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A comparative study of nature inspired optimization algorithms on multilevel thresholding image segmentation

MULTIMEDIA TOOLS AND APPLICATIONS 2019年第24期78卷 34353-34372页

作者： Ameur, Mustapha Habba, Maryam Jabrane, Younes Cadi Ayyad Univ GECOS Lab Marrakech Morocco

In this paper, five successful nature inspired algorithms;the artificial tree algorithm (AT), the particle swarm optimization (PSO), the genetic algorithm (GA), the cultural algorithm (CA), and the cuckoo search algorithm (CS) have been compared on multilevel image thresholding. The segmentation process is based on the Levine and Nazif intra class uniformity criterion which is seen as an optimization problem. The comparison performances are in terms of the value of the objectif function, the peak signal to noise ratio (PSNR) and the computation time. Empirical results over different benchmark images for different threshold numbers reveal the robustness, the reliability and the rapidity of the cultural algorithm (CA).

关键词： Artificial tree algorithm Particle swarm optimization Genetic algorithm cultural algorithm Cuckoo search algorithm Levine and Nazif intra class uniformity criterion Multilevel image thresholding

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Rule Discovery with a Multi Objective cultural algorithm 2nd

Rule Discovery with a Multi Objective Cultural Algorithm

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2nd International Conference on Advances in Computing and Information Technology (ACITY 2012)

作者： Srinivasan, Sujatha Ramakrishnan, Sivakumar Cauvery Coll Women Dept Comp Sci Tiruchirappalli India AVVM Sri Pushpam Coll Dept Comp Sci Poondi India

ISBN: (纸本)9783642315992

cultural algorithms (CA) are evolutionary systems which utilize agent technology and which supports any evolutionary strategy like evolutionary algorithm or swarm intelligence or ant algorithms. CA uses a basic set of five knowledge sources (KS's) which are used in various animal species to guide the search toward best solutions and thus are better than evolutionary algorithms which are memory less blind search methods. The preserved knowledge in CA is disseminated throughout the system in future generations. cultural algorithms have been used effectively in solving optimization problems, in engineering rule based systems, and combined with data mining to study complex social systems. However application of cultural algorithm for multi objective optimization of classification rules is hardly found in the literature. Research gap exists in using cultural algorithm for rule mining taking the various properties of rules as objectives for optimization. In the current study a cultural algorithm framework is proposed for rule mining considering it as a multi objective optimization problem.

关键词： cultural algorithm Classification rule Multi-objective optimization

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A Novel Metaheuristic for Adaptive Signal Timing Optimization Considering Emergency Vehicle Preemption and Tram Priority

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SAE INTERNATIONAL JOURNAL OF TRANSPORTATION SAFETY 2019年第2期7卷 129-151页

作者： Alami Chentoufi, Maryam Ellaia, Rachid Univ Mohammed V Rabat Ecole Mohammadia Ingn Rabat Morocco

In this article, a novel hybrid metaheuristic based on passing vehicle search (PVS) cultural algorithm (CA) is proposed. This contribution has a twofold aim: First is to present the new hybrid PVS-CA. Second is to prove the effectiveness of the proposed algorithm for adaptive signal timing optimization. For this, a system that can adapt efficiently to the real-time traffic situation based on priority signal control is developed. Hence, Transit Signal Priority (TSP) techniques have been used to adjust signal phasing in order to serve emergency vehicles (EVs) and manage the tram priority in a coordinated tram intersection. The system used in this study provides cyclic signal operation based on a real-time control approach, including an optimization process and a database to manage the sensor data from detectors for real-time predictions of EV and tram arrival time. Then, a simulation model is developed using Arena Simulation Software to evaluate best timing plans at the intersection.

关键词： Novel hybrid metaheuristic PVS cultural algorithm Adaptive signal timing Optimization Tram priority Emergency vehicle preemption TSP techniques Traffic road simulation Arena simulation

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A cost-optimized power management strategy for combined wind thermal-pumped hydro generation considering wind power uncertainty

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第7期29卷 e12104.1-e12104.18页

作者： Bhattacharya, Bidishna Chakraborty, Niladri Mandal, Kamal K. Techno India Salt Lake Elect Engn Dept Kolkata 700091 India Jadavpur Univ Power Engn Dept Kolkata India

