OVRP_GELS: solving open vehicle routing problem using the gravitational emulation local search algorithm

In open vehicle routing problem (OVRP), after delivering service to the last customer, the vehicle does not necessarily return to the initial depot. This type of problem originally defined about thirty years ago and still is an open issue. In real life, the OVRP is similar to the delivering newspapers and consignments. The problem of service delivering to a set of customers is a particular open VRP with an identical fleet for transporting vehicles that do not necessarily return to the initial depot. Contractors which are not the employee of the delivery company use their own vehicles and do not return to the depot. Solving the OVRP means to optimize the number of vehicles, the traveling distance and the traveling time of a vehicle. In time, several algorithms such as tabu search, deterministic annealing and neighborhood search were used for solving the OVRP. In this paper, a new combinatorial algorithm named OVRP_GELS based on gravitational emulation local search algorithm for solving the OVRP is proposed. We also used record-to-record algorithm to improve the results of the GELS. Several numerical experiments show a good performance of the proposed method for solving the OVRP when compared with existing techniques.

     

New delay range–dependent stability criteria for interval time‐varying delay systems via Wirtinger‐based inequalities

Stability conditions for time‐delay systems using the Lyapunov‐based methodologies are generically expressed in terms of linear matrix inequalities. However, due to assuming restrictive conditions in deriving the linear matrix inequalities, the established stability conditions can be strictly conservative. This paper attempts to relax this problem for linear systems with interval time‐varying delays. A double‐integral inequality is derived inspired by Wirtinger‐based single‐integral inequality. Using the advanced integral inequalities, the reciprocally convex combination techniques and necessary slack variables, together with extracting a condition for the positive definiteness of the Lyapunov functional, novel stability criteria, have been established for the system. The effectiveness of the criteria is evaluated via 2 numerical examples. The results indicate that more complex stability criteria not only improve the stability region but also bring computational expenses.     

Simultaneous size and geometry optimization of steel trusses under dynamic excitations

During the past decades, the main focus of the research in steel truss optimization has been tailored towards optimal design under static loading conditions and limited work has been devoted to investigating the optimum structural design considering dynamic excitations. This study addresses the simultaneous size and geometry optimization problem of steel truss structures subjected to dynamic excitations. Using the well-known big bang-big crunch algorithm, the minimum-weight design of steel trusses is conducted under both periodic and non-periodic excitations. In the case of periodic excitations, in order to examine the effect of the exciting period of the dynamic load on the final results, the design instances are optimized under different exciting periods and the obtained results are compared. It is observed that by increasing the excitation period of the considered sinusoidal loading as well as the finite rise time of the non-periodic step force, the optimization results approach the minimum design weight obtained under the static loading counterpart. However, in the case of the studied rectangular periodic excitation, the results obtained do not approach the optimum design associated with the static loading case even for higher values of the exciting period.     

Pre-bent shape design of full free-form curved beams using isogeometric method and semi-analytical sensitivity analysis

In this paper, isogeometric analysis (IGA) is employed to solve the problem of a curved beam with free-form geometry, arbitrary loading, and variable flexural/axial rigidity. The main objective of the study is to develop a unified approach for full free-from curved beam problems that can be integrated with a newly developed semi-analytical sensitivity analysis to solve pre-bent shape design problems. The required set of B-spline control points are calculated using an interpolation technique based on chord-length parameterization. The one-to-one correspondence is considered for parameters of the geometry, loading, and rigidity which is proven to have extreme importance. An IGA curved beam element is suggested based on the Euler-Bernoulli beam theory for the general curvilinear coordinate. The validity and effectiveness of the proposed formulation is confirmed by application to a variety of examples. Moreover, three shape optimization examples are taken into consideration. In the first two examples, the pre-bent shapes of spiral and Tschinhausen curved beams with free-form geometry under distributed loading are obtained. In the third example, the pre-bending problem of wind turbine blades is addressed as an industrial example.     

A robust fuzzy multi-objective location-routing problem for hazardous waste under uncertain conditions

Industrialization and population growth have been accompanied by many problems such as waste management worldwide. Waste management and reduction have a vital role in national management. The presents study represents a multi-objective location-routing problem for hazardous wastes. The model was solved using Non dominated Sorting Genetic Algorithm-II, Multi-Objective Particle Swarm Optimization, Multi-Objective Invasive Weed Optimization, Pareto Envelope-based Selection Algorithm, Multi-Objective Evolutionary Algorithm Based on Decomposition and Multi-Objective Grey Wolf Optimizer algorithms. The findings revealed that the Multi-Objective Invasive Weed Optimization algorithm was the best and the most efficient among the algorithms used in this study. Obtaining income from the incineration of the wastes and reducing the risk of COVID-19 infection are the first innovation of the present study, which considered in the presented model. The second innovation is that uncertainty was considered for some of the crucial parameters of the model while the robust fuzzy optimization model was applied. Besides, the model was solved using several meta-heuristic algorithms such as Multi-Objective Invasive Weed Optimization, Multi-Objective Evolutionary Algorithm Based on Decomposition and Multi-Objective Grey Wolf Optimizer, which were rarely used in literature. Eventually, the most efficient algorithm was identified by comparing the considered algorithms.

