Competitive Scheme Research Articles

Muse: Multi-algorithm collaborative crystal structure prediction

The algorithm and testing of the Multi-algorithm-collaborative Universal Structure-prediction Environment (Muse) are detailed. Presently, in Muse I combined the evolutionary, the simulated annealing, and the basin hopping algorithms to realize high-efficiency structure predictions of materials under certain conditions. Muse is kept open and other algorithms can be added in future. I introduced two new operators, slip and twist, to increase the diversity of structures. In order to realize the self-adaptive evolution of structures, I also introduced the competition scheme among the ten variation operators, as is proved to further increase the diversity of structures. The symmetry constraints in the first generation, the multi-algorithm collaboration, the ten variation operators, and the self-adaptive scheme are all key to enhancing the performance of Muse. To study the search ability of Muse, I performed extensive tests on different systems, including the metallic, covalent, and ionic systems. All these present tests show that Muse has very high efficiency and 100% success rate.

Energy-Aware Distributed Clustering Algorithm for Improving Network Performance in WSNs

Wireless sensor networks (WSNs) consist of a large number of sensor nodes equipped with a diverse number of small and low-cost devices with limited resources, such as a short communication range, a low bandwidth, a small memory, and a restricted energy. In particular, among these constraint factors, a sensor node's energy consumption is a very important factor in extending a network's lifetime. Many researchers are focused on the energy efficiency of wireless sensor networks. Many clustering algorithms have been proposed to improve energy efficiency. However, most protocols in previous literature have the problem of not considering the characteristics of real applications, for example, forest fire detection, intruder detection, target tracking, and the like. In this paper, we propose an energy-efficient clustering algorithm that can respond rapidly to unexpected events with increased energy efficiency, because each sensor node detects events individually and creates clusters using a regional competition scheme. Simulation results show improved performance when our algorithm is used.

Competition, Cooperation and Collective Choice

The ability of groups to implement efficiency-enhancing institutions is emerging as a central theme of research in economics. This study explores voting on a scheme of intergroup competition, which facilitates cooperation in a social dilemma situation. Experimental results show that the competitive scheme fosters cooperation. Competition is popular but the electoral outcome depends strongly on specific voting rules of institutional choice. If the majority decide, competition is almost always adopted. If likely losers from competition have veto power, it is often not, and substantial gains in efficiency are foregone.

With or Without Siblings: Sorting into Competition in Experimental Chinese Labor Market

We examine whether Chinese only child and child with siblings, having similar ability as well as similar risk and social preferences, differ in their selection into a competitive environment. Participants in a laboratory experiment solve a real task, first under a non-competitive piece rate and then a competitive tournament incentive scheme. We find a significant differential sorting behavior into tournament between only child and child with siblings, even after controlling for gender, ability, risk attitude, social preference and other individual characteristics. Only child tends to underestimate her winning chances and is shy away from competition. But as only child get more confident, she will more embrace competition than her comparatively equivalent counterpart.

Agile Management of Complex Goods & Services Bundles for B2B E-Commerce by Global Narrow-Specialized Companies

Agile management of complex goods & services bundles for business-to-business E-Commerce may play a crucial role to facilitate efficiency and profitability of industrial companies. However, it appears to be essential to examine whether it prevails within different business strategies. Thus, within this article the usage and managing of E-Commerce by global narrow-specialized companies is analyzed, as this business strategy represents an interesting approach of competitive scheme in global markets of goods & services bundles. Though, the amount of the research papers devoted separately to the E-Commerce and global narrow-specialized companies management, is rather high, there is still limited information on how such companies use E-Commerce. Thereby, this article may contribute to the limited knowledge regarding agile management of E-Commerce by global narrow-specialized companies. Moreover, the research may also have managerial implications. The knowledge gained during the research could be useful for other companies. And hence, it could help managers of global narrow-specialized companies to make decisions on investments and execution of E-Commerce strategy.

Tax Competition and Federal Equalization Schemes with Decentralized Leadership

In a federation featuring decentralized leadership, regional governments compete by setting capital taxes in anticipation of the central government's fiscal-equalization and income-redistribution policies. As a benchmark, it is first demonstrated that the constrained socially optimal allocation satisfies the Pareto efficient conditions; therefore, it may be first best. It is also shown that the subgame perfect equilibrium for the decentralized leadership game is socially optimal. The anticipation of equalization of marginal utilities of public consumption and equalization of marginal utilities of private consumption provides regional governments with correct incentives in the setting of capital taxes.

