Experimental Study on Differential Evolution Strategies

Ao, Youyun; Chi, Hongqin

doi:10.1109/gcis.2009.31

Cited by 37 publications

(13 citation statements)

References 11 publications

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“…The load demand (D) of the problem is 10500 MW. Standard DE [1], DE/local-to-best/1 [10] [9], DE/rand/1 with per-vector-dither [10] and the proposed DWM-DE are used to solve the ELD-VPL problem. The following simulation conditions are used:…”

Section: Experiments and Resultsmentioning

confidence: 99%

Economic Load Dispatch using Differential Evolution with double wavelet mutation operations

Lai

Leung

Ling

2010

IEEE Congress on Evolutionary Computation

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in this paper, a modified Differential Evolution (DE) that incorporates double wavelet-based operations is proposed to handle a load flow problem. The wavelet based operation is embedded in the DE mutation and crossover operation. In the DE mutation operation, the scaling factor is controlled by a wavelet function. In the DE crossover operation, a wavelet-based mutation operation is embedded in it. The trial population vectors are thus modified by the wavelet function. The double wavelet mutations are applied in order to enhance DE in exploring the high-dimension solution space more effectively for better solution quality and stability. The proposed DE algorithm is employed to solve the Economic Load Dispatch with Valve-Point Loading (ELD-VPL) Problem.It is shown empirically that the proposed method out-performs significantly the conventional methods in terms of convergence speed, solution quality and solution stability.

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Section: Experiments and Resultsmentioning

confidence: 99%

Economic Load Dispatch using Differential Evolution with double wavelet mutation operations

Lai

Leung

Ling

2010

IEEE Congress on Evolutionary Computation

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“…For optimal controller design, based on three different optimization criteria for obtaining the desired type of control, the differential evolution global optimum search algorithm (Storn & Price, 1997;Ao & Chi, 2009;Gao-yang, 2010) was applied. For optimization-aided design of the control regimes, three elements of the system were combined: BCD mathematical model, cascaded P-PID controller, and differential evolution search algorithm, used for minimizing the specified optimization criteria or objective function.…”

Section: Optimization-aided Design Of the Controllermentioning

confidence: 99%

Optimized Diving Velocity and Depth Control for Diver's Automatic Buoyancy Control Device

Rižnar¹,

Valenko²,

Golob³

et al. 2015

mar technol soc j

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In this article, the design and multiobjective optimization of a non-model-based proportional-integral-derivative (PID) control system for diver's buoyancy control device (BCD) is presented. Changing and maintaining depth is an essential task in diving, for which the diver typically uses manual control of two pneumatic valves. An effective automatic control of the BCD can be beneficial in specific diving circumstances; i.e., in amateur diving, safety stop procedures, automatic ascending in case of critical life functions of the diver, poor visibility, when both of the diver's hands are required for other tasks, etc. In addition to multiple control problems caused by internal nonlinear dynamics of the BCD hardware, three main control aspects were considered: position or depth of the BCD; vertical, meaning the ascending or descending velocity of the BCD; and air supply consumption. From that, we examined and implemented a combined depth and vertical velocity control, which was configured as a cascaded controller setup with outer depth and inner vertical velocity control loops. Such controller setup could easily implement the specified limitations of the BCD's vertical velocity, which are critical for proper decompression procedure while the diver is ascending. In a simulation environment the optimization of the controller parameters was achieved with a differential evolution global optimum search algorithm. Obtained results were then compared to a manually driven BCD in a simulated environment.

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“…In the process of reconfiguration, to avoid a huge number of power flow calculations due to early Optimal Flow Pattern Algorithm and Branch Exchange Algorithm, various artificial intelligence algorithms are increasingly used in distribution network reconfiguration. Differential evolution algorithm, which is applied to distribution network reconfiguration, for its simple principle, few controlled parameters and robust features, attracted more attention from scholars [1,2]. Ching-Tzong Su et al [3] proposed an effective method of network reconfiguration to reduce power loss and enhance the voltage profile by the improved mixed-integer hybrid differential evolution (MIHDE) method for distribution systems.…”

Section: Introductionmentioning

confidence: 99%

Distribution Network Reconfiguration Based on Differential Evolution Algorithm

Liu¹,

Wang²,

Xiao³

2011

2011 Third Pacific-Asia Conference on Circuits, Communications and System (PACCS)

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On the base of the special configuration of distribution network, solved generating a large number of infeasible solutions in reconfiguration of distribution network by using cyclic coding strategy, this paper proposed corresponding decimal encoding and its mutation, crossover and selection operators. For the previous discussion about radial structure decision, further cleared its definition, figured out the issue that reverse of current flow in part of network branches after reconfiguration, and finally simulate the IEEE 33-bus system based on differential evolution algorithm. The result shows that the method proposed is fast and effective.

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Experimental Study on Differential Evolution Strategies

Cited by 37 publications

References 11 publications

Economic Load Dispatch using Differential Evolution with double wavelet mutation operations

Economic Load Dispatch using Differential Evolution with double wavelet mutation operations

Optimized Diving Velocity and Depth Control for Diver's Automatic Buoyancy Control Device

Distribution Network Reconfiguration Based on Differential Evolution Algorithm

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