Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

A constrained portfolio selection model at considering risk-adjusted measure by using hybrid meta-heuristic algorithms

Portfolio selection is a key issue in the business world and financial fields. This article presents a new decision making method of portfolio optimization (PO) issues in different risk measures by using new evolutionary computing method and cardinality constrains which is mentioned as hybrid meta-heuristic algorithms. Based on mean–variance (MV) Method by Markowitz we collected three risk levels; mean absolute deviation (MAD), semi variance (SV) and variance with skewness (VWS). The developed algorithms are Electromagnetism-like algorithm (EM), particle swarm optimization (PSO), genetic algorithm (GA), genetic network programming (GNP) and simulated annealing (SA). Also a diversification mechanism strategy is implemented and hybridized with the developed algorithms to increase the diversity and overcome local optimality. The sustainability of this proposed model is verified by 50 factories on the Iranian stock exchange. Finally, experimental results of proposed algorithms with cardinality constraint are compared with each other by four effective metrics in which the algorithms performance for achieving the optimal solution discussed. In addition, we have done the analysis of variance technique to confirm the validity and accurately analyze of the results which the success of this method was proved.     

An improved method for the hot strip mill production scheduling problem

In most research on the hot strip mill production scheduling problem (HSMPSP) arising in the steel industry, it is accepted that a schedule with lower penalty caused by jumps of width, hardness, and gauge will result in lower roller wear, so it is regarded as a better schedule. However, based on the analysis of production processes, it is realised that rolling each coil also cause roller wear. In order to assessing the roller wear associated with production scheduling more precisely, it is necessary to consider it as another factor besides those jumps, especially when complicated constraints are involved. In this paper, an improved method is proposed to quantify the expected wear of the rollers done by those jumps and rolling processes. Then the HSMPSP whose objective is to maximise the total length of all scheduled coils is formulated as a team orienteering problem with time windows and additional production constraints. A heuristic method combining an improved Ant Colony Extended algorithm with local search procedures dedicated to HSMPSP is developed. Finally, computational results on instances generated based on production data from an integrated steel mill in China indicate that the proposed algorithm is a promising solution specific to HSMPSP.     

Diagnosis of the surface morphology of laser-ablated materials using the weighted- DCT approach in laser speckle interferometry for application to plasma facing materials

To implement on-line, real-time monitoring for the surface morphology of Plasma-Facing Materials (PFMs) in tokamak, we developed a Laser Speckle Interferometry measurement approach. A laser ablation method was used to simulate the erosion process during Plasma-Wall Interactions in a tokamak. In the present investigation, we evaluated the results of laser ablation morphology changes on the surface of Mo material reconstructed by four different approaches (Flood-fill, Quality-guided, Discrete Cosine Transform (DCT) and Weighted-DCT). The morphology results measured by the weighted-DCT approach are very close to the measurement results from confocal microscopy with an average error rate within 7%. It is verified that the weighted-DCT algorithm has high accuracy and can efficiently reduce the influence of noise pollution coming from laser ablation, which is used as a proxy for erosion from plasma wall interaction. Additionally, the CPU computer time has been shortened. This is of great significance for the real-time monitoring of PFMs’ morphology in the Experimental Advanced Superconducting Tokamak (EAST) in the future.     

国内农用拖拉机自动转向系统研究现状

作为精准农业的重要组成部分,农用拖拉机自动转向系统的研究也越来越多。通过对相关文献进行梳理和分析,分别从自动转向系统的两个组成部分(自动转向执行机构和自动转向控制系统)阐述国内农用拖拉机自动转向系统的研究成果,为设计合理的自动转向执行机构和稳定准确的自动转向控制系统提供建议和参考。     

