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differential evolution algorithm for Ship Path Planning in Open Waters

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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Investigation on small volume and long optical path multipass cell using differential evolution algorithm

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2023年第5期65卷 1372-1378页

作者： Zhang, Mengru Chen, Chen Chen, Hongda Dong, Weihao Li, Yongyang Xu, Haozhi Li, Chunguang Jilin Univ Coll Instrumentat & Elect Engn Changchun Peoples R China Jilin Univ Coll Biol & Agr Engn Changchun 130022 Peoples R China Jilin Univ Coll Instrumentat & Elect Engn Key Lab Geophys Explorat Equipment Minist Educ China Changchun 130026 Peoples R China

To solve the contradiction between high performance and portability of tunable diode laser absorption spectroscopy (TDLAS) systems, we propose a multipass cell (MPC) design method based on differential evolution (DE) algorithm, and realize intelligent optimization design of MPC with small volume and long optical path. MPC is the core component of the TDLAS system. The optical path of MPC determines the detection accuracy of the TDLAS system. The optical path is affected by the position and angle of incident laser, mirror spacing, and other parameters. The DE algorithm is used to optimize the parameters that affect the performance of MPC, and a compact MPC with a spherical mirror diameter of 25.4 mm, a laser reflection number of 183, an optical path of 5.98 m, and a mirror spacing of 31.7 mm is designed. Under the same spherical mirror condition, the detection limit of MPC designed based on the DE algorithm is increased from 0.91 to 0.76 ppm compared with the MPC commonly used seven-ring spot pattern. This has a guiding and exemplary role in the design and development of high space utilization MPC.

关键词： differential evolution algorithm intelligent optimization multipass cell space utilization rate TDLAS system

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Prediction of partition coefficients of guanidine hydrochloride in PEG-phosphate systems using neural networks developed with differential evolution algorithm

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JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY 2015年 27卷 268-275页

作者： Pirdashti, Mohsen Movagharnejad, Kamyar Curteanu, Silvia Dragoi, Elena Niculina Rahimpour, Farshad Babol Univ Technol Fac Chem Engn Babol Sar Iran Gheorghe Asachi Tech Univ Fac Chem Engn & Environm Protect Dept Chem Engn Iasi 700050 Romania Razi Univ Fac Engn Dept Chem Engn Biotechnol Res Lab Kermanshah *** Iran

The complex problem of determining the partition coefficient of the guanidine hydrochloride in aqueous two-phase systems has been less studied. For this reason, an artificial neural network was developed to predict the partition coefficients of guanidine hydrochloride in poly (ethylene glycol) 4000/phosphate/guanidine hydrochloride/water system. The neural model (topology and internal structure) was determined using a neuro-evolutionary technique based on differential evolution algorithm, designed in different variants. This model was able to predict the guanidine hydrochloride concentrations in each phase with a mean relative error of 1.4%, which closely matched the experimental data. (C) 2015 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.

关键词： Aqueous two-phase system Guanidine hydrochloride Partition coefficient Artificial neural network differential evolution algorithm

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An approach for FIR filter coefficient optimization using differential evolution algorithm

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2015年第1期69卷 101-108页

作者： Reddy, Kotha Srinivasa Sahoo, Subhendu Kumar Birla Inst Technol & Sci Pilani Dept Elect & Elect Engn Pilani Rajasthan India Birla Inst Technol & Sci Pilani Dept Elect & Elect Engn Hyderabad Andhra Pradesh India

In this paper, we present a hardware efficient finite impulse response (FIR) filter design using differential evolution (DE) and common sub expression (CSE) elimination algorithm. With the DE algorithm, we first found a set of filter coefficients with reduced number of signed-power-of-two (SPT) terms without compromising on quality of the filter response. After obtaining coefficients, we applied CSE elimination algorithm, and determined the hardware cost in terms of adders. The filters were designed using DE for various word lengths, and the same were implemented in transposed direct form (TDF) structure. The implemented filters were synthesized in Cadence RTL compiler using UMC 90 nm technology. We compared the performances of our filters with recently best published works in terms of area, delay, power and power-delay-product (PDP). One of the proposed filters found to improve a PDP gain of 29% compared to Remez algorithm. The proposed approach showed improvements in filter design for the given specifications. (C) 2014 Elsevier GmbH. All rights reserved.

