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A differential evolution algorithm for the no-idle flowshop scheduling problem with total tardiness criterion

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2011年第16期49卷 5033-5050页

作者： Tasgetiren, M. Fatih Pan, Quan-Ke Suganthan, P. N. Chua, Tay Jin Yasar Univ Dept Ind Engn Izmir Turkey Liaocheng Univ Coll Comp Sci Liaocheng 252059 Shandong Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Singapore Inst Mfg Technol Singapore 638075 Singapore

In this paper, we investigate the use of a continuous algorithm for the no-idle permutation flowshop scheduling (NIPFS) problem with tardiness criterion. For this purpose, a differential evolution algorithm with variable parameter search (vpsDE) is developed to be compared to a well-known random key genetic algorithm (RKGA) from the literature. The motivation is due to the fact that a continuous DE can be very competitive for the problems where RKGAs are well suited. As an application area, we choose the NIPFS problem with the total tardiness criterion in which there is no literature on it to the best of our knowledge. The NIPFS problem is a variant of the well-known permutation flowshop (PFSP) scheduling problem where idle time is not allowed on machines. In other words, the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions. First of all, a continuous optimisation algorithm is used to solve a combinatorial optimisation problem where some efficient methods of converting a continuous vector to a discrete job permutation and vice versa are presented. These methods are not problem specific and can be employed in any continuous algorithm to tackle the permutation type of optimisation problems. Secondly, a variable parameter search is introduced for the differential evolution algorithm which significantly accelerates the search process for global optimisation and enhances the solution quality. Thirdly, some novel ways of calculating the total tardiness from makespan are introduced for the NIPFS problem. The performance of vpsDE is evaluated against a well-known RKGA from the literature. The computational results show its highly competitive performance when compared to RKGA. It is shown in this paper that the vpsDE performs better than the RKGA, thus providing an alternative solution approach to the literature that the RKGA can be well suited.

关键词： the no-idle permutation flowshop scheduling problem differential evolution algorithm random key genetic algorithm heuristic optimisation

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Eigen-Structure Assignment-Based differential evolution algorithm for T-S Fuzzy Control Tuning Applied to Water-Turbine Governing System

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IEEE ACCESS 2021年 9卷 39322-39332页

作者： Zou, Yidong Qian, Jing Zeng, Yun Guo, Yaqing Wu, Mengyun Wang, Fangfang Mei, Hong Kunming Univ Sci & Technol Fac Met & Energy Engn Kunming 650000 Yunnan Peoples R China Kunming Univ Sci & Technol City Coll Kunming 650093 Yunnan Peoples R China

In this paper, a novel Takagi-Sugeno (T-S) fuzzy controller based on eigenstructure assignment (EA) and optimized by differential evolution algorithm (DE) is proposed, and the application of this control strategy in the hydro-turbine governing system (HTGS) is studied. Based on the non-linear model of HTGS, the corresponding state-space equations (SSE) are obtained by linearization through multiple equilibrium points. Combining with the principle of T-S fuzzy control, a T-S fuzzy model of HTGS integrating multiple SSE with generator power angle as a prerequisite is established. This paper adopts the EA method to design the fitness function in the optimization process and uses DE to complete the optimization operation to improve the performance of T-S fuzzy control in HTGS. Which makes each Linear-Quadratic-Regulator (LQR) controller gain in the fuzzy control reach the optimal under the constraint conditions. The simulation results show that compared with the standard fitness functions IAE (integral absolute error), ITAE (integral time absolute error) and ISE (integral square error), the fitness function designed using the EA method can expand the angle between the left and right eigenvectors of the T-S closed-loop system, and push the closed-loop pole from the imaginary axis to the left. That will make the adjustment time of the system shorter and the robustness against external interferences enhanced. The results also show that the proposed control strategy has better dynamic performance during the non-linear motion of HTGS and is superior to the existing control strategy.

关键词： Licenses Takagi-Sugeno model Mathematical model Fuzzy control Tuning Optimization Indexes Hydro-turbine governing system fuzzy control linear-quadratic-regulator differential evolution algorithm controller optimization

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A dual mutation differential evolution algorithm for singularly perturbed problems with two small parameters

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第6期36卷 6579-6587页

作者： Lu, Ke-Zhong Liu, Li-Bin Fang, Honglin Liu, Lili Chizhou Univ Sch Math & Comp Sci Chizhou Peoples R China Nanning Normal Univ Sch Math & Stat Nanning 530001 Peoples R China Loudi Presch Normal Educ Coll Dept Basic Lengshuijiang Peoples R China

A novel high accuracy numerical method for a singularly perturbed convection-diffusion problem with two small parameters is presented. At first, the given problem is discretized by using a rational spectral collocation method in barycentric form with sinh transformation. By sinh transform, the original Chebyshev points are mapped into the transformed ones clustered near to the boundary layers of the domain. Them, a nonlinear unconstrained optimization problem is designed to determine the widths of boundary layers in sinh transform. Finally, a dual mutation differential evolution (DMDE) algorithm is proposed to solve the nonlinear unconstrained optimization problem, and a comparison of the DMDE algorithm with other algorithms has been made, which shows that DMDE algorithm can get fast convergence and find better results.

