A Local 3D Voronoi-Based Optimization Method for Sensor Network Deployment in Complex Indoor Environments.

The covariance matrix adaptation evolutionary strategy (CMA-ES) is explored here as an improved alternative to well-established algorithms used in electromagnetic (EM) optimization. In the past, methods such as the genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) have commonly been used for EM design. In this article, we examine and compare the performance of CMA-ES, PSO, and DE when applied to test functions and several challenging EM design problems. Of particular interest is demonstrating the ability of the relatively new CMA-ES to more quickly and more reliably find acceptable solutions compared with those of the more classical optimization strategies. In addition, it will be shown that due to its self-adaptive scheme, CMA-ES is a more user-friendly algorithm that requires less knowledge of the problem for preoptimization configuration.

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.

Using WIFI fingerprint positioning is Vulnerable to indoor environment and various noise signals , positioning error in indoor environment to the wireless signal strength is in the controllable range, although the starting point can be obtained, but its accuracy is easily affected by complex indoor environment. Inertial positioning is not affected by the indoor environment, but there is a cumulative error. At present, the widespread application of intelligent mobile phone, the activities of various sensors embedded can indirectly describe pedestrians, with intelligent mobile phone to achieve indoor positioning can not only reduce the cost of positioning, which can meet the daily demand for indoor positioning. Indoor positioning method based on inertial navigation real-time location of pedestrian path and wireless signal fingerprint with the online positioning by using the neural network matching algorithm outperforms the KNN algorithm in the positioning accuracy, so it can avoid the cumulative error of inertial navigation and positioning, and also reduce the influence of environment interference factor on the positioning accuracy, finally verified by experiments two, positioning algorithm combined with neural network positioning result is obviously better than the traditional positioning accuracy.

Local positioning in buildings without access to the Internet is a crucial requirement in many scenarios, such as large corporate offices, hospitals, universities, and shopping malls. However, this need for local positioning also presents certain challenges and problems that must be addressed. One of the most significant challenges of local positioning in buildings without internet access is the lack of reliable and up-to-date indoor map data. Without accurate map data, it can be difficult to navigate through complex indoor environments or locate specific rooms or facilities within a building. Another challenge is the absence of GPS signals in indoor environments, which makes it challenging to obtain accurate location information. This is especially problematic when attempting to track the movement of people or objects within a building.Additionally, the lack of internet access can make it difficult to implement location-based services that rely on data transfer between devices. This includes applications like indoor navigation, asset tracking, and location-based marketing, which all require internet connectivity to function correctly. Despite these challenges, there is a growing need for local positioning in buildings without internet access. This is especially true for large organizations and institutions that rely on efficient and effective operations within their indoor environments. Some technologies can provide accurate location information and help to mitigate some of the challenges associated with local positioning in buildings without internet access. They allows to obtain the accurate location information without relying on GPS signals or internet connectivity.The need for local positioning in buildings without internet access is becoming increasingly important, especially for large organizations and institutions that require efficient and effective operations within their indoor environments.

Multi types DG expansion dynamic planning in distribution system under stochastic conditions using Covariance Matrix Adaptation Evolutionary Strategy and Monte-Carlo simulation

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

With the development of Internet of Thing (IoT), positioning is of a significant importance in indoor environments. Thus, recent years, localization technology has advanced greatly aimed at localizing the target by utilizing existing infrastructure such as WiFi. However, in a complex indoor environment, the performance of localization system tend to fluctuates when there is Non-Line-of-Sight (NLoS) propagation. Specially, for the high accuracy localization system using Angle of Arrival (AoA) or Time of Flight (ToF) of the direct signal path, the robustness is a challenge since the propagations of signal are not always in Line-of-Sight (LoS) due to the complicated indoor environment. Environment sensing is of significant importance for improving the accuracy and robustness of high accuracy localization system working in NLoS environment. In this paper, we propose a context-aware algorithm by using the spatial properties of multipath signal to aid indoor localization. Firstly, we investigate the influence of Signal-Noise-Ratio (SNR) on the distribution of signal spatial parameters, i.e., AoA and ToF, by deriving a mathematical model. Then, we creatively employ the method of clustering tendency analysis to construct a LoS/NLoS identification collaborating with non-parametric statistical method. Finally, we deploy a localization system with the proposed context-aware algorithm. The extensive experimental result shows that the proposed algorithm can sense environment well and further improve the accuracy and robustness of localization system.

Features of several well-known global and local optimization methods were discussed and their robustness and efficiency were tested on several artificial test functions in Matlab environment. The tested local methods were the interior-point, the quasi-Newton method, Nelder–Mead simplex, the pattern search, the NEWUOA and the BOBYQA methods. The global methods were the genetic algorithm (GA), the simulated annealing (SA), the particle swarm optimization (PSO), and the covariance matrix adaptation evolutionary strategy (CMA-ES) methods (see subsections 2.2 and 2.3 for their details). Furthermore, a novel global optimization method, called FOCusing robusT Optimization with Uncertainty-based Sampling (FOCTOPUS), which proved to be very efficient in the optimization of constrained and highly correlated parameters of combustion kinetic models, was also tested. The test functions were selected in such a way that they had a variety of features: uni-modal and multi-modal, differentiable and non-differentiable, separable and non-separable, low dimensional and high dimensional. The following test functions were used: the 20D Alpine function, the 4D Ackley function, the Cross-in-tray 2D function, the Hartmann-6D function, the Holder table 2D function, the 5D Rastrigin function, the 5D Rosenbrock function, the 4D modified Rosenbrock function, the 20D Zakharov function and a typical 2D multi-modal function. The general conclusion here is that, the global methods performed well on the multi-modal and high-dimensional test functions while the local methods were superior in the case of low-dimensional and uni-modal test functions. For the highly multi-modal test functions, the GA was better than all the other methods. The FOCTOPUS method proved to be inferior to GA for most of the test functions, thus its application cannot be generally recommended.