Provably secure and pairing-free certificateless digital signature scheme using elliptic curve cryptography

A provably secure certificateless digital signature scheme using elliptic curve cryptography is presented in this paper. Since the certificateless public key cryptosystem removes the complex certificate management procedure and the private key escrow problem of traditional public key cryptography (PKC) and identity-based cryptosystem (IBC), respectively, and as a result, the proposed scheme is more efficient than IBC- and PKC-based signatures. Besides, the bilinear pairing and map-to-point hash function are time-consuming operations, and thus the signatures without these two operations are more attractive in real applications and the present work has been carried out in this direction. Based on the elliptic curve discrete logarithm assumption, it is shown that the proposed scheme is unforgeable under the adaptive chosen message and identity attacks in the random oracle model against variety of adversaries. Finally, our signature scheme is compared with a number of competitive schemes and the satisfactory performance has been achieved.

Multiuser beamforming in multicell downlinks for maximising worst signal‐to‐interference‐plus‐noise ratio

In a multicell scenario, two competitive beamforming design schemes are proposed to maximise the worst signal-to-interference-plus-noise ratio (SINR) in each cell for downlink transmissions. The first design adopts interference control. The authors use different interference control weighting factors to suppress the inter-cell interference effectively as each cell may experience different level of inter-cell interference. The other design is based on power control, where the authors can adjust the power of each base station to improve the system performance. Both the two improved designs keep the distributive nature of the competitive design as well as improve the worst SINR of the system. The existence of Nash equilibrium of the proposed designs is proved and the system overhead is analysed for the comparison with other distributive algorithms. Simulation results show that both the proposed competitive designs have improved performances, and the competitive design with power control outperforms that with the interference control as the transmission power increases.

Customer Demand and Product Strategy of Historic Area Renovation in Shenzhen China

Using method of questionnaire survey, we get the customer demand in renovation of Nantou historic area in Shenzhen China. Consulting the experience of similar projects, the product stratagem has been suggested which meets the customer’s demand. At the end, a competitive scheme has been designed to achieve a successful project of resort and entertainment.

A space search optimization algorithm with accelerated convergence strategies

Evolutionary algorithms (EAs), which have been widely used to solve various scientific and engineering optimization problems, are essentially stochastic search algorithms operating in the overall solution space. However, such random search mechanism may lead to some disadvantages such as a long computing time and premature convergence. In this study, we propose a space search optimization algorithm (SSOA) with accelerated convergence strategies to alleviate the drawbacks of the purely random search mechanism. The overall framework of the SSOA involves three main search mechanisms: local space search, global space search, and opposition-based search. The local space search that aims to form new solutions approaching the local optimum is realized based on the concept of augmented simplex method, which exhibits significant search abilities realized in some local space. The global space search is completed by Cauchy searching, where the approach itself is based on the Cauchy mutation. This operation can help the method avoid of being trapped in local optima and in this way alleviate premature convergence. An opposition-based search is exploited to accelerate the convergence of space search. This operator can effectively reduce a substantial computational overhead encountered in evolutionary algorithms (EAs). With the use of them SSOA realizes an effective search process. To evaluate the performance of the method, the proposed SSOA is contrasted with a method of differential evolution (DE), which is a well-known space concept-based evolutionary algorithm. When tested against benchmark functions, the SSOA exhibits a competitive performance vis-a-vis performance of some other competitive schemes of differential evolution in terms of accuracy and speed of convergence, especially in case of high-dimensional continuous optimization problems.

