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evolutionary algorithm for Water Storage Forecasting Response to Climate Change with Small Data Sets: The Wolonghu Wetland, China

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ENVIRONMENTAL ENGINEERING SCIENCE 2012年第8期29卷 814-820页

作者： Ni, Qingwei Wang, Li Zheng, Binghui Sivakumar, M. Wuhan Univ Sci & Technol Coll Resources & Environm Engn Wuhan 430081 Peoples R China Chinese Res Inst Environm Sci State Environm Protect Key Lab Estuary & Coastal Beijing Peoples R China Univ Wollongong Fac Engn Sustainable Earth Res Ctr Wollongong NSW Australia

A novel genetic programming (GP) technique, a new method of evolutionary algorithms, was applied to a small data set to predict the water storage of Wolonghu wetland in response to the climate change in the northeastern part of China. Fourteen years (1993-2006) of annual water storage and climatic data of the wetland were used for model training and testing. Results of simulations and predictions illustrate a good fit between calculated water storage and observed values (mean absolute percent error = 9.47, r = 0.99). By comparison, a multilayer perceptron method (a popular artificial neural network model) and Grey theory model with the same data set were applied for performance estimation. It was found that GP technique had better performance than the other two methods, in both the simulation step and the predicting phase. The case study confirms that GP method is a promising way for wetland managers to make a quick estimation of fluctuations of water storage in some wetlands under the limitation of a small data set.

关键词： water storage small data set evolutionary algorithm Wolonghu wetland

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evolutionary algorithm-based optimisation of the signal-to-noise ratio for indoor visible-light communication utilising white light-emitting diode

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IET OPTOELECTRONICS 2012年第6期6卷 307-317页

作者： Ding, J. P. Ji, Y. F. Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

Visible-light communication system that utilises white light-emitting diode (LED) lighting equipment has been proposed and expected to provide uniform wireless communications and illumination simultaneously. Owing to multipath transmission channel characteristics, up to now, it is still challenging to obtain uniform signal-to-noise ratio (SNR) for each position within the indoor environment. In this study, an evolutionary algorithm (EA)-based alternative scheme is proposed to optimise the SNR distribution for indoor visible-light communication systems. Presented analysis shows that in three distributed lighting configurations, when the optical powers of individual white LED lighting equipment are dynamically controlled by tailored EA, the dynamic range of SNR referenced against the peak SNR can be reduced by up to about 24.7% whereas the uniformity illuminance ratio is improved from less than 0.63 to over 0.70 with the impact to root-mean-square delay spread is negligible. Furthermore, the relationship between the field of view of receivers and the optimisation performance is presented as well.

关键词： multipath transmission channel evolutionary algorithm optical links uniform wireless communications white light emitting diode evolutionary computation signal-to-noise ratio white LED lighting equipment Optical communication equipment Free-space optical links Optimisation techniques Light emitting diodes indoor communication light emitting diodes optical communication equipment indoor visible light communication

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evolutionary algorithm applied to time-series landing flight path and control optimization of supersonic transport

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NEURAL COMPUTING & APPLICATIONS 2023年第2期35卷 1211-1223页

作者： Kanazaki, Masahiro Setoguchi, Nao Saisyo, Ryouta Tokyo Metropolitan Univ 6-6 Asahigaoka Hino Tokyo 1920065 Japan

An evolutionary algorithm (EA) was applied in this study to optimize the landing flight path of a delta-winged supersonic transport (SST). However, it is difficult for a delta wing with a large sweepback angle to reduce the aerodynamic drag during supersonic cruising to gain sufficient lift force at low speeds, particularly during takeoff and landing. Besides, high-fidelity computational fluid dynamics is required to evaluate the flight path with a complex flowfield. This study performed an efficient flight simulation based on the Kriging model-assisted aerodynamic estimation to carry out global optimization. Then, the designs of the flight and control sequence were realized for time-series optimization of effective SST landing. To develop the EA, two design scenarios were considered;one involved only the elevator, which is an aerodynamic control surface that controls the aircraft, and the other involved introducing thrust control in addition to elevator control. In the scenario involving only elevator control, feasible solutions could not be obtained owing to the poor low-speed aerodynamic performance of the SST. This paper presents several feasible solutions enabling reasonable SST landing performance in the scenario involving the elevator and thrust controls along with descriptions regarding the optimum flight and control sequences. In addition, we analyzed the solutions by analyzing the variance to obtain qualitative information. Consequently, we determined that elevator control was considerably effective in cases with the microburst effect than in cases without the microburst effect.

