Traditional Artificial Fish Swarm Algorithm Research Articles

A Dynamic Path Optimization Model of IOT Delivery Vehicles for E-commerce Logistics Distribution

Logistics and distribution is a vital link to guarantee the stable supply of the e-commerce market and the healthy development of the industry. With the constant growth of the e-commerce, the efficiency and service quality of logistics and distribution have been paid more and more attention to. Therefore, the study firstly Considering distribution fixed cost, transportation penalty cost and carbon emission cost, the vehicle routing optimization model is transformed into the lowest transportation cost model, then uses an improved traditional artificial fish swarm algorithm to find the optimum way for this model, and finally verifies its performance and applicability through experiments. The performance test results show that the algorithm finds the optimal solution 3589 and 3590 in 63 and 78 iterations in the Oxford Robot Car dataset and Apollo Scape dataset, respectively; the average running time of the algorithm is 11.864s and 11.967s in the 10 operation time tests; in the operation function test, the algorithm. The algorithm was able to overcome the local optimal solution problem. The applicability simulation shows that this algorithm stabilizes after 53 iterations, the minimum cost of the optimal solution of the model is $41,224, and the total distance of distribution is 9035 km. The research algorithm is fast in finding the optimal value, which is close to it, indicating that the algorithm is highly efficient and reliable, and can greatly optimize the path of e-commerce logistics delivery vehicles, and give a theoretical foundation for the optimization of logistics delivery paths in other industries.

Optimization Model of Financial Market Portfolio Using Artificial Fish Swarm Model and Uniform Distribution.

The central issue in finance is how to select a portfolio in the financial market. The traditional artificial fish swarm algorithm (AFSA) is optimized in this paper, and the improved AFSA is used to solve the portfolio model. This model generates a uniform distribution operator using uniform distribution and combines it with the basic fish swarm algorithm. Uniform variation occurs when the variance of the optimal value of continuous convergence is within the allowable error. In this manner, the fish can escape the trap of the local extremum, obtaining the global optimal state. To validate the feasibility of improving AFSA, this paper conducts simulation experiments on portfolio problems using MATLAB tools. Experiments show that this model has an accuracy of 93.56 percent, which is 8.43 percent higher than that of the NSGA-II model and 3.76 percent higher than that of the multiobjective optimization model. The experiment shows that the algorithm in this paper can solve these types of problems well and that, using this model, the optimal portfolio investment decision scheme satisfying investors can be obtained. The optimized AFSA presented in this paper can serve as an important reference for investment portfolios and has a wide range of application possibilities in the investment market.

Path planning and smoothing of mobile robot based on improved artificial fish swarm algorithm

An algorithm that integrates the improved artificial fish swarm algorithm with continuous segmented Bézier curves is proposed, aiming at the path planning and smoothing of mobile robots. On the one hand, to overcome the low accuracy problems, more inflection points and relatively long planning paths in the traditional artificial fish swarm algorithm for path planning, feasible solutions and a range of step sizes are introduced based on Dijkstra's algorithm. To solve the problems of poor convergence and degradation that hinder the algorithm's ability to find the best in the later stage, a dynamic feedback horizon and an adaptive step size are introduced. On the other hand, to ensure that the planned paths are continuous in both orientation and curvature, the Bessel curve theory is introduced to smooth the planned paths. This is demonstrated through a simulation that shows the improved artificial fish swarm algorithm achieving 100% planning accuracy, while ensuring the shortest average path in the same grid environment. Additionally, the smoothed path is continuous in both orientation and curvature, which satisfies the kinematic characteristics of the mobile robot.

Forecasting annual natural gas consumption via the application of a novel hybrid model.

Accurate prediction of natural gas consumption (NGC) can offer effective information for energy planning and policy-making. In this study, a novel hybrid forecasting model based on support vector machine (SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed to predict annual NGC. An adaptive learning strategy based on sigmoid function is introduced to improve the performance of traditional artificial fish swarm algorithm (AFSA), which provides a dynamic adjustment for parameter moving step step and visual scope visual. IAFSA is used to obtain the optimal parameters of SVM. In addition, the annual NGC data of China is selected as an example to evaluate the prediction performance of the proposed model. Experimental results reveal that the proposed model in this study outperforms the benchmark models such as artificial neural network (ANN) and partial least squares regression (PLS). The mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) values are as low as 0.512, 1.4958, and 1.0940. Finally, the proposed model is employed to predict NGC in China from 2020 to 2025.

An Improved Fish Swarm Algorithm for Neighborhood Rough Set Reduction and its Application

In this paper, an improved fish swarm algorithm for neighborhood rough set reduction (IFSANRSR) is proposed. In IFSANRSR, by introducing an adaptive function to control the visual and step size of artificial fish, the problem of inconsistent convergence speed existed in a traditional artificial fish swarm algorithm (FSA) is avoided. The movement of artificial fish in the swarming and following behavior is improved to shorten the running time of the algorithm. The searching behavior is improved to enhance the local search ability without changing the global searching ability of the algorithm. By introducing the mechanism of extinction and rebirth, the worst solution is eliminated and rebirth takes place after each iteration, which ensures a high level of the overall fitness. The experimental results on three datasets from the University of California at Irvine (UCI) show that the attributes reduction by using the IFSANRSR has higher reduction rate and classification accuracy in most cases. It could better deal with real-valued data attributes and ensure the optimal attribute reduction set to be found. The experimental result on the decision system of aluminum alloy welded joints show that by using the IFSANRSR, the key influencing factors of fatigue life of aluminum alloy welded joints could be obtained, and the weight of each influencing factor could be calculated quantitatively.

