Earticle

This paper presents a kind of coverage optimization strategy based on improved artificial fish swarm algorithm for wireless sensor networks, by adaptively adjusting the vision range and the step length of artificial fish swarm the accuracy of optimization, convergence speed and stability are improved, then combining with the performance of WSN network coverage, the network coverage can be optimized. The simulation results show that comparing to the basic artificial fish swarm algorithm, the network coverage ratio of improved artificial fish swarm algorithm improves 17%.

A strategy which using the particle swarm algorithm improved by the artificial fish swarm algorithm to optimize the BP (Back propagation) algorithm was proposed. It can conquer the shortcomings that the convergence rate of BP is too slow and it is easy to fall into local extreme value, and can improve the learning ability of BP neural network. Finally, the improved algorithm has been used to analysis the earthquake prediction. The results of simulation and test show that the optimized algorithm can improve the predicting accuracy of the BP network.

In order to gain more information reflecting bridge health status, with fewer sensors, a method based on artificial fish swarm algorithm (AFSA) is proposed to solve optimal sensor placement (OSP) problem. The algorithm takes modal assurance criterion (MAC) matrix obtained from modal analysis of an arch bridge structure as the objective function. Four typical behaviors of artificial fish are applied to search the optimal solution. The results show that AFSA is more effective than the particle swarm optimization (PSO) method, in achieving optimal sensor placement.

In the resource allocation process of multi-user OFDM system, in order to realize the purpose of maximizing the total transmission rate under constant power, a new subcarrier allocation algorithm has been proposed in this paper, which is to introduce cultural evolution method into the original artificial fish swarm algorithm. Because the population space in cultural algorithm framework has the advantage of guiding search process, the new algorithm can effectively overcome the defect of falling into local extreme which generally exists in the fish swarm algorithm in the resource allocation. At the same time, the proposed new algorithm also can make it easy to quantify the optimized goals and variable values. Simulation results show that the proposed artificial fish swarm algorithm with cultural evolution (CE-AFS) has been greatly improved the global search ability and convergence speed compared with the AFS algorithm and Shen algorithm.

The artificial fish swarm algorithm (AFSA) is a heuristic global optimization technique based on population which is easy to understand, good robustness, and not insensitive to initial values. The behavior of fishes has a great impact on the performance of the algorithm, such as global search and convergence speed. At present, there has no general research theory to select behaviors of fishes. In order to deal with this problem, we proposed an improved artificial fish swarm algorithm based on hybrid behavior selection. There are two mainly works in this paper. Firstly, we propose an improved algorithm based on swallowed behavior, which can greatly speed up the convergence. Secondly, in order to deal with the problems of easy to fall into local optimum value, we added breeding behavior to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

With regard to the subject of sensor network node optimization, a strategy for wireless sensor network coverage optimization based on improved fish swarm algorithm is proposed in this paper. The improved algorithm is targeted on network coverage, node utilization rate and energy consumption balance, which makes use of the ergodic property of chaotic motion to overcome the disadvantage that artificial fish swarm algorithm may easily lead to regional optimization. As for this, the global searching ability of algorithm is improved and the solving efficiency is optimized. Moreover, the algorithm is able to adapt to complicated environment. Shown by related simulation experiment, the improved fish swarm algorithm could effectively optimize sensor network node deployment and improve network coverage rate. Compared with basic artificial fish swarm algorithm, improved fish swarm algorithm increases network coverage rate by 8.9%.

For the problem with imprecise optimal solution and reduced convergence efficiency of basic artificial fish swarm algorithm (BAFSA) in the late, the adaptive enhanced prey behavior of artificial fish and the segmented adaptive strategy of artificial fish’s view and step were designed. The hybrid adaptive artificial fish swarm algorithm (HAAFSA) was structured by the adaptive enhanced prey behavior and the segmented adaptive strategy of artificial fish’s view and step, which have been verified on research. According to the characteristics of the coalmine rescue environment, the path planning environment model was established in two-dimensional plane and the optimization constraints conditions were disposed by detecting the distance between path sections and barriers. The HAAFSA was applied to coalmine rescue robot path planning. Simulation results showed that the HAAFSA could improve the performance of the optimal path.

In urban management, it is important to precisely forecast the short-term demand for necessary resources, including water, electric power, and gas. Although a variety of prediction models have been proposed in literature, the underlying defects and limitations confine the effectiveness and forecasting precision of these models. In this paper, the short-term prediction problem is modeled as a non-linear multivariate regression problem, which is solved by support vector regression (SVR). The parameters in SVR are optimized by artificial fish-swarm algorithm (AFSA). The proposed prediction model (termed SVR-AFSA) overcomes the defects of existing prediction models, thus promoting forecasting precision. In order to verify the effectiveness and prediction precision of SVR-AFSA, this paper conducts experiments on a real dataset of two-month hourly water consumption. It also compares SVR-AFSA with two commonly adopted models, i.e., traditional BP neural network, and SVR optimized by grid method (SVR-grid). The experiments results show that SVR-AFSA outperforms these two models in prediction precision in terms of mean squared error (MSE) and mean absolute percentage error (MAPE).