Hybrid Artificial Fish Swarm Research Articles

Design of programmable Gaussian‐derived wavelet filter for wearable biomedical sensor

AbstractTo provide multiple options for specific application in biosignal processing, the programmable Gaussian‐derived Gm‐C wavelet filter has been proposed. To realize the programmable characteristic, the analog wavelet base with one numerator term is constructed by using hybrid artificial fish swarm algorithm. Also, the inverse follow‐the‐leader feedback Gm‐C filter structure with a switch array is employed. By programming switches only, Gaussian and Marr wavelet transforms can be realized flexibly with all component parameters unchanged. The seventh‐order programmable wavelet filter is designed as an example. Simulation results show that power consumption is only 141.68 pW at scale a = 0.1, with dynamic range of 42.6 dB and figure‐of‐merit of 2.05 × 10−13. Due to the programmability, the proposed design method can implement two wavelet filters with very low circuit complexity.

Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Secured Free Scale Networks against Malicious Attacks

Due to the recent proliferation of cyber-attacks, highly robust wireless sensor networks (WSN) become a critical issue as they survive node failures. Scale-free WSN is essential because they endure random attacks effectively. But they are susceptible to malicious attacks, which mainly targets particular significant nodes. Therefore, the robustness of the network becomes important for ensuring the network security. This paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization (RHAFS-SA) Algorithm. It is introduced for improving the robust nature of free scale networks over malicious attacks (MA) with no change in degree distribution. The proposed RHAFS-SA is an enhanced version of the Improved Artificial Fish Swarm algorithm (IAFSA) by the simulated annealing (SA) algorithm. The proposed RHAFS-SA algorithm eliminates the IAFSA from unforeseen vibration and speeds up the convergence rate. For experimentation, free scale networks are produced by the Barabási– Albert (BA) model, and real-world networks are employed for testing the outcome on both synthetic-free scale and real-world networks. The experimental results exhibited that the RHAFS-SA model is superior to other models interms of diverse aspects.

Global maximum power point tracking for solar power systems using the hybrid artificial fish swarm algorithm

Maximum power point tracking (MPPT) techniques are used to maintain photovoltaic modules operating points at the local maximum power points under non-uniform irradiance conditions (NUIC). For global maximum power point tracking (GMPPT) within an appropriate period, a hybrid artificial fish swarm algorithm (HAFSA) is proposed in this paper, which was developed using particle swarm optimization (PSO) to reformulate AFSA and improve its principal parameters. Simulation results show that under NUIC, compared with PSO and AFSA, the proposed algorithm has better performance with respect to convergence speed and convergence accuracy. Under NUIC, the average convergence times for 1000 simulation experiments completed with PSO, AFSA, and HAFSA are 0.4830 s, 0.4003 s and 0.3152 s respectively, and the average tracking time of the HAFSA algorithm is reduced by 34.74% and 21.26% compared with PSO and AFSA, respectively. The convergence times of the velocity inertia m relative constant and linear decrement method decreased by 35.48% and 8.19%, the convergence time of the Visual relative constant mode decreased by 10.16%, and the convergence time of the Step relative constant mode decreased by 17.88%. The proposed GMPPT algorithm is simulated in MATLAB, and the algorithm tracks GMPP with excellent efficiency and fast speed.

Damage detection of carbon fiber reinforced plastic π adhesive-bonded joints based on the peeling stress reconstruction with hybrid artificial fish swarm and frog leaping algorithm

In this article, we first research on the relationship between joint damage evolution and peeling stress distribution of carbon fiber reinforced plastic π adhesive-bonded joints by finite element m...

Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy

The cooperative path planning problem of multiple mobile robots in an unknown indoor environment is considered in this article. We presented a novel obstacle avoidance and real-time navigation algorithm. The proposed approach consisted of global path planning and local path planning via HAFSA (hybrid artificial fish swarm algorithm) and an expansion logic strategy. Meanwhile, a kind of scoring function was developed, which shortened the time of local path planning and improved the decision-making ability of the path planning algorithm. Finally, using STDR (simple two dimensional robot simulator) and RVIZ (robot operating system visualizer), a multiple mobile robot simulation platform was designed to verify the presented real-time navigation algorithm. Simulation experiments were performed to validate the effectiveness of the proposed path planning method for multiple mobile robots.

