Artificial fish swarm based power allocation algorithm for MIMO‐OFDM relay underwater acoustic communication

Zhou, G.T.; Li, Youming; He, Yu-Cheng; Wang, Xiaoli; Yu, Mingchen

doi:10.1049/iet-com.2017.0149

Cited by 37 publications

(23 citation statements)

References 15 publications

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“…where Visual is the range of the artificial fish random field of vision, and x inext is the next target of an artificial fish in which the artificial fish in this position is a new antibody. Subscript P s (x i ) is the probability of antibody concentration for the immune algorithm [13], that is, the probability of selecting artificial fish to swim to the current food source, and rand() is the random variable of artificial fish swimming, with a value between 0 and 1.…”

Section: (A) Foraging Behaviormentioning

confidence: 99%

Optimization Model of EV Charging and Discharging Price Considering Vehicle Owner Response and Power Grid Cost

Song

Liu

et al. 2019

J. Electr. Eng. Technol.

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The problem of load fluctuation in the distribution network and increasing power grid cost input caused by the unpredictable behavior of electric vehicle (EV) users in response to electricity price is investigated in this paper. An optimization model method for the charging and discharging price of electric vehicles is proposed, considering the vehicle owner response and power grid cost. The rule of EV user travel is first analyzed, and the travel and battery state constraints are defined. Under the constraints of user charging and discharging behavior and battery characteristics, a user transfer rate and unit energy cost function is designed to construct a multi-objective model of charging and discharging price that minimizes electricity expenditure and avoids an increase in power grid investment. Finally, an improved multi-target fish swarm algorithm is presented to solve the model optimization problem. The example analysis shows that the proposed method can reduce the peak-valley load difference of the system and cost input of the power grid, as well as provide users with regulation ability to access the power grid at different time periods.

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Section: (A) Foraging Behaviormentioning

confidence: 99%

Optimization Model of EV Charging and Discharging Price Considering Vehicle Owner Response and Power Grid Cost

Song

Liu

et al. 2019

J. Electr. Eng. Technol.

View full text Add to dashboard Cite

show abstract

“…The results show that the computational complexity of FRFT is basically the same as that of FFT while the performance better. Zhou et al [14] design an energy allocation algorithm for relay communication of an underwater MIMO-OFDM system built on the artificial fish swarming (ASF) mechanism. In the algorithm, the data link of a single-input single-output (SISO) system is converted into virtual ones by using a singular value decomposition technique.…”

Section: Related Workmentioning

confidence: 99%

A Novel Signal Detection Algorithm for Underwater MIMO-OFDM Systems Based on Generalized MMSE

et al. 2019

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The MIMO-OFDM system fully exploits the advantages of MIMO and OFDM, effectively resisting the channel multipath fading and inter-symbol interference while increasing the data transmission rate. Studies show that it is the principal technical mean for building underwater acoustic networks (UANs) of high performance. As the core, a signal detection algorithm determines the performance and complexity of the MIMO-OFDM system. However, low computational complexity and high performance cannot be achieved simultaneously, especially for UANs with a narrow bandwidth and limited data rate. This paper presents a novel signal detection algorithm based on generalized MMSE. First, we propose a model for the underwater MIMO-OFDM system. Second, we design a signal coding method based on STBC (space-time block coding). Third, we realize the detection algorithm namely GMMSE (generalized minimum mean square error). Finally, we perform a comparison of the algorithm with ZF (Zero Forcing), MMSE (minimum mean square error), and ML (Maximum Likelihood) in terms of the BER (bit error rate) and the CC (computational complexity). The simulation results show that the BER of GMMSE is the lowest one and the CC close to that of ZF, which achieves a tradeoff between the complexity and performance. This work provides essential theoretical and technical support for implementing UANs of high performance.

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“…Compared with the PSO that updates the location by updating the speed, AFSA has stronger global optimization ability and is more suitable for solving high-dimensional optimization [19,20]. In recent years, it has been successfully applied in the fields of Data Mining, Signal Processing and Communication, etc [21][22][23], but has quite few applications in the UC problem. Meanwhile, it also has the drawback of falling into local optimum.…”

Section: Introductionmentioning

confidence: 99%

Improved Binary Artificial Fish Swarm Algorithm and Fast Constraint Processing for Large Scale Unit Commitment

Zhu

Gao

2020

IEEE Access

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As the power systems in some large developing and developed countries are getting bigger, solving large-scale unit commitment (UC) is an urgent need and significant task to ensure their economic operation and contribute green energy consummation to society. In this paper optimization models covering economy and environmental protection are established, and an improved binary artificial fish swarm algorithm (IBAFSA) is presented to solve the large-scale UC problems. The parameters of IBAFSA are improved by Lé vy flight and adaptive average visual distance to search space more actively, and a double threshold selection strategy is used to enhance the effectiveness of population evolution in the optimization. Meanwhile, a heuristic greedy search algorithm among the best individuals of all generations in the iterative process of the optimization is proposed, which is beneficial to improve computation convergence and reach the optimum solution. A fast constraints processing mechanism based on the heuristic modifying strategy of unit violation is established to handle the coupling between system spinning reserve constraint and unit minimum up and down time constraint. The effectiveness of the proposed approach is verified by the UC simulations of test systems of 10-1000 units, the IEEE 118-bus system, and a large-scale power system of 270 units. The numerical simulating results show that the proposed UC solution method can achieve the near-optimal solutions in a reasonable time, improve the economic and environmental benefits of a largescale power system, and is a general method to adapt to the changes of the objective function and constraints of a UC optimization.

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Artificial fish swarm based power allocation algorithm for MIMO‐OFDM relay underwater acoustic communication

Cited by 37 publications

References 15 publications

Optimization Model of EV Charging and Discharging Price Considering Vehicle Owner Response and Power Grid Cost

Optimization Model of EV Charging and Discharging Price Considering Vehicle Owner Response and Power Grid Cost

A Novel Signal Detection Algorithm for Underwater MIMO-OFDM Systems Based on Generalized MMSE

Improved Binary Artificial Fish Swarm Algorithm and Fast Constraint Processing for Large Scale Unit Commitment

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