A Scheme of Selecting Vehicles to Assist Download Based on WebGIS for VANET

The assisted download is an effective method solving the problem that the coverage range is insufficient when Wi-Fi access is used in VANET. For the low utilization of time-space resource within blind area and unbalanced download services in VANET, this paper proposes an approximate global optimum scheme to select vehicle based on WebGIS for assistance download. For WebGIS, this scheme uses a two-dimensional matrix to respectively define the time-space resource and the vehicle selecting behavior, and uses Markov Decision Process to solve the problem of time-space resource allocation within blind area, and utilizes the communication features of VANET to simplify the behavior space of vehicle selection so as to reduce the computing complexity. At the same time, Euclidean Distance(Metric) and Manhattan Distance are used as the basis of vehicle selection by the proposed scheme so that, in the case of possessing the balanced assisted download services, the target vehicles can increase effectively the total amount of user downloads. Experimental results show that because of the wider access range and platform independence of WebGIS, when user is in the case of relatively balanced download services, the total amount of downloads is increased by more than 20%. Moreover, WebGIS usually only needs to use Web browser (sometimes add some plug-ins) on the client side, so the system cost is greatly reduced.

Lower limb exoskeleton robots’ dynamics parameters identification based on improved beetle swarm optimization algorithm

Efficient and high-precision identification of dynamic parameters is the basis of model-based robot control. Firstly, this paper designed the structure and control system of the developed lower extremity exoskeleton robot. The dynamics modeling of the exoskeleton robot is performed. The minimum parameter set of the identified parameters is determined. The dynamic model is linearized based on the parallel axis theory. Based on the beetle antennae search algorithm (BAS) and particle swarm optimization (PSO), the beetle swarm optimization algorithm (BSO) was designed and applied to the identification of dynamic parameters. The update rule of each particle originates from BAS, and there is an individual’s judgment on the environment space in each iteration. This method does not rely on the historical best solution in the PSO and the current global optimal solution of the individual particle, thereby reducing the number of iterations and improving the search speed and accuracy. Four groups of test functions with different characteristics were used to verify the performance of the proposed algorithm. Experimental results show that the BSO algorithm has a good balance between exploration and exploitation capabilities to promote the beetle to move to the global optimum. Besides, the test was carried out on the exoskeleton dynamics model. This method can obtain independent dynamic parameters and achieve ideal identification accuracy. The prediction result of torque based on the identification method is in good agreement with the ideal torque of the robot control.

Hybridization of PSO for the Optimal Coordination of Directional Overcurrent Protection Relays

In the electrical power system, the coordination of directional overcurrent protection relays (DOPR) plays a preeminent role in protecting the electrical power system with the help of primary and back up protection to keep the system vigorous and to avoid unnecessary interruption. The coordination between these relays should be pursued at optimal value to minimize the total operating time of all main relays. The coordination of directional overcurrent relay is a highly constrained optimization problem. The DOPR problem has been solved by using a hybridized version of particle swarm optimization (HPSO). The hybridization is achieved by introducing simulated annealing (SA) in original PSO to avoid being trapped in local optima and successfully searching for a global optimum solution. The HPSO has been successfully applied to five case studies. Furthermore, the obtained results outperform the other traditional and state of the art techniques in terms of minimizing the total operating of DOPR and convergence characteristics, and require less computational time to achieve the global optimum solution.

An approach to finding a global optimum in constrained clustering tasks involving the assessments of several experts

An approach to solving the constrained clustering problem has been developed, based on the aggregation of data obtained as a result of evaluating the characteristics of clustered objects by several independent experts, and the analysis of alternative variants of clustering by constraint programming methods using original heuristics. Objects clusterized are represented as multisets, which makes it possible to use appropriate methods of aggregation of expert opinions. It is proposed to solve the constrained clustering problem as a constraint satisfaction problem. The main attention is paid to the issue of reducing the number and simplifying the constraints of the constraint satisfaction problem at the stage of its formalization. Within the framework of the approach, we have created: a) a method for estimating the optimal value of the objective function by hierarchical clustering of multisets, taking into account a priori constraints of the subject domain, and b) a method for generating additional constraints on the desired solution in the form of “smart tables”, based on the obtained estimate. The approach allows us to find the best partition in the problems of the class under consideration, which are characterized by a high dimension.

