Vol. 29 · No. 5

In this paper, a hybrid particle swarm evolutionary algorithm is proposed for solving constrained multi-objective optimization. Firstly, in order to keep some particles with smaller constraint violations, a threshold value is designed, the updating strategy of particles is revised based on the threshold value; then in order to keep some particles with smaller rank values, an infeasible elitist preservation strategy is proposed in order to make the infeasible elitists act as bridges connecting disconnected feasible regions. Secondly, in order to find a set of diverse and well-distributed Pareto-optimal solutions, a new crowding distance function is designed for bi-objective optimization problems. It can assign larger crowding distance function values not only for the particles located in the sparse region but also for the particles located near to the boundary of the Pareto front. In this step, the reference points are given, and the particles which are near to the reference points are kept no matter how crowded these points are. Thirdly, a new mutation operator with two phases is proposed. In the first phase, the total force is computed first, then it is used as a mutation direction, searching along this direction, better particles will be found. The comparative study shows the proposed algorithm can generate widely spread and uniformly distributed solutions on the entire Pareto front.

TPC-E is the new benchmark approved by the TPC council. It is designed to exercise a brokerage firm workload, which is representative of current On-Line Transaction Processing workloads. In this paper we present DBT-5, a fair usage open-source implementation of the TPC-E benchmark. In addition to reporting about the design and implementation of the tool, experimental results from a system running a database engine are also described. The significance of this work is that it provides an environment where recent innovations in the OLTP workload field can be evaluated.

In this paper, a new lossless data compression method that is based on digram coding is introduced. This data compression method uses semi-static dictionaries: All of the used characters and most frequently used two character blocks (digrams) in the source are found and inserted into a dictionary in the first pass, compression is performed in the second pass. This two-pass structure is repeated several times and in every iteration particular number of elements is inserted in the dictionary until the dictionary is filled. This algorithm (ISSDC: Iterative Semi-Static Digram Coding) also includes some mechanisms that can decide about total number of iterations and dictionary size whenever these values are not given by the user. Our experiments show that ISSDC is better than LZW/GIF and BPE in compression ratio. It is worse than DEFLATE in compression of text and binary data, but better than PNG (which uses DEFLATE compression) in lossless compression of simple images.

The portable/hand-held devices deployed in mobile computing environment are mostly limited in memory. To make it possible for them to locally process queries over a large volume of XML data, the data needs to be streamed in fragments of manageable size and the queries need to be processed over the stream with as little memory as possible. In this paper, we report a considerable improvement of the state-of-the-art techniques of query processing over XML fragment stream in memory efficiency. We use XML fragment labeling (XFL) as a method of representing XML fragmentation, and show that XFL is much more effective than the popular hole-filler (HF) model employed in the state-of-the-art in reducing the amount of memory required for query processing. The state-of-the-art with the HF model requires more memory as the stream size increases. With XFL, we overcome this fundamental limitation, proposing the techniques to make query processing scalable in the sense that memory requirement is not affected by the size of the stream as long as the stream is bounded. The improvement is verified through implementation and a detailed set of experiments.

This paper looks at two variants of polynomial mutation used in various evolutionary optimisation algorithms for mutliobjective problems. The first is a non-highly disruptive and the second is a highly disruptive mutation. Both are used for problems with box constraints. A new hybrid polynomial mutation that combines the benefits of both is proposed and implemented. The experiments with three evolutionary multi-objective algorithms on well-known multi-objective optimisation problems show the difference in terms of generational distance, hypervolume, convergence speed and hit rate metrics. The hybrid polynomial mutation in general retains the advantages of both versions in the same algorithm.

Some applications in wireless sensor networks (WSNs) only need to record the information of a target entering or leaving some specific regions of WSNs perimeter. One important issue in this context is to detect the perimeter of the deployed network to ensure that the sensor nodes cover the target area. In this paper we propose two distributed algorithms to elect the perimeter nodes of query regions in a WSN. We consider the most general case, where every sensor has a different sensing radius. We provide performance metrics to analyze the performance of our approach and show by simulation that the proposed algorithms give good performance.

Artificial neural networks have a wide range of applications nowadays in which they are used for intelligent information processing. This paper deals with an application of spiking neural networks in autonomous mobile robot control. The topology of the implemented spiking neural networks was developed through a modified genetic algorithm and through the process of autonomous interaction with the scene environment. Since the genetic algorithm did not use a crossover operator we adapted the mutation operator adding a constraint that prevented creation of a new generation of population with weak individuals in comparison with the previous generation of population. The paper proposes a parallel combination of both left and right local spiking neural network as well as a practical implementation of this proposition in the form of an intelligent navigation system in an autonomous mobile robot. This design enhances the implemented navigation system with a new cognitive property of intelligent information processing using a spiking neural network. Having been adapted to the scene environment, the navigation system was able to make right decisions, change its direction and refrain from collision with the scene walls.

There are many architectures for RSA hardware implementation which improve its performance. Two main methods for this purpose are Montgomery and RNS. These are fast methods to convert plaintext to ciphertext in RSA algorithm with hardware implementation. RNS is faster than Montgomery but it uses more area. The goal of this paper is to compare these two methods based on the speed and on the used area. For this purpose the architecture that has a better performance for each method is selected, and some modification is done to enhance their performance. This comparison can be used to select the proper method for hardware implementation in both FPGA and ASIC design.