Synthesis,structure, CO oxidation,and H2 production activities of CaCu3−xMnxTi4−xMnxO12 (x = 0, 0.5, and 1.0)

Synthesis of nanocrystalline pristine and Mn-doped calcium copper titanate quadruple perovskites, CaCu_3?xMn_xTi_4?xMn_xO₁₂ (x = 0, 0.5, and 1.0) by modified citrate solution combustion method has been reported. Powder X-ray diffraction patterns attest the phase purity of the perovskite materials. Average particle sizes of all the materials obtained from the Scherrer's formula are in the range of 55–70 nm. The specific surface areas for all the perovskites obtained from BET isotherms are found to be low as expected for the condensed oxide systems and fall in the range of 13–17 m² g^?1. Transmission electron microscopy studies show a reduction in particle size of CaCu₃Ti₄O₁₂ with increase in Mn doping. Ca and Ti are present in +2 and +4 oxidation states in all the materials as demonstrated by X-ray photoelectron spectroscopy analyses. Cu²⁺ gets reduced in CaCu₃Ti₄O₁₂ with higher Mn content. Mn is observed to be present only in +3 oxidation state. All the materials have been examined to be active in CO oxidation as well as H₂ production from methanol steam reforming. CaCu₃Ti₄O₁₂ with ~14 at.% Mn is found to show best catalytic activities among these materials. A comprehensive analysis of the catalytic activities of these perovskites toward CO oxidation and H₂ production from MSR reveal the cooperative activity of copper-manganese in the doped perovskites and it is more effective at lower manganese content.     

Coverage optimization algorithm based on VF-CS in mobile sensor network

A random placement of large-scale sensor network in the outdoor environment often causes low coverage.An area coverage optimization algorithm of mobile sensor network (MSN) based on virtual force perturbation and Cuckoo search (VF-CS) was proposed.Firstly,the virtual force of the sensor nodes within the Thiessen polygon was analyzed based on the partitioning of Voronoi diagram of the monitoring area.Secondly,the force of polygon vertices and neighbor nodes was taken as the perturbation factor for updating the node’s location of the Cuckoo search (CS).Finally,the VF-CS guided the node to move so as to achieve the optimal coverage.The simulation results demonstrate that the proposed algorithm has higher coverage and shorter average moving distance of nodes than the Voronoi diagram based algorithms in literatures.     

考虑交叉销售的O2O供应链合作广告策略研究

基于网络零售平台与制造商的合作广告实践，运用Stackelberg博弈理论研究了平台交叉销售效应及合作广告引导作用对O2O供应链合作广告决策的影响，并通过数值算例对参与合作广告计划前后渠道成员的决策变量均衡结果和利润进行对比分析，研究发现平台交叉销售效应是影响网络零售平台合作广告计划制定和实施的关键因素，只有当其达到一定水平时，制造商才拥有参与网络零售平台合作广告计划的资格；在一定条件时，合作广告计划能够实现制造商和网络零售平台利润的提升；当平台交叉销售效应较大时，网络零售平台获得的利润大于制造商。     

Relevance- and interface-driven clustering for visual information retrieval

Search results of spatio-temporal data are often displayed on a map, but when the number of matching search results is large, it can be time-consuming to individually examine all results, even when using methods such as filtered search to narrow the content focus. This suggests the need to aggregate results via a clustering method. However, standard unsupervised clustering algorithms like $K$-means (i) ignore relevance scores that can help with the extraction of highly relevant clusters, and (ii) do not necessarily optimize search results for purposes of visual presentation. In this article, we address both deficiencies by framing the clustering problem for search-driven user interfaces in a novel optimization framework that (i) aims to maximize the relevance of aggregated content according to cluster-based extensions of standard information retrieval metrics and (ii) defines clusters via constraints that naturally reflect interface-driven desiderata of spatial, temporal, and keyword coherence that do not require complex ad-hoc distance metric specifications as in $K$-means. After comparatively benchmarking algorithmic variants of our proposed approach – RadiCAL – in offline experiments, we undertake a user study with 24 subjects to evaluate whether RadiCAL improves human performance on visual search tasks in comparison to $K$-means clustering and a filtered search baseline. Our results show that (a) our binary partitioning search (BPS) variant of RadiCAL is fast, near-optimal, and extracts higher-relevance clusters than $K$-means, and (b) clusters optimized via RadiCAL result in faster search task completion with higher accuracy while requiring a minimum workload leading to high effectiveness, efficiency, and user satisfaction among alternatives.     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

Large neighborhood search for the bike request scheduling problem

This paper introduces an efficient algorithm for the bike request scheduling problem (BRSP). The BRSP is built around the concept of request, defined as the pickup or dropoff of a number of identical items (bikes) at a specific station, within a certain time window, and with a certain priority. The aim of the BRSP is to sequence requests on (and hence determine the routes of) a set of vehicles, in such a way that the sum of the priorities of the executed requests is maximized, all time windows are respected, and the capacity of the vehicles is not exceeded. The generation of the set of requests is explicitly not a part of the problem definition of the BRSP. The primary application of the BRSP, from which it derives its name, is to determine the routes of a set of repositioning vehicles in a bike sharing system, although other applications exist. The algorithm introduced in this paper is based on a set of related greedy randomized adaptive search procedure followed by variable neighborhood descent (GRASP + VND) operators embedded in a large neighborhood search (LNS) framework. Since this paper presents the first heuristic for the BRSP, a computational comparison to existing approaches is not possible. We therefore compare the solutions found by our LNS heuristic to those found by an exact solver (Gurobi). These experiments confirm that the proposed algorithm scales to realistic dimensions and is able to find near‐optimal solutions in seconds.     