A new global optimum solution model based on multi-value dynamic programming for the cut-off grade optimization in a surface mining complex

In techno-economic concern, cut-off grade (COG) optimization is the key for efficient mineral liquidation from thehuge metalliferous surface mining sector. In this paper, a sequentially advancing algorithm based on discretemulti-value dynamic programming (MDP) has been developed to calculate the global optimum COG of alarge-scale open-pit metalliferous deposit. The proposed COG optimization algorithm aims to overcome thelimitations of straightforward classical techniques in determining the optimum COG. This discrete COG-MDPmodel is the first of its kind and has the novelty of dealing with the simulation of eight dynamic possibilities toachieve the maximal global Net Present Value (NPV). A high-level programming language (Python) has been usedto develop the computer model to deal with the complexity of handling a minimum of 500 series of dynamicvariables. This model can generate results in polynomial-time from the complex of mining, milling, and smeltingand refining system corresponding to various limiting conditions. The prime objective considered in the model isto optimize the COG of a metalliferous deposit. A working open-pit copper mining complex from India has beenused to validate the model. In this study, the optimum COG for the Malanjkhand copper deposit has been found tobe (0.33%, 0.23%, 0.52%, 0.26%, 0.27%, 0.22%, 0.24%) with a maximum NPV of ₹ (12204, 14653, 16948, 14609,21454, 26717, 38821) million corresponding to various scenarios. The findings also show that the present valuegradually hits zero after the project’s life cycle, confirming the typical pattern of other mining firms.

Parametric Optimization of Integrated Circuit Assembly Process: An Evolutionary Computing-Based Approach

Strict demands for very tight tolerances and increasing complexity in the semiconductors’ assembly impose a need for an accurate parametric design that deals with multiple conflicting requirements. This paper presents application of the advanced optimization methodology, based on evolutionary algorithms (EAs), on two studies addressing parametric optimization of the wire bonding process in the semiconductors’ assembly. The methodology involves statistical pre-processing of the experimental data, followed by an accurate process modeling by artificial neural networks (ANNs). Using the neural model, the process parameters are optimized by four metaheuristics: the two most commonly used algorithms – genetic algorithm (GA) and simulated annealing (SA), and the two newly designed algorithms that have been rarely utilized in semiconductor assembly optimizations – teaching-learning based optimization (TLBO) and Jaya algorithm. The four algorithm performances in two wire bonding studies are benchmarked, considering the accuracy of the obtained solutions and the convergence rate. In addition, influence of the algorithm hyper-parameters on the algorithms effectiveness is rigorously discussed, and the directions for the algorithm selection and settings are suggested. The results from two studies clearly indicate superiority of the TLBO and Jaya algorithms over GA and SA, especially in terms of the solution accuracy and the built-in algorithm robustness. Furthermore, the proposed evolutionary computing-based optimization methodology significantly outperforms the four frequently used methods from the literature, explicitly demonstrating effectiveness and accuracy in locating global optimum for delicate optimization problems.

Optimal Economic–Environmental Operation of BESS in AC Distribution Systems: A Convex Multi-Objective Formulation

This paper deals with the multi-objective operation of battery energy storage systems (BESS) in AC distribution systems using a convex reformulation. The objective functions are CO2 emissions, and the costs of the daily energy losses are considered. The conventional non-linear nonconvex branch multi-period optimal power flow model is reformulated with a second-order cone programming (SOCP) model, which ensures finding the global optimum for each point present in the Pareto front. The weighting factors methodology is used to convert the multi-objective model into a convex single-objective model, which allows for finding the optimal Pareto front using an iterative search. Two operational scenarios regarding BESS are considered: (i) a unity power factor operation and (ii) a variable power factor operation. The numerical results demonstrate that including the reactive power capabilities in BESS reduces 200kg of CO2 emissions and USD 80 per day of operation. All of the numerical validations were developed in MATLAB 2020b with the CVX tool and the SEDUMI and SDPT3 solvers.