关键词： Finite impulse response filter Signed-power-of-two Coefficient optimization differential evolution algorithm

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Modified differential evolution algorithm for contrast and brightness enhancement of satellite images

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APPLIED SOFT COMPUTING 2017年 61卷 622-641页

作者： Suresh, Shilpa Lal, Shyam Natl Inst Technol Karnataka Dept Elect & Commun Engn Surathkal 575025 Mangaluru India

Satellite images normally possess relatively narrow brightness value ranges necessitating the requirement for contrast stretching, preserving the relevant details before further image analysis. Image enhancement algorithms focus on improving the human image perception. More specifically, contrast and brightness enhancement is considered as a key processing step prior to any further image analysis like segmentation, feature extraction, etc. Metaheuristic optimization algorithms are used effectively for the past few decades, for solving such complex image processing problems. In this paper, a modified differential Modified differential evolution (MDE) algorithm for contrast and brightness enhancement of satellite images is proposed. The proposed algorithm is developed with exploration phase by differential evolution algorithm and exploitation phase by cuckoo search algorithm. The proposed algorithm is used to maximize a defined fitness function so as to enhance the entropy, standard deviation and edge details of an image by adjusting a set of parameters to remodel a global transformation function subjective to each of the image being processed. The performance of the proposed algorithm is compared with ten recent state-of-the-art enhancement algorithms. Experimental results demonstrate the efficiency and robustness of the proposed algorithm in enhancing satellite images and natural scenes effectively. Objective evaluation of the compared methods was done using several full-reference and no reference performance metrics. Qualitative and quantitative evaluation results proves that the proposed MDE algorithm outperforms others to a greater extend. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Image enhancement Metaheuristics differential evolution algorithm Cuckoo search algorithm Satellite images

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Improving sampling-based image matting with cooperative coevolution differential evolution algorithm

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SOFT COMPUTING 2017年第15期21卷 4417-4430页

作者： Cai, Zhao-Quan Lv, Liang Huang, Han Hu, Hui Liang, Yi-Hui Coll Huizhou Huizhou 516007 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China

Image matting is a fundamental operator in image editing and has significant influence on video production. This paper explores sampling-based image matting technology, with the aim to improve the accuracy of matting result. The result of sampling-based image matting technology is determined by the selected samples. Every undetermined pixel needs both a foreground and background pixel to estimate whether the undetermined one is in the foreground region of the image. These foreground pixels and background pixels are sampled from known regions, which form sample pairs. High-quality sample pairs can improve the accuracy of matting results. Therefore, how to search for the best sample pairs for all undetermined pixels is a key optimization problem of sampling-based image matting technology, termed "sample optimization problem." In this paper, in order to improve the efficiency of searching for high-quality sample pairs, we propose a cooperative coevolution differential evolution (DE) algorithm in solution to this optimization problem. Strong-correlate pixels are divided into a group to cooperatively search for the best sample pairs. In order to avoid premature convergence of DE algorithm, a scattered strategy is used to keep the diversit) of population. Besides. a simple but effective evaluation function is proposed to distinguish the quality of various candidate solutions. The existing optimization method, original DE algorithm and a popular evolution algorithm are used for comparison. The experimental results demonstrate that the proposed cooperative coevolution DE algorithm can search for higher-quality sample pairs and improve the accuracy of sampling-based image matting.

关键词： Image matting Sample selection Cooperative coevolution differential evolution algorithm Diversity of population

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Production cost optimization of enzyme washing for indigo dyed cotton denim by combining Kriging surrogate with differential evolution algorithm

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TEXTILE RESEARCH JOURNAL 2020年第15-16期90卷 1860-1871页

作者： Xu, Jie He, Zhenglei Li, Sheng Ke, Wenbo Wuhan Text Univ 1 Yangguang Ave Wuhan 430200 Hubei Peoples R China Natl Local Joint Engn Lab Adv Text Proc & Clean P Wuhan Peoples R China Adv Denim Co Ltd Foshan Peoples R China