关键词： Singularly perturbed convection-diffusion problems rational spectral collocation method optimization problem differential evolution algorithm

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A self-adaptive binary differential evolution algorithm for large scale binary optimization problems

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INFORMATION SCIENCES 2016年 367卷 487-511页

作者： Banitalebi, Akbar Abd Aziz, Mohd Ismail Aziz, Zainal Abdul Univ Teknol Malaysia Ibnu Sina Inst Sci & Ind Res UTM Ctr Ind & Appl Math Utm Skudai 81310 Johor Malaysia

This study proposes a new self-adaptive binary variant of a differential evolution algorithm, based on measure of dissimilarity and named SabDE. It uses an adaptive mechanism for selecting how new trial solutions are generated, and a chaotic process for adapting parameter values. SabDE is compared against a number of existing state of the art algorithms, on a set of benchmark problems including high dimensional knapsack problems with up to 10,000 dimensions as well as on the 15 learning based problems of the Congress on evolutionary Computation (CEC 2015). Experimental results reveal that the proposed algorithm performs competitively and in some cases is superior to the existing algorithms. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Binary optimization Large-Scale global optimization Self-adaptation differential evolution algorithm Knapsack problem

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Optimization of heliostat field layout in solar central receiver systems on annual basis using differential evolution algorithm

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ENERGY CONVERSION AND MANAGEMENT 2015年 95卷 1-9页

作者： Atif, Maimoon Al-Sulaiman, Fahad A. King Fahd Univ Petr & Minerals Dept Mech Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Ctr Res Excellence Renewable Energy CoRE RE Dhahran 31261 Saudi Arabia

Optimization of a heliostat field is an essential task to make a solar central receiver system effective because major optical losses are associated with the heliostat fields. In this study, a mathematical model was developed to effectively optimize the heliostat field on annual basis using differential evolution, which is an evolutionary algorithm. The heliostat field layout optimization is based on the calculation of five optical performance parameters: the mirror or the heliostat reflectivity, the cosine factor, the atmospheric attenuation factor, the shadowing and blocking factor, and the intercept factor. This model calculates all the aforementioned performance parameters at every stage of the optimization, until the best heliostat field layout based on annual performance is obtained. Two different approaches were undertaken to optimize the heliostat field layout: one with optimizing insolation weighted annual efficiency and the other with optimizing the un-weighted annual efficiency. Moreover, an alternate approach was also proposed to efficiently optimize the heliostat field in which the number of computational time steps was considerably reduced. It was observed that the daily averaged annual optical efficiency was calculated to be 0.5023 as compared to the monthly averaged annual optical efficiency, 0.5025. Moreover, the insolation weighted daily averaged annual efficiency of the heliostat field was 0.5634 for Dhahran, Saudi Arabia. The code developed can be used for any other selected location. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Annual optimization differential evolution algorithm Genetic algorithm Heliostat field layout system Solar power tower Solar central receiver system

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A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization

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SOFT COMPUTING 2013年第12期17卷 2293-2309页

作者： Kong, Xiangyong Ouyang, Haibin Piao, Xiaoxue Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China Queens Univ Sch Comp Kingston ON K7L 3N6 Canada

In this paper, a new adaptive grouping differential evolution (AGDE) algorithm is proposed to improve the optimization performance by implementing a prediction strategy of the constraints for constrained optimization problems. It is unnecessary to calculate the constraint values when dealing with the constraints in this method. The constraints are handled after a simple prediction according to the Lipschitz condition. When the constraints are very complex, the load arisen from the calculation of the constraint values is reduced dramatically and the feasibility of the solutions remains with great probability. In AGDE algorithm, the population is dynamically grouped to three subpopulations with respective newly-designed mutation strategy. Meanwhile, the mutation factor and crossover probability are adopted associated with the evolutionary process according to the information of the entire population. Both of the above improvements not only increase the diversity of population and speed up the convergence, but also reduce the complexity of the parameter selection. Four sets of comparative experiments are carried out to evaluate the feasibility and effectiveness of the proposed method that deals with the constraints.

