In this paper, we present a bi-objective Virtual-Force local search particle swarm optimization (BVFPSO) Algorithm to improve the placement of sensors in wireless sensor networks while it simultaneously increases the coverage rate and preserves the battery energy of the sensors. Mostly, sensor nodes in a wireless sensor network are first randomly deployed in the target area, and their deployment should be then modified such that some objective functions are obtained. In the proposed BVFPSO Algorithm, PSO is used as the basic meta-heuristic Algorithm and the Virtual-Force operator is used as the local search. As far as we know, this is the first time that a bi-objective PSO Algorithm has been combined with a Virtual Force operator to improve the coverage rate of sensors while preserving their battery energy. The results of the simulations on some initial random deployments with the different numbers of sensors show that the BVFPSO Algorithm by combining two objectives and using Virtual-Force local search is enabled to achieve a more efficient deployment in comparison to the competitive Algorithms PSO, GA, FRED and VFA with providing simultaneously maximum coverage rate and the minimum energy consumption.

Sensor placement is a critical issue in wireless sensor networks that affects the quality of wireless sensor network coverage. In this paper, we propose an improved Virtual Force Algorithm based on the states of matter (IVFASM) for relocating sensors of a mobile wireless sensor network. IVFASM simulates the behavior of molecules in different states of matter to improve the coverage of sensors. In the proposed IVASM Algorithm, the strength of repulsive Forces, the kinetic energy of the matter molecules, and attraction radius are dynamically adjusted over time according to different states of matter. As a result, in the gaseous state, sensors move rapidly apart; in the liquid state, sensors absorb each other to fill small holes gradually; in the solid state, sensors stabilize their final position. In the simulation, different states of matter led to improved coverage and fewer holes. Evaluation of the proposed method on 14 sample problems with the different numbers of sensors and comparison of the results with state of the art revealed that the proposed method can achieve a higher coverage rate in almost all sample problems. For a sample problem of 30 sensors, genetic Algorithm (GA) and particle swarm optimization (PSO) achieved a coverage ratio of 69%, fuzzy redeployment Algorithm (FRED) achieved a coverage ratio of 72%, classical Virtual Force Algorithm (VFA) obtained a coverage ratio of 79%, improved Virtual Force Algorithm based on area intensity (IVFAI) achieved a coverage ratio of 82%, and our proposed method IVFASM achieved a coverage ratio of 83%.

Central Force Optimization (CFO) is a new member of heuristic Algorithms which has been recently proposed and added to swarm intelligence Algorithms. In this paper, an effective unsupervised image clustering technique is proposed, using CFO and called CFO-clustering. In the presented method, each probe includes the information of center of the clusters, and fitness function contains both inter-distance and intra-distance of the samples. Extensive experimental results show that the proposed CFO-clustering outperforms other similar clustering Algorithms which were designed based on the evolutionary techniques.

In this paper we present a robust hybrid motion/Force control1er for rigid robot manipulators. The main contribution of this paper is that the proposed hybrid control system is able to at complies motion objectives in free directions and Force objectives in constrained directions under parametric uncertainty both in robot dynamics and stiffness constraint constant. Also, the given scheme is proved global1y stable in the sense that the control objectives are achieved asymptotically, when a signum function is used in the control law, though giving rise to chattering effects. To avoid this problem a saturation function is used. In this case the motion and Force errors are proved to be bounded functions. Using the proposed control structure there is no need to measure the derivative of the interaction Forces. Some simulation results are given to illustrate the control system performance.

The main enabling technology for cloud computing is the use of Virtual machines. After making the decision of their placement on hosts, they will be set to run. This process is called Virtual machine placement. This process has a great importance on energy consumption and resource wastage avoidance. On the other hand, the growing complexity of cloud infrastructure compounds the problem. In this article, the problem of Virtual machine placement is transformed to an optimization problem. The goal is minimizing energy consumption and maximizing the profit of placement, simultaneously. A newly emerged optimization method, called Imperialist Competitive Algorithm is applied in this paper. In addition, a unique method for generating new solutions based on already discovered ones, proposed. Finally the success of the proposed Algorithm is confirmed by simulation results and its evaluation is compared with GGA and FFD Algorithm.

Deep-sea vessels use various navigational aids like AIS (automatic identification system), ECDIS (electronic chart display and information system), radar, GPS (global positioning system), and etc. vessels use AIS to communicate their navigational characteristics, destinations, and load information. Such data are very helpful for vessel officers; therefore, AIS can be used for communicating the name and position of the vessel as well as navigational signs in the port area. So, this system is very helpful in improving the quality of pilotage as well as improving the safety of navigation in canals like Khur Musa.

This research aims to minimize the weight of truss structures using Force method formulation as a structural analyzer and Jaya Algorithm as an optimizer tool. Constraints considered herein include stress limitations, displacement limitations, and size limitations. Design variables include the cross-sectional area of each element. They may easily be related to each other which will lead to decrease of design variables. Jaya Algorithm is a meta-heuristic random search method recently developed for constrained and unconstrained problems. The main superiority of Jaya Algorithm to other random search methods is that it does not need any specific tuning parameter to generate next population. This Algorithm consists of two steps in each cycle. First, a new population is generated using a simple random formula. Second, each new point is compared to its corresponding previous one while penalty function method is implemented. If the new point is in a better condition than the old one, the old one is replaced by the new one. All points are tested similarly till the population is updated. The procedure is repeated so as to achieve the desired convergence. Several landmark examples appearing in the literature have been solved by the proposed method, thus showing the efficacy of the developed procedure. A perusal of results shows that procedure is not sensitive to the starting points and it should just be selected large enough to lie in feasible-usable design space. Moreover, rapid reduction of weight is obtained in the first few steps and the tendency of decreasing of the weight appears to be monotonic and uniform in all examples. Unlike other metaheuristic methods that need a large number of optimization cycles to settle near the optimum point, combination of Force method and Jaya Algorithm provides higher computational efficiency and rapid convergence ability achieved by the above match. This is owing to the Forced method formulation that makes the stress constraints to be linear, resulting in facilitating the procedure and enhancing its efficiency.