The enzyme washing process is extensively applied in the industrial production of denim garments. The process parameters of enzyme washing have significant effects on washing performances and costs. Since the relationships between the process parameters and washing performances cannot be expressed explicitly, it is impractical to determine the process parameters to obtain the optimal production cost while satisfying requirements of customers intuitively. This paper proposes an optimization methodology by combining Kriging surrogate and differential evolution (DE) algorithm to address the production cost optimization of enzyme washing for indigo dyed cotton denim. First, an experiment using Taguchi L-16 orthogonal array is conducted where temperature and concentration of cellulase enzyme are taken into consideration with processing time as the input parameters, while the washing performances (including color strength value, stiffness, and tensile strength in warp and weft directions of the washed denim fabrics) are the output responses. Second, the relationships between the inputs and outputs are established using the Kriging model. Third, the effects of the input parameters on the washing performances are analyzed, and the production cost optimization model is illustrated. Finally, a case study is given to depict the optimization process and a verification experiment is conducted to verify the effectiveness of the optimal values. On the whole, the proposed hybrid method, Kriging-DE, shows great capability of optimizing the production costs of the enzyme washing process for indigo dyed cotton denim.

关键词： enzyme washing denim production cost Kriging differential evolution algorithm

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Novel Coordination of DOCRs Using Trigonometric differential evolution algorithm

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IEEE LATIN AMERICA TRANSACTIONS 2015年第5期13卷 1605-1611页

作者： Shih, M. Y. Conde, A. UANL FIME San Nicolas De Los Garza Mexico

The coordination of directional overcurrent relays (DOCRs) has been formulated with different optimization algorithms (deterministic, heuristic, swarm, and evolutionary). The surge of studying coordination problem using heuristic, swarm and evolutionary optimization methods became popular. Because the coordination optimization problem is highly complex and non-linear, some algorithms may result in undesired operation time of DOCRs and also violation of coordination constraints. Therefore, the primary objective is to focus on the use of Trigonometric differential evolution (Tri-DE) algorithm in solving complex and nonlinear DOCR coordination. The secondary objective is to improve the Trigonometric differential evolution algorithm in the coordination problem and compare it with the original Tri-DE algorithm.

关键词： Overcurrent relay coordination differential evolution algorithm

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Optimizing protections against cascades in network systems: A modified binary differential evolution algorithm

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2012年 103卷 72-83页

作者： Zio, E. Golea, L. R. Sansavini, G. Ecole Cent Paris European Fdn New Energy Elect France Paris France Supelec Paris France Politecn Milan Dept Energy I-20133 Milan Italy

This paper addresses the optimization of protection strategies in critical infrastructures within a complex network systems perspective. The focus is on cascading failures triggered by the intentional removal of a single network component. Three different protection strategies are proposed that minimize the consequences of cascading failures on the entire system, on predetermined areas or on both scales of protective intervention in a multi-objective optimization framework. We optimize the three protection strategies by devising a modified binary differential evolution scheme that overcomes the combinatorial complexity of this optimization problem. We exemplify our methodology with reference to the topology of an electricity infrastructure, i.e. the 380 kV Italian power transmission network. We only focus on the structure of this network as a test case for the suggested protection strategies, with no further reference on its physical and electrical properties. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Critical infrastructure Network protection Line switching Cascading failure differential evolution algorithm Multi-objective optimization

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Optimum Design of Reinforced Concrete Continuous Foundation Using differential evolution algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019年第10期44卷 8401-8415页

作者： Kamal, Muhammet Inel, Mehmet Pamukkale Univ Fac Engn Dept Civil Engn POB 20070 Denizli Turkey

This paper aims to study optimum design of continuous foundation systems that are commonly used for mid-rise reinforced concrete buildings. The differential evolution algorithm (DE) was used for optimum design of reinforced concrete continuous foundation systems compatible with Turkish Standards 500 and 2007 Turkish Earthquake Code and providing certain constraints of these standards. In the optimization problem, the cross-sectional dimensions, spacing of transverse reinforcement, the diameters and numbers of reinforcement were defined as discrete variables and a total of 26 different decision variables were taken into consideration. Two different examples were created by changing the axial load and moment values transmitted from the structure. The material costs were investigated by choosing the T-beam and rectangular cross section for two examples. Besides, the sensitivity of the problem has been investigated with 2880 analyses for different parameters of the algorithm. The outcomes suggest that the optimum design of the continuous foundation systems can be achieved using T-beam sections. The study also shows that the differential evolution algorithm (DE), which is preferred in solving many engineering problems, can be used effectively in the optimum design of the continuous foundation systems.

关键词： Continuous foundation Optimum cost differential evolution algorithm Structural optimization Reinforced concrete

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