The trend of today's power system is to incorporate more green power in the conventional grid to mitigate the power demand and to minimize environmental pollution. This paper proposes a multi-energy system with wind thermal-pump storage plant aiming to minimize the total cost of operation. In this paper, a forecasted data of wind speed is considered. Because of the variable nature of wind power, real-time generation scheduling may lead to deviation of forecasted thermal power generation. This paper works on a power management strategy (PMS) where the gap between the day-ahead scheduled thermal power and the real-time scheduled power is minimized optimizing the operation cost. cultural algorithm (CA) is used here very efficiently in a modified UI 62 bus. It is observed from the simulation results that the proposed strategy can effectively reduce the power deviation fluctuations with reducing the operating as well as runtime cost. A comparison of total cost between CA and particle swarm optimization (PSO) is drawn in this paper to observe the efficacy of the proposed method.

关键词： cultural algorithm optimal scheduling power management strategy pumped hydro generation renewable energy wind power

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Data Augmentation using CA Evolved GANs

Data Augmentation using CA Evolved GANs

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IEEE Symposium on Computers and Communications (ISCC)

作者： Mehta, Kaitav Kobti, Ziad Pfaff, Kathryn Fox, Susan Univ Windsor Sch Comp Sci Windsor ON Canada Univ Windsor Fac Nursing Windsor ON Canada

ISBN: (纸本)9781728129990

Mining medical data images have great potential for exploring hidden patterns in the medical domain. Medical data are heterogeneous which involves images to a great extent like MRI, ECG or Stroke effects etc. Knowledge discovery from such data can improve the diagnostic technique. However, to make the machine learn from such datasets requires large data. In the low -data regime, machine learning algorithms work poorly. Data Augmentation alleviates this by using existing data more effectively, but standard data augmentation produces only limited alternative data. Recent developments in Deep Learning field is noteworthy when it comes to learning probability distribution of points through neural networks, and one of key part for such progress is because of Generative Adversarial Networks(GANs). In this paper, we propose an evolutionary training technique using a cultural algorithm(CA) for neuro-evolution of deep task oriented GANs to find the best architecture for the given dataset. This architecture will help in generating similar but completely new data images which can be further used for training diagnostic Neural Networks. We have compared our approach with the Genetic algorithm(GA) based neuro-evolution of GANs and show that CA based neuro-evolution of GANs evolves architecture which can generate a higher number of stroke -face images with better resolution when there is low data of original stroke faces.

关键词： Data Mining Generative Adversarial Networks Neuro-evolution cultural algorithm Machine Learning

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Optimization of Load Frequency Control for Non-linear System 2nd

Optimization of Load Frequency Control for Non-linear System

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2nd International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM)

作者： Oozeer, Muhammad Yasser Ramjug-Ballgobin, Rajeshree Univ Mauritius Fac Engn Reduit Mauritius

ISBN: (纸本)9783030182403;9783030182397

The aim of this paper is to apply three optimization techniques, namely Genetic algorithm (GA), cultural algorithm (CA) and Jaya-based Invasive Weed Optimization (JIWO), to the tuning of proportional-integral-derivative (PID) controllers for the load frequency control (LFC) of a non-linear two-area system, in which the frequency is to be kept uniform and tie-line power deviations are to be mitigated. The Matlab/Simulink environment is used for the implementations and simulations. The results obtained show that, while all three optimization techniques improve the responses relative to the uncompensated system, JIWO gives the best results in terms of convergence and optimality.

关键词： Load frequency control Non-linear Two-area Genetic algorithm cultural algorithm Jaya-based Invasive Weed Optimization

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A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms

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COMPUTERS IN INDUSTRY 2018年 98卷 80-89页

作者： Sabzi, Sajad Abbaspour-Gilandeh, Yousef Garcia-Mateos, Gines Univ Mohaghegh Ardabili Coll Agr Dept Biosyst Engn Ardebil Iran Univ Murcia Comp Sci & Syst Dept E-30100 Murcia 30100 Spain

