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Research on Artificial Fish Swarm Algorithm with Cultural Evolution for Subcarrier Allocation
LIU Mingzhu,LI Xin,ZHANG Mingyu,LI Chang 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.6
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.
Bo Wang,Bairui Tao,Yanju Liu,Xiaoxin Du 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.2
In the study of mine GIS index optimization of mining decision support system, in order to reduce the overlap between the index nodes, in this paper we propose the intensity of polygon to ensure the index nodes have good geometry. In this paper, we also put forward artificial fish swarm clustering based on Voronoi diagram (AFSCV) and apply the AFSCV to mine GIS index splitting algorithm. In the end, we give the optimization rules of AFSCV. In order to verify the effectiveness of our algorithm we compare our method with traditional methods, the experimental results show that our method can improve the query performance of mine GIS database and greatly reduce the overlap which generates from reinsert after the index node split.
Yi, Ting-Hua,Zhou, Guang-Dong,Li, Hong-Nan,Zhang, Xu-Dong Techno-Press 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.54 No.2
Optimal sensor placement (OSP) is an integral component in the design of an effective structural health monitoring (SHM) system. This paper describes the implementation of a novel collaborative-climb monkey algorithm (CMA), which combines the artificial fish swarm algorithm (AFSA) with the monkey algorithm (MA), as a strategy for the optimal placement of a predefined number of sensors. Different from the original MA, the dual-structure coding method is adopted for the representation of design variables. The collaborative-climb process that can make the full use of the monkeys' experiences to guide the movement is proposed and incorporated in the CMA to speed up the search efficiency of the algorithm. The effectiveness of the proposed algorithm is demonstrated by a numerical example with a high-rise structure. The results show that the proposed CMA algorithm can provide a robust design for sensor networks, which exhibits superior convergence characteristics when compared to the original MA using the dual-structure coding method.
Optimization of Reservoir Operation using New Hybrid Algorithm
Zaher Mundher Yaseen,Hojat Karami,Mohammad Ehteram,Nuruol Syuhadaa Mohd,Sayed Farhad Mousavi,Lai Sai Hin,Ozgur Kisi,Saeed Farzin,김성원,Ahmed El-Shafie 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.11
Due to the scarcity of fresh water resources, exploiting dams’ reservoirs, based on their optimal operation, obviates construction of extra dams and high costs and satisfies downstream consumers’ water needs with high reliability. In this research, a new hybrid approach of Artificial Fish Swarm Algorithm (AFSA) and Particle Swarm Optimization Algorithm (PSOA) is used to optimize Karun-4 reservoir, increase energy production and minimize downstream water shortages. This Hybrid Algorithm (HA) brings about diversity of responses in PSOA, prevents entrapment of AFSA in local optimum traps and increases convergence speed and balances between the abilities to scan and make profit in the AFSA. This method was assessed based on reliability, vulnerability and resilience indices. In addition, based on a multi-criteria decision-making model, it was evaluated by comparing it with other evolutionary algorithms. To verify the HA, it was tested on few mathematical functions. Results indicated that the HA features performed higher reliability, lower vulnerability and resiliency, as compared with AFSA and PSOA. In addition, HA is ranked first according to the multi criteria decision making model. Further, among all the tested evolutionary methods, this new algorithm yielded the best answer for dam power plant’s objective function.
Ting-Hua Yi,Hong-Nan Li,Xu-Dong Zhang,Guang-Dong Zhou 국제구조공학회 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.54 No.2
Optimal sensor placement (OSP) is an integral component in the design of an effectivestructural health monitoring (SHM) system. This paper describes the implementation of a novelcollaborative-climb monkey algorithm (CMA), which combines the artificial fish swarm algorithm (AFSA)with the monkey algorithm (MA), as a strategy for the optimal placement of a predefined number of sensors. Different from the original MA, the dual-structure coding method is adopted for the representation of designvariables. The collaborative-climb process that can make the full use of the monkeys’ experiences to guidethe movement is proposed and incorporated in the CMA to speed up the search efficiency of the algorithm. The effectiveness of the proposed algorithm is demonstrated by a numerical example with a high-risestructure. The results show that the proposed CMA algorithm can provide a robust design for sensornetworks, which exhibits superior convergence characteristics when compared to the original MA using thedual-structure coding method.
Lung Sounds Signal Separation Model of Medical Monitoring Based on Wireless Sensor Network
Beibei Dong,Bing Han,Jingjing Yang,Wei Peng 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.12
According to the present medical monitoring system still exist the problems such as low accuracy of the condition judgment and the less range of data transmission, a kind of lung sounds signal separation model of medical monitoring is put forward based on wireless sensor network. First, using the optimization strategy of the flying speed and the effect between particles to two-way optimization for particle swarm optimization algorithm (PSOA), and then applied it to the blind source separation of lung sounds signal, in order to improve the precision of the blind source separation of lung sound signals, then carried on the optimization of artificial fish behavior through tabu search, did coverage optimization for wireless sensor network by using the improved algorithm, to expand the scope of wireless data transmission. As the simulation experiments results showed that, the proposed lung sounds signal separation model of medical monitoring based on wireless sensor network had good accuracy and large range of data transmission, and deserved to be popularized and used.
Haowen Zheng,Jun Liu,Ruihong Zhuang,Fu-tian Zhao,Meng-yang Zhen,Yue Wang,Zheng Liu 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.9
The prediction of rock fragmentation is critical to improve the efficiency and economy of blasting excavation. In this study, an attempt is made to predict the entire fragmentation size distribution using a least square support vector machine (LSSVM) model. In addition, three optimization algorithms – the bacterial foraging algorithm (BFO), artificial fish swarm algorithm (AFSA), and adaptive particle swarm optimization (APSO) – were used to determine the appropriate parameters of the LSSVM model. In the constructed LSSVM-BFO, LSSVM-AFSA and LSSVM-APSO models, the hole spacing, row spacing, change per delay and stemming were used as the input parameters, while the statistical rock fragmentation size was assigned as the output. The LSSVM model was also employed as a control group for comparing with the optimized models. The above-mentioned models were trained and tested based on a database comprising of 10 datasets collected from in-site testing of Altashi Water Control Project in China. The performance of the proposed models was compared by several statistical criteria. The viability and efficiency of the LSSVM-BFO model were confirmed with an R2 of 0.9960 and an RMSE of 1.8044, which were better than those of the LSSVM-AFSA, LSSVM-APSO and LSSVM. Last but not least, sensitivity analysis was also executed. The result of sensitivity analysis demonstrated when the size of rock fragmentation of prediction is less 80mm, the most effective parameter will be stemming; otherwise the most effective parameter will be hole spacing.