An intelligent EGWO-SCA-CS algorithm for PSS parameter tuning under system uncertainties

This paper proposes a novel hybrid technique called enhanced grey wolf optimization-sine cosine algorithm-cuckoo search (EGWO-SCA-CS) algorithm to improve the electrical power system stability. The proposed method comprises of a popular grey wolf optimization (GWO) in an enhanced and hybrid form. It embraces the well-balanced exploration and exploitation using the cuckoo search (CS) algorithm and enhanced search capability through the sine cosine algorithm (SCA) to elude the stuck to the local optima. The proposed technique is validated with the 23 benchmark functions and compared with state-of-the-art methods. The benchmark functions consist of unimodal, multimodal function from which the best suitability of the proposed technique can be identified. The robustness analysis also presented with the proposed method through boxplot, and a detailed statistical analysis is performed for a set of 30 individual runs. From the inferences gathered from the benchmark functions, the proposed technique is applied to the stability problem of a power system, which is heavily stressed with the nonlinear variation of the load and thereby operating conditions. The dynamics of power system components have been considered for the mathematical model of a multimachine system, and multiobjective function has been framed in tuning the optimal controller parameters. The effectiveness of the proposed algorithm has been assessed by considering two case studies, namely, (i) the optimal controller parameter tuning, and (ii) the coordination of oscillation damping devices in the power system stability enhancement. In the first case study, the power system stabilizer (PSS) is considered as a controller, and a self-clearing three-phase fault is considered as the system uncertainty. In contrast, static synchronous compensator (STATCOM) and PSS are considered as controllers to be coordinated, and perturbation in the system states as uncertainty in the second case study.     

Simultaneous identification of structural stiffness and mass parameters based on Bare-bones Gaussian Tree Seeds Algorithm using time-domain data

This paper mainly illustrates the Tree Seeds Algorithm (TSA) to tackle structural damage identification problem. The damage model is simulated by the alterations of both stiffness and mass parameters. The objective function is introduced by minimizing the differences between the measured and calculated acceleration data. To enhance the performance of the standard TSA, two modifications including the bare-bones Gaussian updated mechanism and the withering process are introduced. The modified algorithm is named after the BGTSA. In the numerical simulation part, the BGTSA is firstly used to make comparisons with several state-of-the-art algorithms on the CEC05. Secondly, the BGTSA is utilized to deal with the structural damage identification problem by optimizing the acceleration-based nonlinear objective function. Numerical experiments involving a simply supported beam and a truss are carried out to verify the effectiveness of the proposed algorithm. The final results show that with low amount of acceleration data, the BGTSA can acquire better identification results compared with other evolutionary algorithms. Therefore the proposed algorithm could be viewed as a potential tool to solve the structural damage identification problem.     

基于k-shell的复杂网络最短路径近似算法

复杂网络最短路径经典算法的处理效率较低，不适用于大规模复杂网络，而现有近似算法通用性有限，且计算准确率不理想，不能满足规模日益扩大的复杂网络中的最短路径计算需求。针对于此，提出基于[k]-shell的复杂网络最短路径近似算法。算法利用节点的[k]-shell值进行网络划分并引导搜索路径，利用超点聚合处理[k]-shell子网来降低路径搜索中节点和连边的规模，通过在路径搜索过程使用双向搜索树方法提高算法的计算效率和准确率。实验结果表明，算法通用性较好，在现实与仿真大规模复杂网络中均具有较高的计算效率和准确率。     

A similarity hybrid harmony search algorithm for the Team Orienteering Problem

The Harmony Search (HS) is a metaheuristic algorithm, which is inspired from the composition of music harmonies. The functionality and flexibility of HS contribute to the development of successful methodologies for different kind of scientific problems. The aim of this paper is to propose a variant of the classic HS algorithm in order to provide competitive solutions for the Team Orienteering Problem (TOP). We introduce the Similarity Hybrid Harmony Search (SHHS) algorithm as an alternative and innovative optimization method. The SHHS follows the standard procedure of HS with some modifications and includes a new strategy called “similarity process”. Two versions of the proposed method have been developed, the static version with predefined values for the parameters of the method and the dynamic one with dynamic adjustment of the parameters. The SHHS algorithm is applied to the known benchmark instances of TOP. The dynamic of the algorithm is tested through a complete solution analysis which gives the superiority of the dynamic version compared to the static one. The results of both versions of the proposed algorithm indicate the positive performance against other effective and robust optimization algorithms from the literature.