Composite Lamina Model Design with the Use of Heuristic Optimization

In engineering practice, a problem is quite often faced in which the number of unknown parameters exceeds the number of conditions or requirements or, otherwise, there are too many requirements for too few parameters to design. Such under- or over-defined tasks are sometimes not possible to solve using a direct approach. The number of solutions to such problems is multiple, and it is most rational to search for the optimal one by numerical methods since the more unknown design parameters there are to be designed, the more potential solutions there are. This article discusses a way to find an optimal solution to such an underdetermined problem by heuristic optimization methods on the basis of the example of designing a composite wing skin of an aircraft. Several heuristic approaches, specifically gradient descent and Tabu search, are studied to solve the design problem and to locate an optimal solution. They are also compared to a conventional direct approach. The examined composite lamina is optimized by the target function of minimum weight with the constraints of strength and buckling failure criteria. In most of the observed cases, the heuristic method designed structures which were considerably better than the structures that were obtained by conventional direct approaches in terms of the weight to load ratio.

Semi-quantitative discrimination of honey adulterated with cane sugar solution by an ETongue

This study successfully applied a potentiometric E-tongue with 20 cross-selectivity lipidic polymeric membranes in the discrimination of three semi-quantitative groups, that represented the following intervals of honey adulteration percentage with cane sugar: 0%; ]0, 10]%; ]10, 20]% of adulteration. We analysed five different types of Portuguese honey; five brands of cane sugar were added to the adulterated samples; a comparative analyses was then performed. Linear discriminant analysis coupled with a tabu search algorithm for feature selection was applied to the ETongue's analytical data to select the best model. A discriminant model with 12 sensors was obtained. This model classified correctly all samples in both in internal (train data, 15 samples) and external validation (test data,10 samples). Also, multiple linear regression with tabu search was applied to verify if ETongue's data would allow quantifying the honey's adulteration level. The results showed that it was possible to obtain a quantitative model but with unsatisfactory predictive performance in the test data group (external validation), giving, in general, values below the expected concentrations. E-tongue is a real-time green, flexible and low-cost analytical tool that requires minimum sample preparation and no special technical skills, being a promising tool for everyday application.

A Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem

In this paper, we present a hybrid genetic-hierarchical algorithm for the solution of the quadratic assignment problem. The main distinguishing aspect of the proposed algorithm is that this is an innovative hybrid genetic algorithm with the original, hierarchical architecture. In particular, the genetic algorithm is combined with the so-called hierarchical (self-similar) iterated tabu search algorithm, which serves as a powerful local optimizer (local improvement algorithm) of the offspring solutions produced by the crossover operator of the genetic algorithm. The results of the conducted computational experiments demonstrate the promising performance and competitiveness of the proposed algorithm.

Time Difference of Arrival Passive Localization Sensor Selection Method Based on Tabu Search

This paper proposes a time difference of arrival (TDOA) passive positioning sensor selection method based on tabu search to balance the relationship between the positioning accuracy of the sensor network and system consumption. First, the passive time difference positioning model, taking into account the sensor position errors, is considered. Then, an approximate closed-form constrained total least-squares (CTLS) solution and a covariance matrix of the positioning error are provided. By introducing a Boolean selection vector, the sensor selection problem is transformed into an optimization problem that minimizes the trace of the positioning error covariance matrix. Thereafter, the tabu search method is employed to solve the transformed sensor selection problem. The simulation results show that the performance of the proposed sensor optimization method considerably approximates that of the exhaustive search method. Moreover, it can significantly reduce the running time and improve the timeliness of the algorithm.

Vehicle routing for the urgent delivery of face shields during the COVID-19 pandemic

The speed by which the COVID-19 pandemic spread throughout the world caught some national and local governments unprepared. Healthcare systems found themselves struggling to increase capacity and procure key supplies, such as personal protective equipment. Protective face shields became essential for healthcare professionals. However, most hospitals and healthcare facilities did not have them in adequate quantities. The urgency of producing and delivering face shields increased as the number of COVID-19 cases rapidly multiplied. This was the situation that we encountered in the city and province of Burgos (Spain). Since there was no time to wait for a large manufacturer to produce face shields, private citizens and small companies volunteered to make them using technologies such as 3D printers. Nonprofits, citizens, and governments agencies volunteered to deliver materials to the face shield makers and to pick up and deliver the face shields to health centers and other locations where they were needed. This resulted in a vehicle routing problem with some special characteristics that made it different from models used for commercial purposes. We describe the development of a heuristic to find feasible and efficient routes for this problem. We highlight the advantages of using heuristics in an emergency context like the one triggered by the COVID-19 pandemic. In particular, the heuristic approach allowed us to design, implement, test, and delivery a routing system in less than 1 week from the time that the local government contacted us with what they described as a logistics nightmare.