Hybrid Biogeography Based Optimization—Multilayer Perceptron for Application in Intelligent Medical Diagnosis

Manual medical diagnosis which depends on physicians’ knowledge to diagnose the presence of the symptoms of the disease is impracticable. Therefore, automatic and intelligent medical diagnosis has become very useful to the physicians when dealing with huge amount and high dimensional medical database. In this paper, we have proposed hybridization method by improving MLP learning with Biogeography Based Optimization (BBO) to be adopted and applied in five medical diagnoses. Comparisons are done between the following proposed methods: hybrid Particle Swarm Optimization (PSO) and MLP; hybrid Genetic Algorithm (GA) and MLP; and hybrid Artificial Fish Swarm Algorithm (AFSA) and MLP using the same standard parameters. Results are analyzed in terms of their classification accuracy. The performance of each method was evaluated based on their specificity, sensitivity, accuracy and precision. The findings disclose that BBO is a promising optimization tool in enhancing MLP learning with better average accuracy and convergence rate in intelligent medical diagnosis.

A novel fuzzy time series forecasting method based on the improved artificial fish swarm optimization algorithm

Recently, many forecasting methods have been proposed for the analysis of fuzzy time series. The main factors that affect the results of the forecasting of these models are partition universe of discourse and determination of fuzzy relations. In this paper, a novel fuzzy time series forecasting method which uses a hybrid artificial fish swarm optimization algorithm for the determination of interval lengths is proposed. Firstly, we introduce the chemotactic behavior of Bacterial foraging optimization into foraging behavior. Secondly, the Levy flight is used as the mutation operator for a mutation strategy. Finally, the new proposed method is applied to a fuzzy time series forecasting and the experimental results show that the proposed model obtain better forecasting results than those of other existing models. It proves the feasibility and validity of above-mentioned approaches.

A Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Automatic Identification of Clusters

This paper introduces an alternative fuzzy clustering method that does not require fixing the number of clusters a priori and produce reliable clustering results. This newly proposed method empowers the existing Improved Artificial Fish Swarm algorithm (IAFSA) by the simulated annealing (SA) algorithm. The hybrid approach can prevent IAFSA from unexpected vibration and accelerate convergence rate in the late stage of evolution. Computer simulations are performed to compare this new method with well-known fuzzy clustering algorithms using several synthetic and real-life datasets. Our experimental results show that our newly proposed approach outperforms some other well-known existing fuzzy clustering algorithms in terms of both accuracy and robustness.

A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzy c-Means Clustering.

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).

A Novel Self-Adaptation Hybrid Artificial Fish-Swarm Algorithm

In this paper, the social behaviors of fish swarm were classified in four ways: foraging behavior, stray behavior, reproductive behavior, and escaping behavior. Inspired by this, a novel artificial fish swarm algorithm (NAFSA) was proposed, which integrated the merits of the self-adaptation strategy, mutation strategy and hybrid strategy into the social behaviors of fish swarm. In the case of mutation strategy, the cloud theory was introduced into the escaping behavior, and the basic cloud generator was used as the mutation operator because of the properties of randomness and stable tendency of a normal cloud model. For the hybrid strategy, the selection, crossover and mutation operator in evolutionary algorithm were applied to define the reproductive ability of an artificial fish. Furthermore, the parameters of Step and Visual were developed in forms of hyperbolic tangent function to adjust the optimize performance dynamically during iterations process. Finally, ten standard test functions are used as the benchmark to validate the effectiveness of the NAFSA. Experimental results confirmed the superiority of NAFSA in terms of both solution quality and convergence speed.

A Winner Determination Algorithm for Combinatorial Auctions Based on Hybrid Artificial Fish Swarm Algorithm

Abstract The problem of winner determination in combinatorial auctions is a hotspot electronic business, and a NP hard problem. A Hybrid Artificial Fish Swarm Algorithm(HAFSA), which is combined with First Suite Heuristic Algorithm (FSHA) and Artificial Fish Swarm Algorithm (AFSA), is proposed to solve the problem after probing it base on the theories of AFSA. Experiment results show that the HAFSA is a rapidly and efficient algorithm for The problem of winner determining. Compared with Ant colony Optimization Algorithm, it has a good performance with broad and prosperous application.

Hybrid artificial fish swarm algorithm for global optimization problems

Based on the Powell algorithm and Adaptive Artificial Fish Swarm Algorithm (AAFSA), a hybrid artificial fish swarm algorithm (AAFSA-Powell) for global optimization problems was presented by inserting Powell algorithm into AAFSA. As a local search operator, Powell algorithm has strong local search ability, whereas artificial fish swarm algorithm has global convergence. Therefore, the hybrid algorithm is capable of improving the global search ability of the algorithm, as well as reducing the computational burden. The numerical experimental results show that the algorithm can converge quickly with high adjustment.