Since there is no fixed infrastructure or centralized management in Wireless Sensor Networks (WSNs), a Connected Dominating Set (CDS) has been proposed as a virtual backbone. In this paper, degree-constrained minimum-weight connected dominating set (DC-MWCDS) problem is used for modeling energy-efficient backbone formation in wireless sensor networks in UDG. DC-MWCDS aims at forming degree-constrained backbone and minimizing the weight of the CDS made for network graph simultaneously. The aim of this paper, is proposing an energy efficient connected dominating set (CDS) scheme in wireless sensor networks, which prolongs the network lifetime. In proposed ALGORITHMs, we use an optimal weight based on the minimum residual energy and maximum effective degree of nodes for backbone formation to prolong the network lifetime. The optimal weight coefficients are determined using particle swarm optimization (PSO) ALGORITHM. Then, when selecting nodes for dominating set (DS) formation, these coefficients will be used. If the degree of a node is more than coefficient of degree constraint and energy of a node is less than coefficient of energy constraint, the node won’t be selected for DS formation. The message and time complexity of the proposed ALGORITHM is O (n). Simulation results show that proposed ALGORITHMs outperforms the other methods in terms of network lifetime.

One of the ways to enhance the security and concealment of data used today is image watermarking. In image watermarking operation, we try to hide image inside another image without letting others know about the hidden image. In this paper, Contourlet Transform and SVD Transform are used to embedded watermark in the host image. The PSO Optimization ALGORITHM is also used in the watermark extraction step to find the best scale factor. The results of the proposed ALGORITHM in this article show an improvement over the comparative methods.

The topic of distribution network reconfiguration has been recognized as a common problem in the operation of distribution networks. Up to now, reconfiguration of the distribution network has been used to reduce power loss, issues with reliability and emergency problems. As a result of the increasing installation rate of Distributed Generations (DGs) and the increasingly increasing number of Electric Vehicles (EVs), distribution network operators have faced new problems as a result of high sensitive loads. These new facets of the problem of network operation consist of the problem of congestion and enable the business mechanism to be executed by an existing distribution market. With the application of Distribution Locational Marginal Price (DLMP), using Particle Swarm Optimization (PSO), this paper introduces a new methodology to solve this problem. As a suitable case study, the standard IEEE 33-bus network has been selected and simulation results show the compatibility of the proposed system. In the case of not considering DGs and the problem of congestion, comparisons with other papers were also discussed. The participation of DGs in the electricity distribution industry, the issue of congestion and the numerical results have been shown and discussed below.

Load modeling is one of the most important features in power system operation and stability studies. So far, various methods have been applied based on measuring the relevant load data, and obtaining the load models through the mathematical models. This paper presents a method for load modeling based on processing the recorded data by the measurement equipment with PSO ALGORITHM, which is located in substations. In the proposed method, for improving the accuracy of the load model, some constraints are applied for eliminating the undesirable measured data. These constraints are applicable for any time periods, such as; hourly, daily, seasonally, and annually. Furthermore, a combination of the static and dynamic models is utilized. The performance of the proposed method is tested on two industrial power grids from the west regional electric company (WREC) network, and the results are thoroughly analyzed. The results have shown that the proposed method has an acceptable accuracy for load modeling in comparison with real model of the case study systems.

In this paper, a mathematical modeling of nested hierarchical p-center problem is presented. In this model there are two levels of hierarchies and the first one should be passed to achieve the second level. The second level facility can provide both services of the first and the second level. This model is a linear integer mathematical programming model. To provide an application of the model, some simulated cases are investigated and resolved, the results show that using this model reduces the initial construction cost while the main objective which is maximum servicing distance will not be changed basically.for the large size problem using of metaheuristic ALGORITHMs is necessary so in this paper an ALGORITHM based on the Particle swarm intelligence is proposed to solve the problem. Numerical results confirm efficiency of the ALGORITHM.

In this paper, a new hybrid Particle Swarm Optimization (PSO) and Harmony Search (HS) ALGORITHM, denoted by PSOHS is presented. This hybrid ALGORITHM is designed to improve the efficiency of the PSO and remove some of the disadvantages which reduce the capability of the PSO. The main problem of the PSO is the lack of balance between exploration and exploitation of the ALGORITHM. Another problem is how to handle the violating particles from feasible search space without reduction in the performance of the ALGORITHM. The problem of unbalanced exploration and exploitation is solved using linear varying inertia weight. The second problem is solved in some other ALGORITHMs via reproduction of the violating particles using the HS ALGORITHM. In this paper, these two approaches are combined to achieve a more efficient ALGORITHM for engineering design problems. To show the higher capability of this approach compared to other works, several benchmark engineering examples, which have been considered previously and solved utilizing a variety of optimization ALGORITHMs, is solved by the present hybrid ALGORITHM. Results illustrate a desirable performance of the PSOHS in both obtaining lower weight and having a higher convergence rate.

In solving the electrical power systems economic dispatch (ED) problems, the goal is to schedule the committed generating units outputs so as to meet the required load demand at the minimum operating cost while satisfying all units and system equality and inequality constraints. This paper presents a different approach to economic dispatch problems with particle swarm optimization (PSO) technique. In the proposed method, in order to achieve better control on the ALGORITHM’s exploration and exploitation capabilities, particles velocity is dependent on both particle’s fitness and time. Perturbation module is adopted to perform perturbation on some particles and provide extra diversity for it to jump out from local optima and avoid premature convergence. The proposed P-PSO method solves both the ED problem with no nconvex/no nsmooth cost functions due to valve-point loading and the ED problem that takes into account nonlinear generator characteristics such as ramp-rate limits and prohibited operating zones in the power system operation. The performance of the P-PSO method is evaluated on four different power systems, and compared with that of other effective optimization methods in terms of the solution quality and convergence characteristics. The outcome is very encouraging and suggests that the P- PSO ALGORITHM is very efficient to solve power system economic dispatch problem.