The classic differential evolution algorithm and its convergence properties

Knobloch, Roman; Mlýnek, Jaroslav; Srb, Radek

doi:10.21136/am.2017.0274-16

Cited by 29 publications

(19 citation statements)

References 6 publications

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“…The evolutionary technique, such as genetic algorithm (GA), presents a significant shortcoming as its convergence speed slows down near the global optimum [18,19]. Similarly, particle swarm optimization (PSO) and differential evolution (DE) operate with a high convergence rate but offer premature convergence which is a critical drawback [20,21]. In the literature, penalty-based approaches, such as Lagrangian technique and logarithmic-barrier function (LBF) method, have proven their potentials to obtain the optimum solution with a high convergence speed [22,23].…”

Section: Methodsmentioning

confidence: 99%

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FPGA Implementation of an Improved Reconfigurable FSMIM Architecture Using Logarithmic Barrier Function Based Gradient Descent Approach

Das

Panchanathan

2019

International Journal of Reconfigurable Computing

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Recently, the Reconfigurable FSM has drawn the attention of the researchers for multistage signal processing applications. The optimal synthesis of Reconfigurable finite state machine with input multiplexing (Reconfigurable FSMIM) architecture is done by the iterative greedy heuristic based Hungarian algorithm (IGHA). The major problem concerning IGHA is the disintegration of a state encoding technique. This paper proposes the integration of IGHA with the state assignment using logarithmic barrier function based gradient descent approach to reduce the hardware consumption of Reconfigurable FSMIM. Experiments have been performed using MCNC FSM benchmarks which illustrate a significant area and speed improvement over other architectures during field programmable gate array (FPGA) implementation.

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Section: Methodsmentioning

confidence: 99%

“…The evaluation of the derivative term (i.e., ∇ ( , )) is required to move in the gradient descent direction as shown in (12). The needed derivative term is obtained by putting (20), (21), (22), and (23) into (18). Hence, ∇ ( , ) is defined by (24).…”

Section: Methodsmentioning

confidence: 99%

FPGA Implementation of an Improved Reconfigurable FSMIM Architecture Using Logarithmic Barrier Function Based Gradient Descent Approach

Das

Panchanathan

2019

International Journal of Reconfigurable Computing

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“…In this section, the asymptotic convergence of the local optimization is first analyzed; on this basis, the global convergence analysis is presented as well. [45]. Suppose that at the k th time horizon, the optimal solution set of the cost function J k can be defined as follows:…”

Section: Convergence Analysis Of De Algorithmmentioning

confidence: 99%

Recurrent Neural Network-Based Model Predictive Control for Multiple Unmanned Quadrotor Formation Flight

Zhang

Sun

Deng

2019

International Journal of Aerospace Engineering

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This paper presents a dynamical recurrent neural network- (RNN-) based model predictive control (MPC) structure for the formation flight of multiple unmanned quadrotors. A distributed hierarchical control system with the translation subsystem and rotational subsystem is proposed to handle the formation-tracking problem for each quadrotor. The RNN-based MPC is proposed for each subsystem, where the RNN is introduced as the predictive model in MPC. And to improve the modeling accuracy, an adaptive updating law is developed to tune weights online for the RNN. Besides, the adaptive differential evolution (DE) algorithm is utilized to solve the optimization problem for MPC. Furthermore, the closed-loop stability is analyzed; meanwhile, the convergence of the DE algorithm is discussed as well. Finally, some simulation examples are provided to illustrate the validity of the proposed control structure.

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“…In article [6] the theoretical concepts are introduced that are then used to prove the asymptotic convergence of MDEA.…”

Section: -3mentioning

confidence: 99%

Convergence rate of the modified differential evolution algorithm

Knobloch

Mlýnek

Srb³

2017

AIP Conference Proceedings

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Abstract. Differential evolution algorithms represent an efficient framework to solve complicated optimization tasks with many variables and complex constraints. Nevertheless, the classic differential evolution algorithm does not guarantee the convergence to the global minimum of the cost function. Therefore, the authors developed a modification of this algorithm that ensures asymptotic global convergence. The article provides a comparison of the ability to identify the global minimum of the cost function for the following three algorithms: the classic differential evolution algorithm, the above mentioned modified differential evolution algorithm and an algorithm of random sampling enhanced by a hill climbing procedure. We designed a series of numerical experiments to perform this comparison. The results indicate that the classic differential evolution algorithm is in general an extremely poor global optimizer (global minimum found in 2% of cases). On the other hand the performance of the modified differential evolution algorithm was considerably better (global minimum found in 83% of cases).

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The classic differential evolution algorithm and its convergence properties

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Cited by 29 publications

References 6 publications

FPGA Implementation of an Improved Reconfigurable FSMIM Architecture Using Logarithmic Barrier Function Based Gradient Descent Approach

FPGA Implementation of an Improved Reconfigurable FSMIM Architecture Using Logarithmic Barrier Function Based Gradient Descent Approach

Recurrent Neural Network-Based Model Predictive Control for Multiple Unmanned Quadrotor Formation Flight

Convergence rate of the modified differential evolution algorithm

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