Dynamic friction modelling and parameter identification for electromagnetic valve actuator

A new modified LuGre friction model is presented for electromagnetic valve actuator system. The modification to the traditional LuGre friction model is made by adding an acceleration-dependent part and a nonlinear continuous switch function. The proposed new friction model solves the implementation problems with the traditional LuGre model at high speeds. An improved artificial fish swarm algorithm (IAFSA) method which combines the chaotic search and Gauss mutation operator into traditional artificial fish swarm algorithm is used to identify the parameters in the proposed modified LuGre friction model. The steady state response experiments and dynamic friction experiments are implemented to validate the effectiveness of IAFSA algorithm. The comparisons between the measured dynamic friction forces and the ones simulated with the established mathematic friction model at different frequencies and magnitudes demonstrate that the proposed modified LuGre friction model can give accurate simulation about the dynamic friction characteristics existing in the electromagnetic valve actuator system. The presented modelling and parameter identification methods are applicable for many other high-speed mechanical systems with friction.

Coverage-enhancing approach in multimedia directional sensor networks for smart transportation

Smart transportation has a significantly impact on city management and city planning, which has received extensive attentions from academic and industrial communities. Different from omni-directional sensing system, as a directional sensing system, the multimedia-directional sensor network holds the special coverage scheme, which is usually used for smart cities, smart transportation, and harsh environment surveillance, for instance, nuclear-pollution regions where are inhospitable for people. This paper advances Virtual Stream Artificial Fish-swarm based Coverage-Enhancing Algorithm (VSAFCEA) as a coverage-enhancing means in multimedia directional sensor networks. Firstly, a concept of virtual streams, based on traditional artificial fish-swarm algorithm, is proposed. Then, the traditional behaviors of fishes in artificial fish-swarm algorithm are modified and expanded with several new behaviors. Finally, the presented VSAFCEA is adopted for coverage-enhancing issue in the situation of directional sensor networks with rotational direction-adjustable model. With a sequence of steps of artificial fishes in virtual stream, the presented VSAFCEA can figure out the approximation to the highest area coverage rate. Based on comparison of these simulation results (results of presented VSAFCEA and that of other typical coverage-enhancing ways in directional sensor networks), the conclusion can be drawn that VSAFCEA could attain higher area coverage rate of directional sensor networks with fewer iterative computing times.

Parameter Optimization and Application of Support Vector Machine Based on Parallel Artificial Fish Swarm Algorithm

Parameters selection of support vector machine is a very important problem, which has great influence on its performance. In order to improve the learning and generalization ability of support vector machine, in this paper, proposed a new algorithm -parallel artificial fish swarm algorithm to optimize kernel parameter and penalty factor of support vector machine, improved the loop body of artificial fish swarm algorithm to avoid the missing of the optimum solution, and proved its validity by testing with some test functions; used the optimal parameters in a non-specific persons, isolated words, and medium-vocabulary speech recognition system. The experimental results show that the rates of speech recognition based on support vector machine using the new algorithm are better than those of using the traditional artificial fish swarm algorithm in different signal to noise ratios and different words. Especially, the support vector machine model based on the new algorithm can still maintain better recognition rates in lower signal to noise ratios. So the new algorithm is an effective support vector machine parameter optimization method, which makes the support vector machine not only have good generalization ability, but have better robustness.

An Improved Artificial Fish Swarm Algorithm for Optimal Operation of Cascade Reservoirs

Based on traditional artificial fish swarm algorithm (AFSA), an improved artificial fish swarm algorithm (IAFSA) is proposed and used to solve the problem of optimal operation of cascade reservoirs. To improve the ability of searching the global and the local extremum, the vision and the step of artificial fish are adjusted dynamicly in IAFSA. Moreover, to increase the convergence speed, the threshold selection strategy is employed to decrease the individual large space gap between before and after update operation in the local update operation. The validity of IAFSA is proved by case study and the threshold parameters of IAFSA are rated.

The Novel Compound Evolutionary Optimization Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization

To overcome the problem of low convergence speed and sensitivity to local convergence with the traditional Artificial Fish-swarm Algorithm (AFSA) to handle complex functions, a novel compound evolutionary algorithm, called AFS-EMPCEOA, was introduced which is combined Artificial Fish-swarm Algorithm with the Elite Multi-parent Crossover Evolutionary Optimization Algorithm (EMPCEOA) that is GuoTao Algorithm improved by elite multi-parent crossover method. AFSEMPCEOA algorithm program with hybrid discrete variables was also developed. The computing example of mechanical optimization design shows that this algorithm has no special requirements on the characteristics of optimal designing problems, which has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence and high efficiency.