Lock-In Effects in Competitive Bidding Schemes for Payments for Ecosystem Services

Competitive bidding is considered to be a cost-effective allocation mechanism for payments for ecosystem services. This article shows that competition is not a necessary condition for sustaining cost-effectiveness in the long run. In a repeated conservation auction, learning, specific investments and the creation of social capital bias the chances of winning a follow-up contract in favour of former auction winners. Applying the concept of fundamental transformation (Williamson 1985), we argue that this asymmetry weakens competition and leads to lock-in effects between the auctioning agency and a stable pool of sellers with uncertain consequences for cost-effectiveness. We compare data from two laboratory experiments on auction-based conservation programmes and show under which conditions lock-in effects are likely to occur in a controlled environment. Our findings demonstrate lock-in effects do not erode the effectiveness of an auction but change the rules of the game towards more favourable conditions for the provision of the targeted good or service. In view of the empirical evidence for a superior performance of long-term contract relationships compared to low-cost short-term contracting, we discuss directions for follow-up empirical work.

Optimal representation set construction with a competitive scheme for palmprint and face recognition

Conventional representation-based methods only consider the whole training set for representation and recognition. However, some of the training samples make little contribution to the representation of the test sample. In this paper, we propose to construct an optimal representation set for recognition. First, the nearest neighbour principle is used to initialize the optimal representation set. Then, it will be updated through adding a training sample which can work with the current representation set to represent the test sample with minimum error. With this scheme, we accelerate the computation procedure by the partitioned matrix technique. To fully validate the effectiveness of the proposed method, experiments have been conducted on public palmprint and face databases by comparing the proposed method with the state-of-the-art methods. The proposed competitive sample selection method shows promising results.

A transgenetic algorithm applied to the Traveling Car Renter Problem

The Traveling Car Renter is a new problem and constitutes a generalization of the Traveling Salesman. Several applications arise from it, mainly in the scheduling optimization of rental cars and in other problems concerning transport systems. In this paper, an integer quadratic programming model is presented for the Traveling Car Renter. It is linearized and implemented on a solver providing optimal solutions to a set of eighteen small instances. Large instances are tackled with a transgenetic algorithm proposed here. A computational experiment is reported on sixty instances and the proposed algorithm is compared to a memetic algorithm presented previously. The computational experiment aimed at focusing on the differences of performance between the two heuristic algorithms due to the search strategy used by each of them. Therefore, the implementation of both methods shared several elements. The results show that the transgenetic algorithm presents the best performance, indicating that its cooperative evolutionary process was more effective on the investigated problem than the competitive scheme of the memetic algorithm.

Luminescent Core–Shell Imprinted Nanoparticles Engineered for Targeted Förster Resonance Energy Transfer-Based Sensing

Red-luminescent 200 nm silica nanoparticles have been designed and prepared as a versatile platform for developing FRET (Förster resonance energy transfer) biomimetic assays. Ru(phen)₃²⁺ dye molecules embedded off-center in the silica core provide the long-lived donor emission, and a near-infrared labeled analyte serves as fluorescent acceptor (the measured R₀ of this D-A pair is 4.3 nm). A thin surface-grafted molecularly imprinted polymer (MIP) shell intervenes as selective enrofloxacin-binding element. These nanoparticles have been tested for photochemical detection of enrofloxacin by using a competitive scheme that can be readily performed in MeCN-HEPES (pH 7.5) 7:3 (v/v) mixtures and allows for the antibiotic detection in the μM range (LOD = 2 μM) without optimization of the assay. Given the well-known difficulties of coupling the target-binding-to-MIP and the transducing events, the novel photochemical approach tuned up here will be valuable in future developments of MIP-based assays and optosensors that capitalize also on the advantages of nanomaterials for (bio)analysis.

High-contrast and narrow-linewidth resonant profile for continuous operation atomic clock

We report a high-contrast and narrow-linewidth resonant line profile by measuring the magneto-optical rotation of the transmitted light in a forward-scattering arrangement. We also report the splitting of the transmitted line profile at a strong microwave excitation. This profile may provide a good competitive scheme for the passive Rb frequency standard.

Stochastic Congestion Pricing among Multiple Regions: Competition and Cooperation

Previous studies of road congestion pricing problem assume that transportation networks are managed by a central administrative authority with an objective of improving the performance of the whole network. In practice, a transportation network may be comprised of multiple independent local regions with relative independent objectives. In this paper, we investigate the cooperative and competitive behaviors among multiple regions in congestion pricing considering stochastic conditions; especially demand uncertainty is taken into account in transportation modelling. The corresponding congestion pricing models are formulated as a bilevel programming problem. In the upper level, congestion pricing model either aims to maximize the regional social welfare in competitive schemes or attempts to maximize the total social welfare of multiple regions in cooperative schemes. In the lower level, travellers are assumed to follow a reliability-based stochastic user equilibrium principle considering risks of late arrival under uncertain conditions. Numerical examples are carried out to compare the effects of different pricing schemes and to analyze the impact of travel time reliability. It is found that cooperative pricing strategy performs better than competitive strategy in improving network performance, and the pricing effects of both schemes are quite sensitive to travel time reliability.