An Improved Normalized Mutual Information Variable Selection Algorithm for Neural Network-Based Soft Sensors

In this paper, normalized mutual information feature selection (NMIFS) and tabu search (TS) are integrated to develop a new variable selection algorithm for soft sensors. NMIFS is applied to select influential variables contributing to the output variable and avoids selecting redundant variables by calculating mutual information (MI). A TS based strategy is designed to prevent NMIFS from falling into a local optimal solution. The proposed algorithm performs the variable selection by combining the entropy information and MI and validating error information of artificial neural networks (ANNs); therefore, it has advantages over previous MI-based variable selection algorithms. Several simulation datasets with different scales, correlations and noise parameters are implemented to demonstrate the performance of the proposed algorithm. A set of actual production data from a power plant is also used to check the performance of these algorithms. The experiments showed that the developed variable selection algorithm presents better model accuracy with fewer selected variables, compared with other state-of-the-art methods. The application of this algorithm to soft sensors can achieve reliable results.

A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption

The past decade has seen a substantial increase in the use of small unmanned aerial vehicles (UAVs) in both civil and military applications. This article addresses an important aspect of refueling in the context of routing multiple small UAVs to complete a surveillance or data collection mission. Specifically, this article formulates a multiple-UAV routing problem with the refueling constraint of minimizing the overall fuel consumption for all the vehicles as a two-stage stochastic optimization problem with uncertainty associated with the fuel consumption of each vehicle. The two-stage model allows for the application of sample average approximation (SAA). Although the SAA solution asymptotically converges to the optimal solution for the two-stage model, the SAA run time can be prohibitive for medium- and large-scale test instances. Hence, we develop a tabu search-based heuristic that exploits the model structure while considering the uncertainty in fuel consumption. Extensive computational experiments corroborate the benefits of the two-stage model compared to a deterministic model and the effectiveness of the heuristic for obtaining high-quality solutions.

A New TS Algorithm for Solving Low-Carbon Logistics Vehicle Routing Problem with Split Deliveries by Backpack-From a Green Operation Perspective

In order to promote the development of low-carbon logistics and economize logistics distribution costs, the vehicle routing problem with split deliveries by backpack is studied. With the help of the model of classical capacitated vehicle routing problem, in this study, a form of discrete split deliveries was designed in which the customer demand can be split only by backpack. A double-objective mathematical model and the corresponding adaptive tabu search (TS) algorithm were constructed for solving this problem. By embedding the adaptive penalty mechanism, and adopting the random neighborhood selection strategy and reinitialization principle, the global optimization ability of the new algorithm was enhanced. Comparisons with the results in the literature show the effectiveness of the proposed algorithm. The proposed method can save the costs of low-carbon logistics and reduce carbon emissions, which is conducive to the sustainable development of low-carbon logistics.

[Site selection of nature reserve based on the self-learning tabu search algorithm with space-ecology set covering problem: An example from Daiyun Mountain, Southeast China]

