ग्लोबल जर्नल ऑफ़ टेक्नोलॉजी एंड ऑप्टिमाइज़ेशन

This paper presents a techniq for solving multi-stage stochastic nonlinear programs. The techniq is based on projected lagrange approach which generates the search direction by solving parallelly a set of quadratic programming subproblems with size much less than the original problem at each iteration. Mathamatically, can be pointed out that Lagrange’s projection method can solve problem multi-stage stochastic nonlinear programs.

Dependency is the only means to ensure that the source code of a system is consistent with its requirements. During software maintenance and evolution, requirement dependency links become obsolete because dependency model is been not trained properly to updating them. Yet, recovering these dependency links later is a daunting and costly task for building the model for unsupervised enhancements. Consequently, the literature has proposed methods, techniques, and tools to recover these dependency links semi-automatically or automatically. Among the proposed techniques, the literature showed that information retrieval (IR) techniques can automatically recover traceability links between free-text requirements and source code through classification techniques to the Software repositories. However, IR techniques lack accuracy (precision and recall) in terms of Text and concept based mining also leads to code sense disambiguation. In this paper, we show that Semantic mining of software repositories and combining mined results with IR can improve the accuracy (precision and recall) of IR techniques. We apply Dependency Estimation on to compare the accuracy of its dependency links with those recovered using state-of-the-art IR techniques from Vector Space model and Concept based mining. We thus show that mining software repositories and combining the mined data with existing results from IR techniques improves the precision and recall of requirement dependency links.

The paper gives an overview of the latest results in the development of the development of the methodology for expansion planning of electric power industry, electric power systems and companies in the market environment. The prerequisites of the market methodology are analyzed. The market approaches in generation and transmission networks are presented. The main principles of the holistic power system expansion planning are given. The conclusion of the paper suggests same generalization in this important area.

In this paper, we presents a novel approach for solving optimal power flow (OPF) problems using a hybrid differential evolution and harmony search (DEHS). The DEHS method is an improved differential evolution method based on the harmony search scheme. Harmony Search has strong and easy to combine with other methods in optimization and the Differential Evolution algorithm has a very great ability to search solutions with a fast speed to converge, contrary to the most meta-heuristic algorithms. The DEHS method has the flexible adjustment of the parameters to get a better optimal solution. Moreover, an effective constraint handling framework in the method is employed for properly handling equality and inequality constraints of the problem. The proposed DEHS has been tested on three systems including IEEE-30 bus system with quadratic fuel cost function, IEEE-30 bus system with valve point effects fuel cost function and IEEE-57 bus system with quadratic fuel cost function. The obtained results from DEHS algorithm have been compared to those from other methods in the literature. The result comparison has indicated that the proposed DEHS method is more effective than many other methods for obtaining the optimal solution for the test systems. Therefore, the proposed DEHS is a very favorable method for solving the optimal power flow problems.

Evolvable Hardware (EHW) is a field of Evolutionary Computation (EC) started in the early 1990’s that includes a subfield of Evolvable Hardware Design and a subfield of Adaptive Hardware. Two methods of evolvable hardware design of a one-bit full adder are analyzed in this paper: first method is based on the well-known idea of gate-level design using a network of programmable gates, and the second method uses Verilog instructions coded in chromosomes represented as binary strings. Eventually, the two solutions were compared in terms of hardware resources and propagation times.

This paper presents a continuum finite element mechanics approach to model the vibration behaviours of single-walled carbon nanotubes (SWCNTs) of varying lengths, aspect ratios, chiralities, boundary conditions, axial loads and with initial strain applied. The results are in good agreement with the open literature and show that resonance-based carbon nanotubes sensors have the potential to meet the high level performance requirements inherent of many sensor based applications such as mass detectors, biomedical sensors, monitoring for metal deposition and chemical reactions amongst others. Currently, the sensitivity of many electromechanical transducers used for these applications have reached their respective theoretical limit. The merit of carbon nanotubes is that, due to their miniature dimensional structures, the sensitivity of these sensor based applications is vastly improved.