On the numerical solution of the linear complementarity problem

The well-known linear complementarity problem with definite matrices is considered. It is proposed to solve it using a global optimization algorithm in which one of the basic stages is a special local search. The proposed global search algorithm is tested using a variety of randomly generated problems; a detailed analysis of the computational experiment is given.     

Lower bounds on the pathwidth of some grid-like graphs

We present proofs of lower bounds on the node search number of some grid-like graphs including two-dimensional grids, cylinders, tori and a variation we call “orb-webs”. Node search number is equivalent to pathwidth and vertex separation, which are all important graph parameters. Since matching upper bounds are not difficult to obtain, this implies that the pathwidth of these graphs is easily computed, because the bounds are simple functions of the graph dimensions. We also show matching upper and lower bounds on the node search number of equidimensional tori which are one less than the obvious upper bound.     

IP2DM: integrated privacy‐preserving data management architecture for smart grid V2G networks

With the development of battery vehicles, vehicle‐to‐grid (V2G) networks are becoming more and more important in smart grid. Although battery vehicles are environmentally friendly and flexible to use two‐way communication and two‐way electricity flow, they also raise privacy‐preservation challenges, such as location and movement privacy. On the one hand, utility companies have to monitor the grid and analyze user data to control the power production, distribution, scheduling, and billing process, while typical users need to access their data later online. On the other hand, users are not willing to provide their personal data because they do not trust the system security of the utility companies where their data stored, and it may potentially expose their privacy. Therefore, in this paper, we study data management of V2G networks in smart grid with privacy‐preservation to benefit both the customers and the utility companies. Both data aggregation and data publication of V2G networks are protected in the proposed architecture. To check its security, we analyze this architecture in several typical V2G networks attacks. We conduct several experiments to show that the proposed architecture is effective and efficient, and it can enhance user privacy protection while providing enough information for utility companies to analyze and monitor the grid. Copyright © 2016 John Wiley & Sons, Ltd.     

Search recommendation model based on user search behavior and gradual forgetting collaborative filtering strategy

The existing search engines are lack of the consideration of personalization and display the same search results for different users despite their differences in interesting and purpose.By analyzing user's dynamic search behavior,the paper introduces a new method of using a keyword query graph to express user's dynamic search behavior,and uses Bayesian network to construct the prior probability of keyword selection and the migration probability between keywords for each user.To reflect the dynamic changes of the user's preference,the paper introduces non-lineal gradual forgetting collaborative filtering strategy into the personalized search recommendation model.By calculating the similarity between each two users,the model can do the recommendation based on neighbors and be used to construct the personalized search engine.     

On the expected longest length probe sequence for hashing with separate chaining

Hashing is a widely used technique for data organization. Hash tables enable a fast search of the stored data and are used in a variety of applications ranging from software to network equipment and computer hardware. One of the main issues associated with hashing are collisions that cause an increase in the search time. A number of alternatives have been proposed to deal with collisions. One of them is separate chaining, in which for each hash value an independent list of the elements that have that value is stored. In this scenario, the worst case search time is given by the maximum length of that list across all hash values. This worst case is often referred to as Longest Length Probe Sequence (llps) in the literature. Approximations for the expected longest length probe sequence when the hash table is large have been proposed and an exact analytical solution has also been presented in terms of a set of recurring equations. In this paper, a novel analytical formulation of the expected longest length probe sequence is introduced. The new formulation does not require a recursive computation and can be easily implemented in a symbolic computation tool.     

A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities

This paper presents a novel discrete artificial bee colony (DABC) algorithm for solving the multi-objective flexible job shop scheduling problem with maintenance activities. Performance criteria considered are the maximum completion time so called makespan, the total workload of machines and the workload of the critical machine. Unlike the original ABC algorithm, the proposed DABC algorithm presents a unique solution representation where a food source is represented by two discrete vectors and tabu search (TS) is applied to each food source to generate neighboring food sources for the employed bees, onlooker bees, and scout bees. An efficient initialization scheme is introduced to construct the initial population with a certain level of quality and diversity. A self-adaptive strategy is adopted to enable the DABC algorithm with learning ability for producing neighboring solutions in different promising regions whereas an external Pareto archive set is designed to record the non-dominated solutions found so far. Furthermore, a novel decoding method is also presented to tackle maintenance activities in schedules generated. The proposed DABC algorithm is tested on a set of the well-known benchmark instances from the existing literature. Through a detailed analysis of experimental results, the highly effective and efficient performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature.     

Simulated annealing and tabu search approaches for the Corridor Allocation Problem

In the Corridor Allocation Problem, we are given n facilities to be arranged along a corridor. The arrangements on either side of the corridor should start from a common point on the left end of the corridor. In addition, no space is allowed between two adjacent facilities. The problem is motivated by applications such as the arrangement of rooms in office buildings, hospitals, shopping centers or schools. Tabu search and simulated annealing algorithms are presented to minimize the sum of weighted distances between every pair of facilities. The algorithms are evaluated on several instances of different sizes either randomly generated or available in the literature. Both algorithms reached the optimal (when available) or best-known solutions of the instances with n ? 30. For larger instances with size 42 ? n ? 70, the simulated annealing implementation obtained smaller objective values, while requiring a smaller number of function evaluations.     

Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems

This paper presents a parameter adaptive harmony search algorithm (PAHS) for solving optimization problems. The two important parameters of harmony search algorithm namely Harmony Memory Consideration Rate (HMCR) and Pitch Adjusting Rate (PAR), which were either kept constant or the PAR value was dynamically changed while still keeping HMCR fixed, as observed from literature, are both being allowed to change dynamically in this proposed PAHS. This change in the parameters has been done to get the global optimal solution. Four different cases of linear and exponential changes have been explored. The change has been allowed during the process of improvization. The proposed algorithm is evaluated on 15 standard benchmark functions of various characteristics. Its performance is investigated and compared with three existing harmony search algorithms. Experimental results reveal that proposed algorithm outperforms the existing approaches when applied to 15 benchmark functions. The effects of scalability, noise, and harmony memory size have also been investigated on four approaches of HS. The proposed algorithm is also employed for data clustering. Five real life datasets selected from UCI machine learning repository are used. The results show that, for data clustering, the proposed algorithm achieved results better than other algorithms.     

On the nonlinear stochastic realization problem

In this paper we construct a first solution of the stochastic realization problem in a nonlinear setting. The great bulk of previous work on stochastic realization has been in the linear Gaussian setting. Such Markovian representations are used e.g., to apply certain filtering and stochastic control techniques. Our methods consist of an amalgamation of methods introduced by Nelson with the Lax-Phillips type geometric approach to linear Gaussian stochastic realization which has been developed by Lindquist and Picci and by Ruckebusch. The result of this that we are able to realize any purely nondeterministic process satisfying suitable continuity conditions as an instantaneous function of a Markov process.