Simulation Research on GM Navigation Based on AFSA

An improved quantum artificial fish swarm algorithm is proposed in this paper. Based on that quantum compu- ting have exponential acceleration for heuristic algorithm, by examining eight most recent patents and some literatures in the area of artificial fish swarm algorithm and quantum computing. The new algorithm uses qubits to code artificial fish and quantum revolving gate, preying behavior, following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, apply the new algorithm, the basic artificial fish swarm algorithm and the global edition artificial fish swarm algorithm to the simulation experiment high dimensional and complex non-convex programming respectively. The simulation results show that the improved algorithm can escape from the local extremum effectively, and has higher convergence speed and precision.

In order to deal with the problems of inefficient searching and low accuracy of Artificial Fish Swarm Algorithm(AFSA) for multimodal function optimization,an improved AFSA for multimodal function optimization was proposed.In the algorithm,the strategy of the survival of the fitter suppression was adopted,eliminating artificial fish which was situated in food with low concentration of similar artificial fish to select elite artificial fish swarm.Optimization for swarming behavior and following behavior contributed to artificial fish careful search in a new optimization trajectory to enhance its local search capacity.Modifying for preying behavior,artificial fish was avoided sinking flat position.In combination with Pattern Search Method(PSM),its local accuracy search capacity was enhanced.The simulation results indicate that the proposed algorithm has stronger global optimization and local optimization capabilities,and the search for each optimal solution accuracy has reached the ideal value,and it is able to be used for complex multimodal function optimization.

One of the most difficult combinatorial optimization problems in recent studies is job shop scheduling. Job shop scheduling which also holds the key to the company's profitability is a crucial problem faced by many manufacturing companies. Well-structured scheduling has the potential to reduce operating costs and increase profits. Artificial Fish Swarm Algorithm (AFSA) is one of optimization algorithms to solve combinatorial problems. This paper talks about the implementation of AFSA in job shop scheduling cases to produce an optimal solution, containing a minimum completion total time (makespan) of the entire job. The results showed that the AFSA which is designed for job shop scheduling problem optimization is able to provide solutions with the best efficiency value ever achieved was 75%. This figure is still considered unsatisfactory based on the makespan resulted. Nevertheless, the AFSA ability in the searching process for solutions is quite good considering that level of efficiency is achieved by only 10000 artificial fishes around 100 generations within 3,72e+41 solution spaces.

The discovery of high utility itemsets (HUIs) is an attractive topic in data mining. Because of its high computational cost, using heuristic methods is a promising approach to rapidly discovering sufficient HUIs. The artificial fish swarm algorithm is a heuristic method with many applications. Except the current position, artificial fish do not record additional previous information, as other related methods do. This is consistent with the HUI mining problem: that the results are not always distributed around a few extreme points. Thus, we study HUI mining from the perspective of the artificial fish swarm algorithm, and propose an HUI mining algorithm called HUIM-AF. We model the HUI mining problem with three behaviors of artificial fish: follow, swarm, and prey. We explain the HUIM-AF algorithm and compare it with two related algorithms on four publicly available datasets. The experimental results show that HUIM-AF can discover more HUIs than the existing algorithms, with comparable efficiency.

Taking the radial basis function as a kernel function, a prediction model is developed based on the support vector regression machine (SVR). The optimization of the model parameters, including penalty factor and kernel function variance, is realized by the artificial fish swarm algorithm. The model is used to predict nine foreign exchange rate data with updating and rolling. At the same time, simulating by the cross validation, genetic algorithm, particle swarm optimization algorithm and then evaluating the results from the total error (TE), relative error (RE), absolute root mean square error (ARMSE) and correct trend rate (CTR) comprehensively, the comparison shows that the errors of the model based on SVR optimized by artificial fish swarm algorithm are all minimum and CTR are maximum. In the end, in order to improve the convergence speed and precision further, the self-adaption artificial fish swarm algorithm is presented which is joined the attenuation factor and based on the average distance visual. The result is ideal. Therefore, SVR optimized by the improved artificial fish swarm algorithm can be effectively used in forex prediction.

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

Based on a newly improved artificial fish swarm algorithm– local neighborhood artificial fish swarm algorithm (LNAFSA), we studied its application for adaptive polarization mode dispersion (PMD) compensation. In the adopted local neighborhood structure, each artificial fish only communicated with other five neighboring artificial fishes. Under a simulated 40 Gb/s adaptive second-order PMD compensation for non-return- to-zero and carrier-suppressed return-to-zero formats, the study demonstrated the effectiveness of LNAFSA. In a defined searching process, the eye diagrams showed that the algorithm resulted in an effective compensation effect and produced the maximum consuming time within 300 ms. Much better performance than simplex algorithm and genetic algorithm was also observed. In tracking process, LNAFSA achieved the goal of compensation in about 14 ms.

This article describes the adjustments made in the Artificial Fish Swarm Algorithm (AFSA) to find solutions to the traveling salesman problem. The changes are necessary because the AFSA was initially designed for continuous domain problems and the traveling salesman problem is a problem of discrete domain. The performance of AFSA was compared with two other algorithms collective intelligence, Ant System (AS) and the Discrete Particle Swarm Optmization (DPSO), the results demonstrate that AFSA was superior to DPSO and inferior to the AS.

