This paper describes the synthesis of digitally excited pencil/flat top dual beams simultaneously in a linear antenna array constructed of isotropic elements. The objective is to generate a pencil/flat top beam pair using the excitations generated by the evolutionary algorithms. Both the beams share common variable discrete amplitude excitations and differ in variable discrete phase excitations. This synthesis is treated as a multi-objective optimization problem and is handled by Quantum Particle Swarm Optimization algorithm duly controlling the fitness functions. These functions include many of the radiation pattern parameters like side lobe level, half power beam width and beam width at the side lobe level in both the beams along with the ripple in the flat top band of flat top beam. In addition to it, the dynamic range ratio of the amplitudes excitations is set below a certain level to diminish the mutual coupling effects in the array. Two sets of experiments are conducted and the effectiveness of this algorithm is proved by comparing it with various versions of swarm optimization algorithms.

Most applications of sensor nodes are in hazardous areas, inaccessible or hostile environments. Therefore, the need for security in these networks is essential. Trust methods are powerful tools for diagnosing unexpected behavior of nodes (malicious nodes or failure nodes). In this paper, we have proposed TBSI trust model whose main features are low computational overhead, low energy consumption and confronting attacks in WSNs. This model is simulated and evaluated by NS-2 simulator and its behavior has been evaluated based on the results of these simulations. Examining practical results shows that energy consumption, routing overhead, and the time of death of nodes are reduced and the rate of packet delivery to the base station is increased. These desirable outcomes prove that using the method of trust to achieve a secure network is a good solution to solve security issues in wireless sensor networks.

Meta-heuristics have already received considerable attention in various engineering optimization fields. As one of the most rewarding tasks, eigenvalue optimization of truss structures is concerned in this study. In the proposed problem formulation the fundamental eigenvalue is to be maximized for a constant structural weight. The optimum is searched using Particle Swarm Optimization, PSO and its variant PSOPC with Passive Congregation as a recent meta-heuristic. In order to make further improvement an additional hybrid PSO with genetic algorithm is also proposed as PSOGA with the idea of taking benefit of various movement types in the search space. A number of benchmark examples are then treated by the algorithms. Consequently, PSOGA stood superior to the others in effectiveness giving the best results while PSOPC had more efficiency and the least fit ones belonged to the Standard PSO.

Clustering is one of the important methods in data analysis. For big data, clustering is difficult due to the volume of data and the complexity of clustering algorithms. Therefore, methods that can handle a large amount of data clustering at the reasonable time are required. MapReduce is a powerful programming model that allows parallel algorithms to run in distributed computing environments. In this study, an improved artificial bee colony algorithm based on a MapReduce clustering model (MR-CWABC) is proposed. The weighted average without greedy selection of the results improves the local and global search of ABC. The improved algorithm is implemented in accordance with the MapReduce model on the Hadoop framework to allocate optimal samples to the clusters such that the compression and separation of the clusters are preserved. The proposed method is compared with some well-known bio-inspired algorithms such as particle swarm optimization (PSO), artificial bee colony (ABC) and gravitational search algorithm (GSA) implemented based on the MapReduce model on the Hadoop framework. The results showed that MR-CWABC is well-suited for big data, while maintaining clustering quality. The MR-CWABC demonstrates an improvement of 7.13%, 7.71% and 6.77% based on the average F-measure compared to MR-CABC, MR-CPSO, and MR-CGSA, respectively.

The main objective of this study is to hybridize particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms to propose an efficient algorithm for optimal designing of truss structures. Two types of serial integration of the algorithms are studied. In the first one, PSO is employed to explore the design space, while ACO is utilized to achieve a local search about the best solution found by PSO. This is denoted as serial particle swarm ant colony algorithm (SPSACA). In the second one, ACO works as the global optimizer while PSO acts as the local one. This is called as serial ant colony particle swarm algorithm (SACPSA). A number of structural optimization benchmark problems are solved by the proposed algorithms. Numerical results indicate that the SPSACA possesses better computational performance compared with the SACPSA and other existing algorithms.