Biblio

Aiming at the disadvantage of poor convergence performance of PSO and artificial swarm algorithm, an improved hybrid algorithm is proposed to overcome the shortcomings of complex optimization problems. Through the test of four standard function by hybrid algorithm and compared the result with standard particle swarm optimization (PSO) algorithm and Artificial Bee Colony (ABC) algorithm, the convergence rate and convergence precision of the hybrid algorithm are both superior to those of the standard particle swarm algorithm and Artificial Bee Colony algorithm, presenting a better optimal performance.

Machining process parameters optimization is of concern in machining fields considering machining cost factor. In order to solve the optimization problem of machining process parameters in parallel turning operations, which aims to reduce the machining cost, two PSO-based optimization approaches are proposed in this paper. According to the divide-and-conquer idea, the problem is divided into some similar sub-problems. A particle swarm optimization then is derived to conquer each sub-problem to find the optimal results. Simulations show that, comparing to other optimization approaches proposed previously, the proposed two PSO-based approaches can get optimal machining parameters to reduce both the machining cost (UC) and the computation time.

With increasing demand for travelling, high-quality network service is important to people in vehicle cabins. Visible light communication (VLC) system is more appropriate than wireless local area network considering the security, communication speed, and narrow shape of the cabin. However, VLC exhibits technical limitations, such as uneven distribution of optical signals. In this regard, we propose a novel weight search bat algorithm (WSBA) to calculate a set of optimal power adjustment factors to reduce fluctuation in signal distributions. Simulation results show that the fairness of signal distribution in the cabin optimized by WSBA is better than that of the non-optimized signal distribution. Moreover, the coverage rate of WSBA is higher than that of genetic algorithm and particle swarm optimization.

Mobile ad-hoc network (MANET) contains various wireless movable nodes which can communicate with each other and they don't require any centralized administrator or network infrastructure and also can communicate with full capacity because it is composed of mobile nodes. They transmit data to each other with the help of intermediate nodes by establishing a path. But sometime malicious node can easily enter in network due to the mobility of nodes. That malicious node can harm the network by dropping the data packets. These type of attack is called gray hole attack. For detection and prevention from this type of attack a mechanism is proposed in this paper. By using network simulator, the simulation will be carried out for reporting the difficulties of prevention and detection of multiple gray hole attack in the Mobile ad-hoc network (MANET). Particle Swarm Optimization is used in this paper. Because of ad-hoc nature it observers the changing values of the node, if the value is infinite then node has been attacked and it prevents other nodes from sending data to that node. In this paper, we present possible solutions to prevent the network. Firstly, find more than one route to transmit packets to destination. Second, we provide minimum time delay to deliver the packet. The simulation shows the higher throughput, less time delay and less packet drop.

Predict software program reliability turns into a completely huge trouble in these days. Ordinary many new software programs are introducing inside the marketplace and some of them dealing with failures as their usage/managing is very hard. and plenty of shrewd strategies are already used to are expecting software program reliability. In this paper we're giving a sensible knowledge and the difference among those techniques with my new method. As a result, the prediction fashions constructed on one dataset display a extensive decrease in their accuracy when they are used with new statistics. The aim of this assessment, SE issues which can be of sensible importance are software development/cost estimation, software program reliability prediction, and so forth, and also computing its broaden computational equipment with enhanced power, scalability, flexibility and that can engage more successfully with human beings.

In order to solve the problem of millimeter wave (mm-wave) antenna impedance mismatch in 5G communication system, a optimization algorithm for Particle Swarm Ant Colony Optimization (PSACO) is proposed to optimize antenna patch parameter. It is proved that the proposed method can effectively achieve impedance matching in 28GHz center frequency, and the return loss characteristic is obviously improved. At the same time, the nonlinear regression model is used to solve the nonlinear relationship between the resonant frequency and the patch parameters. The Elman Neural Network (Elman NN) model is used to verify the reliability of PSACO and nonlinear regression model. Patch parameters optimized by PSACO were introduced into the nonlinear relationship, which obtained error within 2%. The method proposed in this paper improved efficiency in antenna design.

Metaheuristic search technique is one of the advance approach when compared with traditional heuristic search technique. To select one option among different alternatives is not hard to get but really hard is give assurance that being cost effective. This hard problem is solved by the meta-heuristic search technique with the help of fitness function. Fitness function is a crucial metrics or a measure which helps in deciding which solution is optimal to choose from available set of test sets. This paper discusses hill climbing, simulated annealing, tabu search, genetic algorithm and particle swarm optimization techniques in detail explaining with the help of the algorithm. If metaheuristic search techniques combine some of the security testing methods, it would result in better searching technique as well as secure too. This paper primarily focusses on the metaheuristic search techniques.

An optimisation is a process of finding maxima or minima of the objective function. Particle Swarm Optimisation (PSO) is a nature-inspired, meta-heuristic, black box optimisation algorithm used to search for global minimum or maximum in the solution space. The sampling strategy in this algorithm mimics the flying pattern of a swarm, where each sample is generated randomly according to uniform distribution among three different locations, which marks the current particle location, the individual best found location, and the best found location for the entire swam over all generation. The PSO has known disadvantage of premature convergence in problems with high correlated design variables (high epistatis). However, there is limited research conducted in finding the main reason why the algorithm fails to locate better solutions in these problems. In this paper, we propose to change the traditional triangular flight trajectory of PSO to an elliptical flight path. The new flying method is tested and compared with the traditional triangular flight trajectory of PSO on five high epistatis benchmark problems. Our results show that the samples generated from the elliptical flight path are generally better than the traditional triangular flight trajectory of PSO in term of average fitness and the fitness of best found solution.

This brief proposes a framework to analyze multiple faults based on multiple fault simulation in a particle swarm optimization environment. Experimentation shows that up to ten faults can be diagnosed in a reasonable time. However, the scheme does not put any restriction on the number of simultaneous faults.