     

Mapping urban land cover based on spatial-spectral classification of hyperspectral remote-sensing data

In this article, an innovative classification framework for hyperspectral image data, based on both spectral and spatial information, is proposed. The main objective of this method is to improve the accuracy and efficiency of high-resolution land-cover mapping in urban areas. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MMSF) algorithm. A pixel-based support vector machine (SVM) algorithm is first used to classify the hyperspectral image data, then the enhanced MMSF algorithm is applied in order to increase the accuracy of less accurately classified land-cover types. The enhanced MMSF algorithm is used as a binary classifier. These two classes are the low-accuracy class and remaining classes. Finally, the SVM algorithm is trained for classes with acceptable accuracy. In the proposed approach, namely MSF-SVM, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithms, and are then used to build the MSF. Three benchmark hyperspectral data sets are used for the assessment: Berlin, Washington DC Mall, and Quebec City. Experimental results demonstrate the superiority of the proposed approach compared with SVM and the original MMSF algorithms. It achieves approximately 5, 6, and 7% higher rates in kappa coefficients of agreement in comparison with the original MMSF algorithm for the Berlin, Washington DC Mall, and Quebec City data sets, respectively.     

Artificial neural network training utilizing the smooth variable structure filter estimation strategy

A multilayered neural network is a multi-input, multi-output nonlinear system in which network weights can be trained by using parameter estimation algorithms. In this paper, a novel training method is proposed. This method is based on the relatively new smooth variable structure filter (SVSF) and is formulated for feed-forward multilayer perceptron training. The SVSF is a state and parameter estimation that is based on the sliding mode concept and works in a predictor–corrector fashion. The SVSF training performance is tested on three benchmark pattern classification problems. Furthermore, a study is presented comparing the popular back-propagation method, the extended Kalman filter, and the SVSF.     

Effects of slots on thermoelastic quality factor of a vertical beam MEMS resonator

Thermoelastic damping is one of the dominant mechanisms of structural damping in vacuum-operated microresonators. A three dimensional numerical model based on the finite element method is used for simulating thermoelastic damping in clamped–clamped microelectromechanical beam resonators. In this regards, both simple and slotted beam are considered. To understand the effect of slot positions and sizes on the resonator performance, resonant frequency and thermoelastic quality factor are calculated for both simple and slotted beams for a wide range of beam length from 10 to 400 µm. Punching slots in the resonator beam reduces the stiffness and mass of the beam which affect the resonant frequency. In addition thermo-mechanical coupling mechanisms of the resonator are affected by the slots which improve the thermoelastic quality factor. For most of the beam lengths, it is shown that the slots at the beam-anchor interface region, where the strain is high, are more effectively enhanced the thermoelastic quality factor than one at the centre of the beam region. However, the highest resonance frequency is achieved with the slots at the center region.     

Information fusion between short term learning and long term learning in content based image retrieval systems

Content based image retrieval (CBIR) systems could provide more precise results by taking the user’s feedbacks into account. Two types of the relevance feedback learning paradigms are short term learning (STL) and long term learning (LTL). By using both STL and LTL, a collaborative CBIR system is proposed in this paper. The proposed system introduced three fusion methods: including fusion in retrieved images, fusion in ranks, and fusion in similarities to make cooperation between STL and LTL. The proposed fusion methods are examined in a CBIR system equipped with a proposed statistical semantic clustering (SSC) method of LTL. The SSC method works based on the concept of semantic categories of the images by clustering techniques and constructing a relevancy matrix between images and semantic categories. The results of the SSC method with the suggested fusion methods are compared with two state-of-the-art LTL methods, namely virtual feature based method and dynamic semantic clustering. Comparative results confirm the efficiency of the proposed method. Furthermore, experimental results demonstrate that for a unique LTL method, various fusion methods lead to different results.     

Multiple kernel representation and classification of multivariate satellite-image time-series for crop mapping

Multivariate satellite-image time-series (MSITS) are a valuable source of information for a wide range of agricultural applications. Image classification, one of the main applications of this type of data, is a challenging task. It is mainly because MSITS are generated by a complex interaction among several sources of information, which are known as the factors of variation. These factors contain different information with different levels of relevance to a classification task. Thus, a proper representation of MSITS data is required in order to extract and model the most useful information from these factors for classification purpose. To this end, this article proposes three multiple kernel representations of MSITS data. These representations extract the most classification-related information from these data through combining the basis kernels constructed from different factors of variation of the MSITS data. In the proposed representations, the combination of the basis kernels was achieved by using the multiple kernel learning algorithms. The efficiency of the proposed multiple kernel representations was evaluated based both on analysing the relevance of their kernels to the classification task and their classification performances. Two different MSITS data sets composed of 10 RapidEye imageries of an agricultural area were used to evaluate the performances of the proposed methods. In addition, the classification results of both MSITS using a single kernel were considered as the baseline for comparison. The results showed an increase of up to 14% in overall accuracy of the classification maps by using the multiple kernel representations. Moreover, these particular representations for classification of time-series observations were able to handle the undesirable effects in image data such as the presence of clouds and their shadows.