Using the modified k-mean algorithm with an improved teaching-learning-based optimization algorithm for feedforward neural network training

In this paper we proposed a novel procedure for training a feedforward neural network. The accuracy of artificial neural network outputs after determining the proper structure for each problem depends on choosing the appropriate method for determining the best weights, which is the appropriate training algorithm. If the training algorithm starts from a good starting point, it is several steps closer to achieving global optimization. In this paper, we present an optimization strategy for selecting the initial population and determining the optimal weights with the aim of minimizing neural network error. Teaching-learning-based optimization (TLBO) is a less parametric algorithm rather than other evolutionary algorithms, so it is easier to implement. We have improved this algorithm to increase efficiency and balance between global and local search. The improved teaching-learning-based optimization (ITLBO) algorithm has added the concept of neighborhood to the basic algorithm, which improves the ability of global search. Using an initial population that includes the best cluster centers after clustering with the modified k-mean algorithm also helps the algorithm to achieve global optimum. The results are promising, close to optimal, and better than other approach which we compared our proposed algorithm with them.

Improved Modified Symbiosis Organisms Search (IMSOS): A New and Adaptive Approach for Determining Model Parameters from Geoelectrical Data

Symbiotic Organisms Search (SOS) is a global optimization algorithm inspired by the natural synergy between the organisms in an ecosystem. The interactive behavior among organisms in nature simulated in SOS consists of mutualism, commensalism, and parasitism strategies to find the global optimum solution in the search space. The SOS algorithm does not require a tuning parameter, which is usually used to balance explorative and exploitative search by providing posterior sampling of the model parameters. This paper proposes an improvement of the Modified SOS (MSOS) algorithm, called IMSOS, to enhance exploitation along with exploration strategies via a modified parasitism vector. This improves the search efficiency in finding the global minimum of two multimodal testing functions. Furthermore, the algorithm is proposed for solving inversion problems in geophysics. The performance of IMSOS was tested on the inversion of synthetic and field data sets from self-potential (SP) and vertical electrical sounding (VES) measurements. The IMSOS results were comparable to those of other global optimization algorithms, including the Particle Swarm Optimization, the Differential Evolution and the Black Holes Algorithms. IMSOS accurately determined the model parameters and their uncertainties. It can be adapted and can potentially be used to solve the inversion of other geophysical data as well.

A novel meta-heuristic optimization algorithm based on cell division: Cell Division Optimizer

A novel meta-heuristic algorithm named as the Cell Division Optimizer (CDO) is proposed. The proposed algorithm is inspired by the reproduction methods at the cellular level, which is formulated by the well-known cell division process known as mitosis and meiosis. In the proposed model Meiosis and Mitosis govern the exploration and exploitation aspects of the optimization algorithm, respectively. In the proposed method, the solutions are updated in two phases to achieve the global optimum solution. The proposed algorithm can be easily adopted to solve the combinatorial optimization method. To evaluate the proposed method, 50 well-known benchmark test functions and also 2 classical engineering optimization problems including 1 mechanical engineering problem and 1 electrical engineering problem are employed. The results of the proposed method are compared with the latest versions of state-of-the-art algorithms like Particle Swarm Optimization, Cuckoo Search, Grey Wolf Optimizer, FruitFly Optimization, Whale Optimizer, Water-Wave Optimizer and recently proposed variants of top-performing algorithms like SHADE (success history-based adaptive differential evolution) and CMAES (Covariance matrix adaptation evolution strategy). Moreover, the convergence speed of the proposed algorithm is better than the considered competitive methods in most cases.