A Study on Efficient Vehicular Safety Message Broadcast Scheme using GPS Information in VANET

In Vehicle Safety Communication (VSC), noted as a representative application of VANET technologies, emergency warning messages (EWM) are broadcast over vehicles in response to an emergency situation. However, as the number of vehicles increases, the so-called Broadcast Storm Problem often occurs due to a huge number of redundant EWM transmitted. Though many approaches in the literature have been proposed to solve the problem, most of them require the preliminary phase for setting up or refreshing neighbor relationship through exchanging beacon messages with nearby vehicles, thereby further increasing the traffic load. In this paper, we propose Longest Leaping Relay (LLR) scheme which can efficiently solve the Broadcast Storm Problem without any preliminary phase for setting up neighbor relationship. Through computer simulations, we compared the performance of the proposed scheme with competitive schemes and verified the proposed scheme outperforms the existing schemes.

Optimizing dynamic supply chain formation in supply mesh using CSET model

A new e-Service model called dynamic supply chain is characterized by their dynamic nature in easily being formed and disbanded with the seamless connectivity provided by e-Marketplace. The new term "supply mesh" was coined to represent this virtual community of companies in which dynamic supply chains, as per project (also known as make-to-order), are formed across different tiers of suppliers. In a supply mesh, a dynamic supply chain can be formed vertically, from the top to the bottom layers, mediating different companies for a project. Companies that are on the same level laterally are usually competitors, and the companies that are linked vertically as supply chains are trading partners. From a global view, the companies that are connected in the supply mesh can be viewed as individual entities that have self-interest. They may compete for survival as well as collaborate with each other for jobs. Given such complex relations the challenge is to find an optimal group of members for a dynamic supply chain in the supply mesh. A multi-agent model called the collaborative single machine earliness/tardiness (CSET) model was recently proposed for the optimal formation of make-to-order supply chains. This paper investigates the possibilities of applying CSET in a supply mesh, and the corresponding allocation schemes are experimentally studied in simulations. One scheme called Cost-driven principle leads to destructive competition while the other one namely Pareto-optimal evolves into a cooperative competition that tries to mutually benefit every participant. The results, based on samples from the U.S. textile industry, show that a cooperative competition scheme is superior in terms of optimal allocation, which obtains maximum satisfaction for all participants.

A three-step floating catchment area method for analyzing spatial access to health services

Gravity-based spatial access models have been widely used to estimate spatial access to healthcare services in an attempt to capture the interaction of various factors. However, these models are inadequate in informing health resource allocation work due to their inappropriate assumption of healthcare demand. For the purpose of effective healthcare resource planning, this article proposes a three-step floating catchment area (3SFCA) method to minimize the healthcare-demand overestimation problem. Specifically, a spatial impedance-based competition scheme is incorporated into the enhanced two-step floating catchment area (E2SFCA) method to account for a reasonable model of healthcare supply and demand. A case study of spatial access to primary care physicians along the Austin–San Antonio corridor area in central Texas showed that the proposed method effectively minimizes the overestimation of healthcare demand and reflects a more balanced geographic pattern of spatial access than E2SFCA. In addition, by using an adjusted spatial access index, the 3SFCA method indicates strong potential for identifying health professional shortage areas. The study concludes that 3SFCA is a promising method to provide health professionals and decision makers with useful healthcare accessibility information.

New Rothe-wavelet method for solving telegraph equations

In this article, we present a new competitive scheme to solve one of the most important cases in hyperbolic partial differential equations which is called telegraph equations. This method is based on Rothe's approximation in time discretisation and on the wavelet-Galerkin in the spatial discretisation. For comparison of wavelets, we use sin–cos, Legendre and Daubechies wavelets as basis in projection methods. For showing efficiency of method, a numerical experiment, for which the exact solution is known, is considered.