Current Issue : October - December Volume : 2014 Issue Number : 4 Articles : 6 Articles
This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale\ndistributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms,\na new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The\npropose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect\nglobal solution once they modify assignments. In addition, the construction of initial solution could be received more quickly\nwithout repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing\nlocal stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete\ndistributed constraint optimization algorithms, guaranteeing better solutions received within ideal time....
Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides fundamental support for many\nlocation-aware protocols and applications. Constraints of cost and power consumptionmake it infeasible to equip each sensor node\nin the network with a global position system (GPS) unit, especially for large-scaleWSNs. A promisingmethod to localize unknown\nnodes is to use several mobile anchors which are equipped with GPS units moving among unknown nodes and periodically\nbroadcasting their current locations to help nearby unknown nodes with localization. This paper proposes amobile anchor assisted\nlocalization algorithm based on regular hexagon (MAALRH) in two-dimensional WSNs, which can cover the whole monitoring\narea with a boundary compensation method. Unknown nodes calculate their positions by using trilateration. We compare the\nMAALRH with HILBERT, CIRCLES, and S-CURVES algorithms in terms of localization ratio, localization accuracy, and path\nlength. Simulations show that the MAALRH can achieve high localization ratio and localization accuracy when the communication\nrange is not smaller than the trajectory resolution....
In the original particle swarm optimisation (PSO) algorithm, the particles� velocities and positions are updated after the whole\nswarmperformance is evaluated. This algorithm is also known as synchronous PSO (S-PSO). The strength of this update method is\nin the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle\nin A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using\npartial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods\nto utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO) algorithm divides the particles\ninto smaller groups. The best member of a group and the swarm�s best are chosen to lead the search. Members within a group\nare updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four\nmultimodal functions, and a real world optimisation problemare used to study the performance of SA-PSO,which is comparedwith\nthe performances of S-PSO and A-PSO.The results are statistically analysed and show that the proposed SA-PSO has performed\nconsistently well....
Two common problems are often encountered in analysis dictionary learning (ADL) algorithms. The first one is that the original\nclean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated fromnoisy measurements.\nThis, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is\nhigh), as represented by the Analysis K-SVD (AK-SVD) algorithm. The other problem is the trivial solution to the dictionary, for\nexample, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete\nsparsifying transform (LOST) algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the\nanalysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the\noriginal signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion\nto avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with\nthree baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms...
With the scale expands, traditional deployment algorithms are becoming increasingly complicated than before, which are no longer\nfit for sensor networks. In order to reduce the complexity, we propose a node deployment algorithm based on viscous fluid model.\nIn wireless sensor networks, sensor nodes are abstracted as fluid particles. Similar to the diffusion and self-propagation behavior\nof fluid particles, sensor nodes realize deployment in unknown region following the motion rules of fluid. Simulation results show\nthat our algorithm archives good coverage rate and homogeneity in large-scale sensor networks....
In this study, we examined the impact of meta-cognitive awareness of students on their performance in mathematics. One thousand six hundred and sixty (1660-967male and 693 female) non-science students of senior secondary classes one, two and three (SS1, SS2 and SS3) from government secondary schools in Bwari area council, federal capital territory Abuja, Nigeria participated in the study. Meta-cognitive awareness was measured using inventory, while performance of the students was measured with the help of researcher made test in the subject of mathematics. The overall mean score of female students on the test and MAI was greater than male students. The results of correlation analysis indicated that meta-cognitive awareness was significantly correlated with the performance of students. Among the recommendations are that; proper meta-cognitive strategies may be used by the mathematics teachers as it has significant impact on students’ performance. Furthermore, students may be encouraged to use meta-cognitive strategies as it has significantly correlated with students’ performance. The study was delimited to secondary school non-science students using the subject of mathematics and was a correlation study based on quantitative data therefore a study may be conducted on other subjects and a larger group of students....
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