关键词： Constrained optimization Prediction of the constraint values differential evolution algorithm Self-adaptive Grouping

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Application of an Adaptive differential evolution algorithm With Multiple Trial Vectors to Artificial Neural Network Training

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2011年第8期58卷 3160-3167页

作者： Slowik, Adam Koszalin Univ Technol Dept Elect & Comp Sci PL-75453 Koszalin Poland

In this paper, an application of an adaptive differential evolution (DE) algorithm with multiple trial vectors for training artificial neural networks (ANNs) is presented. The proposed method is DE-ANNT+, which is a DE-ANN Training (DE-ANNT) modified by adding a multiple trial vectors technique. DE-ANNT+ allows one to train an ANN of arbitrary architectures, and it offers a nondifferentiable neuron activation function. In contrast to a basic DE algorithm, DE-ANNT+ possesses two modifications. In DE-ANNT+, adaptive selection of control parameters and a multiple trial vectors technique are introduced. Adaptive selection means that the number of required parameters of the algorithm is decreased. The multiple trial vectors technique increases the probability of generating a better solution because a greater number of temporary solutions is generated around the existing solutions. The DE-ANNT+ algorithm, with these two modifications, is used for ANN training to classify the parity-p problem. The results from the obtained algorithm have been compared with results from the following algorithms: an evolutionary algorithm, a DE algorithm without multiple trial vectors, gradient training methods, such as error back-propagation, and the Levenberg-Marquardt method.

关键词： Artificial intelligence artificial neural network differential evolution algorithm multiple trial vectors training method

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Designing pid controllers with a minimum IAE criterion by a differential evolution algorithm

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CHEMICAL ENGINEERING COMMUNICATIONS 1998年第1期170卷 83-115页

作者： Cheng, SL Hwang, CY Natl Chung Cheng Univ Dept Chem Engn Chiayi 621 Taiwan Natl Cheng Kung Univ Dept Chem Engn Tainan 701 Taiwan

Although the integral of absolute error (IAE) has long been used as a performance measure for controller evaluation, a systematic approach is still lacking for the design of optimal PID controller with the minimum IAE performance criterion. This may be ascribed to the nonsmoothness of the IAE objective function and the lack of closed-form expression for the IAE performance index. In this paper, we propose the use of a differential evolution algorithm (DEA) to design a minimum-IAE PID controller to overcome the nondifferentiability of the IAE objective function. To achieve also a specified least relative stability margin and least damping ratio, the D-partition technique is used to identify the allowable domain in the tuning parameter space. Three examples are provided to illustrate the effectiveness of the DEA.

关键词： PID controller integral of absolute error differential evolution algorithm numerical optimization

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Scheduling flow shops using differential evolution algorithm

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2006年第2期171卷 674-692页

作者： Onwubolu, G Davendra, D Univ S Pacific Dept Engn Suva Fiji

This paper describes a novel optimization method based on a differential evolution (exploration) algorithm and its applications to solving non-linear programming problems containing integer and discrete variables. The techniques for handling discrete variables are described as well as the techniques needed to handle boundary constraints. In particular, the application of differential evolution algorithm to minimization of makespan,flowtime and tardiness in a flow shop manufacturing system is given in order to illustrate the capabilities and the practical use of the method. Experiments were carried out to compare results from the differential evolution algorithm and the genetic algorithm, which has a reputation for being very powerful. The results obtained have proven satisfactory in solution quality when compared with genetic algorithm. The novel method requires few control variables, is relatively easy to implement and use, effective, and efficient, which makes it an attractive and widely applicable approach for solving practical engineering problems. Future directions in terms of research and applications are given. (c) 2004 Elsevier B.V. All rights reserved.

关键词： scheduling flow shops differential evolution algorithm optimization

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differential evolution algorithm for Ship Path Planning in Open Waters

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JOURNAL OF MARINE SCIENCE AND TECHNOLOGY-TAIWAN 2021年第4期29卷 476-486页

作者： Kang, Yu-Tao Chen, Wei-Jiong Wang, Jin-Hui Shanghai Maritime Univ Coll Ocean Sci & Engn 1550 Hai Gang RdPudong New Area Shanghai Peoples R China

A basic problem in the design of a decision support system for ship collision avoidance is path optimization in a complex and dynamic navigational environment. This paper introduces a new path planning method based on the differential evolution (DE) algorithm to calculate a safe, optimal path for a ship. The algorithm was tested on a set of traffic scenarios typically encountered in open waters. The simulation test results prove the method's ability to solve a path planning problem for ships. We also discuss the optimality, consistency, and performance of the algorithm and provide a comparison of this algorithm with the particle swarm optimization (PSO) algorithm. The comparison results clearly show a significant advantage of the DE algorithm over the PSO algorithm in the areas of output optimality, algorithm consistency, and execution efficiency.

关键词： Collision avoidance Path planning differential evolution algorithm

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