is presented, and the numerical results of the two scenarios are compared to obtain an economic plan and avoid a partial or total blackout.

Curtailment of the ambient noise level for providing a better living environment is immensely important. Accordingly, acoustic isolation via different combinations of porous materials is the most widely used passive soundproofing system. The present study focuses on the optimization of single and double-layer absorbers in different frequencies. To this end, the transfer matrix and the Evolution Strategy (ES) method are firstly explained. Afterward, the optimization of single and double-layer absorbers is considered for up to 10 parameters (material porosity, air gap, perforated plate characteristics among others) at 350 Hz frequency and has been compared with the results obtained through other methods (Genetic Algorithm among others). It has been illustrated that ES algorithm provides better optimization in this field. Subsequently, since the incident sound in most cases is a correlation of different frequencies, the broadband optimization of the single and double-layer absorbers is considered in three frequency ranges (100–800 Hz, 800–1600 Hz, 1600–3000 Hz), with an increment of 1 Hz, for three different materials (polyester, fiber and foam). After the optimization, the resulting optimum parameters are presented in form of characteristics charts of the optimized materials for different frequency ranges, as a reference for material designers and manufacturers. Also, the absorption coefficient of all optimized cases are calculated and presented in range of 100 Hz to 3 kHz as a reference for the absorber selection for different situations. Finally, by presenting the improvement chart of double layer versus single layer combinations, it has been shown that double layer combination can improve the absorption coefficient of different materials up to 4% in different frequencies depending on the material (4% for polyester and foam for under 800 Hz, 3–4% for polyester and fiber for 800–1600 Hz and 2.6% for foam in 1600–3000 Hz).

Surrogate-assisted evolutionary algorithms provide an effective means for complex and computationally expensive optimization problems. However, due to the scarcity of training samples, the prediction accuracy of frequently-used regression surrogate models can hardly be guaranteed as the difficulty of the problem increases, resulting in performance degradation of the whole algorithm. Since real-world problems rarely exist in isolation, it is expected to alleviate the above issue by properly exploiting the knowledge shared across different problems. In this context, this study proposes a novel evolutionary multitasking optimization algorithm assisted by a classifier rather than a regression model for expensive multitasking problems, where the accuracy of the classifier is boosted by knowledge transfer. To be specific, a support vector classifier (SVC) is first developed and integrated into a classic evolutionary algorithm, i.e., covariance matrix adaptation evolution strategy (CMA-ES). With a low computational cost, it helps CMA-ES to prescreen parent solutions from the current population. Following that, a knowledge transfer strategy is designed to enrich the training samples for each task-oriented classifier by sharing high-quality solutions among different tasks, where a PCA-based subspace alignment technique is employed. Extensive experiments indicate that the SVC-assisted CMA-ES gains significant superiority over general CMA-ES in terms of both robustness and scalability, and the knowledge transfer strategy further helps it earn a competitive edge over some state-of-the-art algorithms on expensive multitasking optimization problems.

ReaxFF is a computationally efficient force field to simulate complex reactive dynamics in extended molecular models with diverse chemistries, if reliable force-field parameters are available for the chemistry of interest. If not, they must be optimized by minimizing the error ReaxFF makes on a relevant training set. Because this optimization is far from trivial, many methods, in particular genetic algorithms (GAs), have been developed to search for the global optimum in parameter space. Recently, two alternative parameter calibration techniques were proposed, i.e. Monte-Carlo Force Field optimizer (MCFF) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). In this work, CMA-ES, MCFF and a GA method (OGOLEM) are systematically compared using three training sets from the literature. GA shows the smallest risk of getting trapped into a local minimum, whereas CMA-ES is capable of reaching the lowest errors for two third of the cases. For each method, we provide reasonable default settings and our analysis offers useful guidelines for their usage in future work. An important side effect impairing the parameter optimization is numerical noise. A detailed analysis reveals that it can be reduced, e.g. by using exclusively unambiguous geometry optimizations in the training set. Even without this noise, many distinct near-optimal parameter vectors can be found, which opens new avenues for improving the training set and detecting overfitting artifacts.

Back analysis of geomechanical parameters in underground works using an Evolution Strategy algorithm

Background: Defect compensation is one of the enabling techniques for high-volume manufacturing using extreme ultraviolet lithography. Aim: The advanced evolution strategy algorithm based on covariance matrix adaption is applied to compensation optimization to improve the convergence efficiency and algorithm operability. Approach: The advanced algorithm optimizes the solution population by sampling from the self-adapted covariance matrix of mutation distribution. Results: Optimization simulations for three different masks validated the algorithm’s advantage in convergence efficiency and searching ability compared with original differential evolution, evolution strategy, genetic algorithm (GA), and Nelder–Mead simplex method. The advanced algorithm employs fewer user-defined parameters and is proved to be robust to variations of these parameters. Conclusions: The advanced algorithm obtains better results compared with GA for best-focus, through-focus, and complex-pattern optimizations. With the inherent invariance property, appropriate operability, and robustness, we recommend applying this algorithm to other lithography optimization problems.

Variable metric evolution strategies by mutation matrix adaptation

Vis-NIR hyperspectral imaging for the classification of bacterial foodborne pathogens based on pixel-wise analysis and a novel CARS-PSO-SVM model

Many high-performance ultra-wideband array topologies have recently arisen that utilize mathematical constructs such as fractals or simple mathematical expressions to determine element locations [1] – [2]. The main motivation of these methods is to reduce the scale of the optimization problem such that it is feasible for the evolutionary strategy. In this manner, large arrays can be successfully optimized with a reasonably small set of controlling parameters. However, for an array with a small number of elements, these methods can be constraining and lead to less than acceptable peak sidelobe level performances and poor bandwidths. For these small arrays, the element locations can be directly specified with the optimization tool if it is capable of handling the large parameter set. Previous attempts using the genetic algorithm (GA) [3] with array sizes of eight to 24 elements and large scan angles (analogous to a small bandwidth) were preformed in [4]. With a more powerful evolutionary strategy, problems with large parameter sets become much more practical and yield better results. The covariance matrix adaptation evolutionary strategy (CMA-ES) is a recently developed and extremely powerful optimization tool that has shown itself to be a leader in speed and possesses an excellent capacity for handling problems of large dimensions [5]. It is applied here to the direct optimization of element spacings for relatively small to moderate size ultrawideband antenna arrays in order to extract the best performance possible from a limited number of antenna elements.