Designing the nature reserves is an effective approach to protecting biodiversity. The traditional approaches to designing the nature reserves could only identify the core area for protecting the species without specifying an appropriate land area of the nature reserve. The site selection approaches, which are based on mathematical model, can select part of the land from the planning area to compose the nature reserve and to protect specific species or ecosystem. They are useful approaches to alleviating the contradiction between ecological protection and development. The existing site selection methods do not consider the ecological differences between each unit and has the bottleneck of computational efficiency in optimization algorithm. In this study, we first constructed the ecological value assessment system which was appropriated for forest ecosystem and that was used for calculating ecological value of Daiyun Mountain and for drawing its distribution map. Then, the Ecological Set Covering Problem (ESCP) was established by integrating the ecological values and then the Space-ecology Set Covering Problem (SSCP) was generated based on the spatial compactness of ESCP. Finally, the STS algorithm which possessed good optimizing performance was utilized to search the approximate optimal solution under diverse protection targets, and the optimization solution of the built-up area of Daiyun Mountain was proposed. According to the experimental results, the difference of ecological values in the spatial distribution was obvious. The ecological va-lue of selected sites of ESCP was higher than that of SCP. SSCP could aggregate the sites with high ecological value based on ESCP. From the results, the level of the aggregation increased with the weight of the perimeter. We suggested that the range of the existing reserve could be expanded for about 136 km² and the site of Tsuga longibracteata should be included, which was located in the northwest of the study area. Our research aimed at providing an optimization scheme for the sustai-nable development of Daiyun Mountain nature reserve and the optimal allocation of land resource, and a novel idea for designing the nature reserve of forest ecosystem in China.

Identifying Regions Based on Flexible User Defined Constraints

The identification of regions is both a computational and conceptual challenge. Even with growing computational power, regionalization algorithms must rely on heuristic approaches in order to find solutions. Therefore, the constraints and evaluation criteria that define a region must be translated into an algorithm that can efficiently and effectively navigate the solution space to find the best solution. One limitation of many existing regionalization algorithms is a requirement that the number of regions be selected a priori. The max-p algorithm, introduced in Duque et al. (2012), does not have this requirement, and thus the number of regions is an output of, not an input to, the algorithm. In this paper we extend the max-p algorithm to allow for greater flexibility in the constraints available to define a feasible region, placing the focus squarely on the multidimensional characteristics of region. We also modify technical aspects of the algorithm to provide greater flexibility in its ability to search the solution space. Using synthetic spatial and attribute data we are able to show the algorithm's broad ability to identify regions in maps of varying complexity. We also conduct a large scale computational experiment to identify parameter settings that result in the greatest solution accuracy under various scenarios. The rules of thumb identified from the experiment produce maps that correctly assign areas to their "true" region with 94% average accuracy, with nearly 50 percent of the simulations reaching 100 percent accuracy.

A Tabu-Search Heuristic for Deterministic Two-Mode Blockmodeling of Binary Network Matrices

Two-mode binary data matrices arise in a variety of social network contexts, such as the attendance or non-attendance of individuals at events, the participation or lack of participation of groups in projects, and the votes of judges on cases. A popular method for analyzing such data is two-mode blockmodeling based on structural equivalence, where the goal is to identify partitions for the row and column objects such that the clusters of the row and column objects form blocks that are either complete (all 1s) or null (all 0s) to the greatest extent possible. Multiple restarts of an object relocation heuristic that seeks to minimize the number of inconsistencies (i.e., 1s in null blocks and 0s in complete blocks) with ideal block structure is the predominant approach for tackling this problem. As an alternative, we propose a fast and effective implementation of tabu search. Computational comparisons across a set of 48 large network matrices revealed that the new tabu-search heuristic always provided objective function values that were better than those of the relocation heuristic when the two methods were constrained to the same amount of computation time.

Evolutionary tabu search strategies for the simultaneous registration of multiple atomic structures in cryo-EM reconstructions

A structural characterization of multi-component cellular assemblies is essential to explain the mechanisms governing biological function. Macromolecular architectures may be revealed by integrating information collected from various biophysical sources - for instance, by interpreting low-resolution electron cryomicroscopy reconstructions in relation to the crystal structures of the constituent fragments. A simultaneous registration of multiple components is beneficial when building atomic models as it introduces additional spatial constraints to facilitate the native placement inside the map. The high-dimensional nature of such a search problem prevents the exhaustive exploration of all possible solutions. Here we introduce a novel method based on genetic algorithms, for the efficient exploration of the multi-body registration search space. The classic scheme of a genetic algorithm was enhanced with new genetic operations, tabu search and parallel computing strategies and validated on a benchmark of synthetic and experimental cryo-EM datasets. Even at a low level of detail, for example 35-40 A, the technique successfully registered multiple component biomolecules, measuring accuracies within one order of magnitude of the nominal resolutions of the maps. The algorithm was implemented using the Sculptor molecular modeling framework, which also provides a user-friendly graphical interface and enables an instantaneous, visual exploration of intermediate solutions.