Cost-based holistic twig joins

An evaluation of XML queries such as XQuery or XPath expressions represents a challenging task due to its complexity. Many algorithms have been introduced to cope with this problem. Some of them, called binary joins, evaluate separated parts of a query and subsequently merge intermediate results, while the others, called holistic twig joins, evaluate a query as a whole. Moreover, these algorithms also differ in what index data structure they use to handle XML data. There exist cost-based approaches utilizing binary joins and various index data structures; however, they share a limitation. The limitation is that they cannot perform a join between query nodes not having a direct XPath relationship. Such a join can be advantageous especially if their joint selectivity is high. Since holistic joins work with all query nodes they overcome this limitation. In this article, we introduce such a holistic twig join called CostTwigJoin. To the best of our knowledge, CostTwigJoin is the first holistic join capable of combining various index data structures during an evaluation of an XML query. Usage of the holistic join has yet another advantage for cost-based approaches: an optimizer does not have to resolve the order of binary joins; therefore, the search space is reduced. In this article, we perform thorough experiments on hundreds of queries to evaluate our approach and demonstrate its advantages.     

Quantum computation based bundling optimization for combinatorial auction in freight service procurements

Combinatorial auction is a useful trade manner for transportation service procurements in e-marketplaces. To enhance the competition of combinatorial auction, a novel auction mechanism of two-round bidding with bundling optimization is proposed. As the recommended the auction mechanism, the shipper/auctioneer integrates the objects into several bundles based on the bidding results of first round auction. Then, carriers/bidders bid for the object bundles in second round. The bundling optimization is described as a multi-objective model with two criteria on price complementation and combination consistency. A Quantum Evolutionary Algorithm (QEA) with β-based rotation gate and the encoding scheme based on non-zero elements in complementary coefficient matrix is developed for the model solution. Comparing with a Contrast Genetic Algorithm, QEA can achieve better computational performances for small and middle size problems.     

Generating synthetic benchmark circuits for accelerated life testing of field programmable gate arrays using genetic algorithm and particle swarm optimization

Accelerated life testing (ALT) of a field programmable gate array (FPGA) requires it to be configured with a circuit that satisfies multiple criteria. Hand-crafting such a circuit is a herculean task as many components of the criteria are orthogonal to each other demanding a complex multivariate optimization. This paper presents an evolutionary algorithm aided by particle swarm optimization methodology to generate synthetic benchmark circuits (SBC) that can be used for ALT of FPGAs. The proposed algorithm was used to generate a SBC for ALT of a commercial FPGA. The generated SBC when compared with a hand-crafted one, demonstrated to be more suitable for ALT, measured in terms of meeting the multiple criteria. The SBC generated by the proposed technique utilizes 8.37% more resources; operates at a maximum frequency which is 40% higher; and has 7.75% higher switching activity than the hand-crafted one reported in the literature. The hand-crafted circuit is very specific to the particular device of that family of FPGAs, whereas the proposed algorithm is device-independent. In addition, it took several man months to hand-craft the SBC, whereas the proposed algorithm took less than half-a-day.     

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance.     

融合时间隐语义填充和子群划分的推荐算法

随着个性化推荐技术的发展，推荐系统面临着越来越多的挑战。传统的推荐算法通常存在数据稀疏性和推荐精度低等问题。针对以上问题，提出了一种融合时间隐语义填充和子群划分的推荐算法[K]-TLFM（Time Based Latent Factor Model Integrated with [k]-means）。该算法利用融合时间因素的隐语义模型对原始用户物品评分矩阵缺失项进行填充，避免了用全局平均值或者用户/物品平均值补全矩阵带来的误差，有效缓解了数据稀疏性问题，同时融合时间因素有效地刻画了用户偏好随时间的变化；完成评分矩阵缺失项填充后，基于二分[k]-means聚类算法将偏好、兴趣特征相似的对象划分到同一个子群中，在目标用户所属的子群中基于选定的协同过滤算法为用户产生推荐列表，提高了推荐效率和准确性。在MovieLens和Netflix数据集上对该算法的推荐性能进行了对比实验，结果表明该算法具有更高的推荐精度。