关键词： evolutionary algorithm Constrained multi-objective problem Aerodynamic flight simulation Supersonic transport Time-series optimization Real-world application

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An evolutionary algorithm for the registration of 3-d surface representations

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PATTERN RECOGNITION 1999年第1期32卷 53-69页

作者： Fischer, D Kohlhepp, P Bulling, F Forschungszentrum Karlsruhe GmbH Inst Angew Informat Abt Ind Handhabungssyst D-76021 Karlsruhe Germany

The registration of 3-D surface representations is an important task for the recognition of objects and for the fusion of different views (reconstruction). Finding the transformation parameters that optimally align two non-calibrated segmented scene descriptions is a difficult, complex optimization problem. In this paper an evolutionary algorithm (EA) is presented which offers a solution to the registration problem. The fitness function which estimates the quality of the transformation parameters is based on single surface comparisons achieved by a neuro-fuzzy system. We demonstrate some registration experiments with synthetic and real scene descriptions, showing the robustness of the registration with respect to segmentation noise and partial visibility. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： evolutionary algorithm registration surface representation neuro-fuzzy softcomputing localization

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evolutionary algorithm-Based Images, Humanly Indistinguishable and Adversarial Against Convolutional Neural Networks: Efficiency and Filter Robustness

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IEEE ACCESS 2021年 9卷 160758-160778页

作者： Chitic, Raluca Topal, Ali Osman Leprevost, Franck Univ Luxembourg Fac Sci Technol & Med Dept Comp Sci L-4364 Esch Sur Alzette Luxembourg

Convolutional neural networks (CNNs) have become one of the most important tools for image classification. However, many models are susceptible to adversarial attacks, and CNNs can perform misclassifications. In previous works, we successfully developed an EA-based black-box attack that creates adversarial images for the target scenario that fulfils two criteria. The CNN should classify the adversarial image in the target category with a confidence >= 0.95, and a human should not notice any difference between the adversarial and original images. Thanks to extensive experiments performed with the CNN C = VGG-16 trained on the CIFAR-10 dataset to classify images according to 10 categories, this paper, which substantially enhances most aspects of Chitic et al. (2021), addresses four issues. (1) From a pure EA point of view, we highlight the conceptual originality of our algorithm EA(d)(target,C), versus the classical EA approach. The competitive advantage obtained was assessed experimentally during image classification. (2) We then measured the intrinsic performance of the EA-based attack for an extensive series of ancestor images. (3) We challenged the filter resistance of the adversarial images created by the EA for five wellknown filters. (4) We proceed to the creation of natively filter-resistant adversarial images that can fool humans, CNNs, and CNNs composed with filters.

关键词： Convolutional neural network evolutionary algorithm black-box attack image classification adversarial perturbation filter

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evolutionary algorithm and decisional DNA for multiple travelling salesman problem

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NEUROCOMPUTING 2015年第PART A期150卷 50-57页

作者： Wang, Peng Sanin, Cesar Szczerbicki, Edward Univ Newcastle Fac Engn & Built Environm Sch Engn Newcastle NSW 2300 Australia Gdansk Univ Technol Gdansk Poland

In the real world, it is common to face optimization problems that have two or more objectives that must be optimized at the same time, that are typically explained in different units, and are in conflict with one another. This paper presents a hybrid structure that combines set of experience knowledge structures (SOEKS) and evolutionary algorithms, NSGA-II (Non-dominated Sorting Genetic algorithm II), to solve multiple optimization problems. The proposed structure uses experience that is derived from a former decision event to improve the evolutionary algorithm's ability to find optimal solutions rapidly and efficiently. It is embedded in a smart experience-based data analysis system (SEDAS) introduced in the paper. Experimental illustrative results of SEDAS application to solve a travelling salesman problem show that our new proposed hybrid model can find optimal or close to true Pareto-optimal solutions in a fast and efficient way. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Decisional DNA (DDNA) Set of experience knowledge structure (SOEKS) Heuristics evolutionary algorithm Genetic algorithm Optimization problem

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evolutionary algorithm on General Cover with Theoretically Guaranteed Approximation Ratio

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INFORMS JOURNAL ON COMPUTING 2024年第2期36卷 305-704, C2页

作者： Zhang, Yaoyao Zhu, Chaojie Tang, Shaojie Ran, Yingli Du, Ding-Zhu Zhang, Zhao Xinjiang Univ Coll Math & Syst Sci Urumqi 830046 Xinjiang Peoples R China Zhejiang Normal Univ Sch Math Sci Jinhua 321004 Zhejiang Peoples R China Univ Texas Dallas Naveen Jindal Sch Management Richardson TX 75080 USA Univ Texas Dallas Dept Comp Sci Richardson TX 75080 USA

Theoretical studies on evolutionary algorithms have developed vigorously in recent years. Many such algorithms have theoretical guarantees in both running time and approximation ratio. Some approximation mechanism seems to be inherently embedded in many evolutionary algorithms. In this paper, we identify such a relation by proposing a unified analysis framework for a global simple multiobjective evolutionary algorithm (GSEMO) and apply it on a minimum weight general cover problem, which is general enough to subsume many important problems including the minimum submodular cover problem in which the submodular function is real-valued, and the minimum connected dominating set problem for which the potential function is nonsubmodular. We show that GSEMO yields theoretically guaranteed approximation ratios matching those achievable by a greedy algorithm in expected polynomial time when the potential function g is polynomial in the input size and the minimum gap between different g-values is a constant.