Assisting elderly individuals in indoor environments is crucial, as they often encounter mobility and safety challenges. Autonomous Unmanned Aerial Vehicles (UAVs) navigation offers a transformative solution, significantly enhancing safety, efficiency, and quality of life. These UAVs can perform tasks such as monitoring, delivering essential items, and responding to emergencies, providing invaluable support and promoting greater independence for elderly individuals. To achieve effective assistance, autonomous UAV navigation relies on robust path planning with obstacle avoidance to ensure optimal performance. A newly developed path planning, based on a new hybrid Water Cycle Algorithm (WCA), stands out by addressing the challenge of avoiding obstacles while adhering to non-holonomic constraints and conserving energy in complex, cluttered indoor environments. In Population-Based Algorithms (PBAs), the initial population plays a crucial role, as it greatly impacts the algorithm’s efficiency in exploring the search space and its rate of convergence. The new developed hybrid algorithm, called WCA-HS, is based on using the Harmony Search (HS) algorithm to identify the optimal initial population for the WCA. The results indicate that the hybrid WCA-HS outperforms classical WCA and other metaheuristic algorithms, such as HS, Firefly Algorithm (FA), Cuckoo Search (CS), Genetic Algorithm (GA), Differential Evolution (DE), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC), over 20 independent runs, highlighting the effectiveness of the population initialization technique. Additionally, the developed indoor path planning system was evaluated in highly cluttered and low-light environments, showcasing its robustness and real-world applicability, making it highly effective for assisting elderly individuals in complex indoor environments.

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

High-precision, low-cost, and wide coverage indoor positioning technology is the key to indoor and outdoor integrated location-based services, and it has broad market prospects and social value. However, achieving sub-meter level positioning accuracy in indoor environments remains a real challenge due to the blockage of indoor Global Navigation Satellite System (GNSS) signals, the complexity of indoor environments, and the unpredictability of user behavior. In this paper, we introduce a multi-module BLE broadcaster (MMBB)-based indoor positioning solution in which a tightly coupled fusion architecture is implemented on a smartphone. The solution integrates ranging measurements from multiple MMBB and the measurements of the accelerometer built into a smartphone. It becomes an instant positioning solution without any training phase by adopting a calibrated linearly segmented path loss model for ranging. We apply the pedestrian walking speed derived by the smartphone accelerometer to constrain an unscented Kalman filter method that is used to estimate the location and speed. The accuracy of the proposed method is 50% at 0.79 m and 95% at 1.6 m at in terms of horizontal error distance. Position update frequency is 10 Hz and the time to first fix is 0.1 s. The system can easily adapt a global coordinator system so that it can seamlessly work together with the GNSS to form an indoor/outdoor positioning solution.

Aiming at satisfying the users' navigation requirements in complex indoor environments, an indoor route planning method with environment awareness is proposed in this paper. Considering the accessibility, simplicity and comfortable experience of user's navigation, the paper first describes the modeling method of indoor navigation network and defines the expressions of indoor environment semantics. And then a navigation cost function is presented, which includes environment semantics such as path complexity, crowded degree and blocking events into indoor navigation network model. Finally, this paper proposes an optimal indoor route planning method by introducing the navigation cost function and environment semantics into traditional Dijkstra algorithm. The experimental results show that the proposed method can effectively enhance the user's comfort experience in indoor navigation.

The high dimensionality of electronic nose data increases the difficulty of their use in classification models. Reducing this high dimensionality helps reduce variable numbers, improve classification accuracy, and reduce computation time and sensor cost. In this research, the Cyranose 320 electronic nose, which was used for apple defect detection, was optimized by selecting only the most relevant of its internal 32 sensors using different selection methods. Two robust heuristic optimization algorithms, genetic algorithm (GA) and covariance matrix adaptation evolutionary strategy (CMAES), were applied and compared. Although both algorithms searched the optimal sensors resulting in a best classification error rate of 4.4%, the average classification error rate of CMA over 30 random seed runs was 5.0% (s.d.=0.006) which was better than 5.2% (s.d.=0.004) from the GA. The final optimal solution sets obtained by integer GA showed that including more sensors did not guarantee better classification performance. The best reduction in classification error rate was 10% while the number of sensors was reduced 78%. This study provided a robust and efficient optimization approach to reduce high data dimensionality of the electronic nose data, which substantially improved electronic nose performance in apple defect detection while potentially reducing the overall electronic nose cost for future specific applications.

Abstract. The demand for indoor navigation is increasingly urgent in many applications such as safe management of underground spaces or location services in complex indoor environment, e.g. shopping centres, airports, museums, underground parking lot and hospitals. Indoor navigation is still a challenging research field, as currently applied indoor navigation algorithms commonly ignore important environmental and human factors and therefore do not provide precise navigation. Flexible and detailed networks representing the connectivity of spaces and considering indoor objects such as furniture are very important to a precise navigation. In this paper we concentrate on indoor navigation considering obstacles represented as polygons. We introduce a specific space subdivision based on a simplified floor plan to build the indoor navigation network. The experiments demonstrate that we are able to navigate around the obstacles using the proposed network. Considering to well-known path-finding approaches based on Medial Axis Transform (MAT) or Visibility Graph (VG), the approach in this paper provides a quick subdivision of space and routes, which are compatible with the results of VG.

Comparison of emerging metaheuristic algorithms for optimal hydrothermal system operation