Diabetes is a major health problem and a disease that can be very dangerous in developing and developed countries, and its incidence is increasing dramatically. In this chapter, the authors propose a system of automatic detection of diabetes based on a bioinspired model called a swarm of fish (fish swarm or AFSA). AFSA (artificial fish swarm algorithm) represents one of the best methods of optimization among swarm intelligence algorithms. This algorithm is inspired by the collective, the movement of fish and their different social behaviors in order to achieve their objectives. There are several parameters to be adjusted in the AFSA model. The visual step is very significant, given that the fish artificial essentially moves according to this parameter. Large parameter values increase the capacity of the global search algorithm, while small values tend to improve local search capability. This algorithm has many advantages, including high speed convergence, flexibility, and high accuracy. In this chapter, the authors evaluate their model of AFSA for the purpose of automatic detection of diabetes.

The scalability of traditional clustering algorithm is not strong. Its capacity of processing isolated points is also weak. Artificial fish swarm algorithm (AFSA) is an algorithm for global optimization based on animal behavior. It is used in web users clustering; imitating fish feeding, clustering, pileup and random acts to construct artificial fish. Throu gh the local optimization of each individual fish, wefind the global optimal value, and thusget reasonable clustering for web access users. The actual results verify that the algorithm is effective.

The allocation issues of the location of the cargo have affected the operational efficiency of retail e-commerce warehouses tremendously. Adjusting the cargo location with the change of the order and the operation of the warehouse is a significant research area. A novel approach employing the FP-Tree and the Artificial Fish Swarm Algorithms is proposed. Firstly, energy consumption and shelf stability are employed for the location-allocation. Secondly, the association rules among product items are obtained by the FP-Tree Algorithm to mine frequent list of items. Furthermore, the frequency and the weight of product items are taken into account to ensure the local stability of the shelf during data mining. Thirdly, another method of the location-allocation is obtained with the objectives of the energy consumption and the overall shelf stability along with the frequent items stored nearby that is conducted by the Artificial Fish Swarm Algorithm. Finally, the picking order distance is obtained through two methods of the location-allocation above. The performance and efficiency of the novel introduced method have been confirmed by running the experiment. The outcomes of the simulation suggest that the introduced method has a higher performance concerning criterion called the picking order distance.

To simplify the solving process of the inverse kinematics problem of a bionic hand, a method of solving the inverse kinematics problem based on an artificial fish swarm algorithm was proposed in this paper. First, the kinematic equations of the bionic hand were established using the Denavit-Hartenberg (D-H) parameters. By selecting the appropriate decision variables and objective functions, an optimization model was established for solving the inverse kinematics problem of the bionic hand. Then, the artificial fish swarm algorithm was used to search the solution space to find the inverse solution of the position of the bionic hand. The results show that the artificial fish swarm algorithm has good convergence performance in solving the kinematic problem of the bionic hand. Besides, the accuracy of the artificial fish swarm algorithm can be improved by adjusting parameters such as the initial number of fish swarms, visual field, crowding degree, and step size.

North China is a typical developing region where the conflict between large water demand and little natural water resources exist. Thus, scientifically optimizing the allocation of regional water resources is particularly significant in this area. In this study, Haixing County, China was selected as the research region. Then, regional optimization model of water resource allocation was established, of which the objective function aims at the maximum integrated benefit of regional social and economy. In this model, two forbidden red lines of water use amount and water use efficiency were considered as the constraints, and Artificial Fish Swarm Algorithm was applied to calculate the optimization of water resource allocation. In 2020, the p = 50% total water shortage is forecast as 0.747 × 106 m3 or 2.1%. The results show that the efficiency and benefits have been improved, which illustrates that the optimization allocation of water resources through Artificial Fish Swarm Algorithm is reasonable. The research could provide a crucial technical support for regional water resource management.

Taxi plays an important role in public transportation. However, in real life, it usually takes a long time for passengers to wait for an unoccupied taxi, while number of no-load taxi is very high. Optimized scheme is required to handle global taxi dispatching. The paper presents a novel method of taxi dispatching based on Artificial Fish Swarm Algorithm (AFSA). To avoid slow convergence and falling into local optimum, standard AFSA is improved using strategies of optimizing visual range, step size and swimming behavior. We compare our improved algorithm with standard AFSA and Particle Swarm Optimization through the evaluation with real data of GPS trajectory of 12,000 taxis in Beijing and make performance analysis of effects of parameter configuration to dispatching algorithm. The experimental results show good effectiveness of our method.

Artificial fish swarm algorithm (AFSA) is one of the critical swarm intelligent algorithms. In this paper, the authors decide to enhance AFSA via diversity operators (AFSA-DO). The diversity operators will be producing more diverse solutions for AFSA to obtain reasonable resolutions. AFSA-DO has been used to solve flexible job shop scheduling problems (FJSSP). However, the FJSSP is a significant problem in the domain of optimization and operation research. Several research papers dealt with methods of solving this issue, including forms of intelligence of the swarms. In this paper, a set of FJSSP target samples are tested employing the improved algorithm to confirm its effectiveness and evaluate its execution. Finally, this paper concludes that the enhanced algorithm via diversity operators has discrepancies about the initial AFSA, and it also provided both sound quality resolution and intersected rate.