关键词： approximation ratio evolutionary algorithm minimum weight general cover minimum submodular cover minimum connected dominating set

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evolutionary algorithm based selective harmonic elimination for three-phase cascaded H-bridge multilevel inverters with optimized input sources

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JOURNAL OF POWER ELECTRONICS 2020年第5期20卷 1172-1183页

作者： Kumar, S. Sankara Iruthayarajan, M. Willjuice Sivakumar, T. Natl Engn Coll Dept Elect & Elect Engn Kovilpatti India

Multilevel inverters are finding wide application in electric drives, traction, flexible AC transmission systems (FACTS) and renewable energy systems. A cascaded H-bridge type multilevel inverter (CHBMLI) produces a near sinusoidal output voltage with lower switching stress and a higher conversion efficiency than the other types of MLIs. The Selective Harmonic Elimination (SHE) strategy is used to eliminate lower-order harmonic profiles and to regulate the fundamental component in the output voltage. SHE has the advantages of low switching frequency, low switching losses and low stress. In this paper, the modulation index and input voltage values are also considered as optimization variables along with the conventional switching angles to analyze the performance improvement in selective harmonic elimination. Heterogeneous Comprehensive Learning Particle Swarm Optimization (HCLPSO) and Gravitational Search algorithm (GSA) algorithms are used to find the optimal switching angles, modulation index and input voltage source values for minimizing the lower-order harmonics present in the output voltage of seven-level and eleven-level CHBMLIs, while maintaining the fundamental component of the output voltage. The results obtained from MATLAB simulations and an experimental setup clearly indicate that the proposed HCLPSO-based multilevel inverter provides better performance when compared with GSA, firefly and Differential Search algorithm (DSA)-based MLIs.

关键词： Cascaded H-bridge multilevel inverters Selective harmonic elimination evolutionary algorithm Harmonics THD

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evolutionary algorithm-based face verification

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PATTERN RECOGNITION LETTERS 2004年第16期25卷 1857-1865页

作者： Jang, JS Han, KH Kim, JH Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Sci Taejon 305701 South Korea Samsung Elect Co Ltd Digital Media R&D Ctr Suwon 442742 Gyeonggi South Korea

This paper proposes a novel face verification method using principal components analysis (PCA) and evolutionary algorithm (EA). Although PCA related algorithms have shown outstanding performance, the problem lies in making decision rules or distance measures. To solve this problem, quantum-inspired evolutionary algorithm (QEA) is employed to find out the optimal weight factors in the distance measure for a predetermined threshold value which distinguishes between face images and non-face images. Experimental results show the effectiveness of the proposed method through the improved verification rate and false alarm rate. (C) 2004 Elsevier B.V. All rights reserved.

关键词： face verification principal components analysis evolutionary algorithm quantum-inspired evolutionary algorithm

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evolutionary algorithm using adaptive fuzzy dominance and reference point for many-objective optimization

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SWARM AND evolutionary COMPUTATION 2019年 44卷 1092-1107页

作者： Das, Siddhartha Shankar Islam, Md Monirul Arafat, Naheed Anjum Bangladesh Univ Engn & Technol Dept Comp Sci & Engn Dhaka Bangladesh Natl Univ Singapore Sch Comp Singapore Singapore

Many-objective optimization is very important for numerous practical applications. It, however, poses a great challenge to the Pareto dominance based evolutionary algorithms. In this paper, a fuzzy dominance based evolutionary algorithm is proposed for many-objective optimization. The essence of the proposed algorithm is that it adaptively determines a fuzzy membership function for each objective of a given many-objective optimization problem and employs preferred reference points for clustering evolved solutions. Our algorithm uses distribution information of the evolving population to find preferred reference points from a set of generated reference points. The aim of using such preferred points is to emphasize both convergence and diversity of all the evolved solutions by maintaining cluster uniformity and handling irregular Pareto front. Extensive experimentation has been performed on a number of benchmark problems in evolutionary computing, including nine Waking-Fish-Group and seven Deb-Thiele-Laumanns-Zitzler benchmark problems having 2 to 25 objectives. In addition, we have investigated the performance of the proposed algorithm on three instances of degenerate Rectangle Problems. The experimental results show that the proposed algorithm is able to solve many-objective optimization problems efficiently, and it is compared favorably with the other evolutionary algorithms devised for such problems. A parametric study is also provided to understand the influence of a key parameter of the proposed algorithm.

关键词： Many-objective optimization evolutionary algorithm Pareto dominance Fuzzy dominance Reference point

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