Precision Agriculture is an area that can benefit from the latest advances in expert systems. One of the goals is to detect and remove weeds intelligently, so that herbicides are only sprayed in areas where weeds exist. By reducing the amount of herbicides used, the risk of contamination of crop and water resources is avoided, which could produce harmful environmental effects. In this paper, a new computer vision based expert system is presented for identifying potato plants and three different kinds of weeds (Secale cereale L., Polygonum aviculare L. and Xanthium strumarium L.) in order to perform site-specific spraying. The videos were recorded from two Marfona potato crops, with a total area of 4 ha located in Kermanshah-Iran (34 degrees 20'17.203 '' N, 46 degrees 19'56.807 '' E), taken with a moving platform with a speed of 0.13 m/s, under outdoor lighting conditions. Applying image processing, 3459 objects were extracted and used to train and test the classifiers.126 color features and 60 texture features were extracted from each object. The main contribution of the proposed approach was the application of two metaheuristic algorithms to optimize the performance of a neural network classifier: first, the cultural algorithm is used to select the five most effective features, in order to improve computational efficiency;then, the harmony search algorithm is applied to find the optimal configuration of the network. This approach has been compared with a statistical method based on linear discriminant analysis. The experimental results show that the proposed expert system achieves an excellent identification accuracy of 98.38%, requiring less than 0.8 s of execution on an average PC. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Agricultural expert systems Computer vision Weed control cultural algorithm Harmony search

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Maiden application of two degree of freedom cascade controller for multi-area automatic generation control

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2018年第9期28卷 1-1页

作者： Raju, More Saikia, Lalit Chandra Sinha, Nidul Natl Inst Technol Dept Elect Engn Silchar Assam India

In this article, a 2 degree of freedom cascade controller (2DOF-CC) is presented as the supplementary controller (SC) for multi-area hydro-thermal system. Proposed 2DOF-CC is the combination of 2DOF proportional-integral-derivative (2DOF-PID) and 2DOF proportional-derivative (2DOF-PD) controllers. The gains and other parameters of various controllers like PID, 2DOF-PID, and 2DOF-CC are optimized with cultural algorithm when considered one at a time. Comparison of dynamic responses of the system for PID, 2DOF-PID, and 2DOF-CC reveals better performance of the latter in terms of settling time and magnitude of oscillations. The study is also extended by incorporating superconducting magnetic energy storage units and explored that superconducting magnetic energy storage units effectively suppress the system dynamics against both step and random perturbations in presence of 2DOF-CC. Selection of governor speed regulation parameters (R) and frequency bias parameter (B) in the presence of 2DOF-CCs have been done in the considered system. Investigations recommend the use of higher values of R, and B should be kept at area frequency response characteristics (). The effect of double reheat turbines on system response is also investigated and reveals that use of double reheat turbines deteriorates the system responses. Sensitivity analysis validates the sturdiness of parameters of 2DOF-CC obtained at nominal conditions.

关键词： cascade controller cultural algorithm double reheat turbine frequency bias superconducting magnetic energy storage units

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cultural Evolution algorithm for Global Optimizations and its Applications

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JOURNAL OF APPLIED RESEARCH AND TECHNOLOGY 2013年第4期11卷 510-522页

作者： Kuo, H. C. Lin, C. H. Natl Taiwan Ocean Univ Dept Syst Engn & Naval Architecture Keelung 202 Taiwan

The course of socio-cultural transition can neither be aimless nor arbitrary, instead it requires a clear direction. A common goal of social species' evolution is to move towards an advanced spiritual and conscious state. This study aims to develop a population-based algorithm on the basis of cultural transition goal. In this paper, the socio-cultural model based on a system thought framework could be used to develop a cultural evolution algorithm (CEA). CEA leverage four strategies, each consists of several search methods with similar thinking. Seven benchmark functions are utilized to validate the search performance of the proposed algorithm. The results show that all of the four strategies of cultural evolution algorithm have better performance when compared with relevant literatures. Finally, the CEA was then applied to optimize two different reliability engineering problems, a Serial-Parallel System design and a Bridge System design. For the Serial-Parallel System design, the CEA achieved the exact solution with ease, and for the Bridge System design, the solution obtained by the CEA is superior to those from other literatures.

关键词： cultural algorithm Genetic algorithm Nelder-Mead's simplex method Global optimization

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IIR Digital Filter Design Based on cultural Quantum-inspired Flower Pollination algorithm 17

IIR Digital Filter Design Based on Cultural Quantum-inspired...

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IEEE 17th International Conference on Communication Technology (ICCT)

作者： Gao, Hongyuan Liang, Yansong Liu, Dandan Diao, Ming Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9781509039449

In order to resolve the multi-parameter optimization problem of infinite impulse response (IIR) digital filter design, a cultural quantum-inspired flower pollination algorithm (CQFPA) is proposed. The proposed CQFPA is a multidimensional search algorithm for optimization of real numbers, and uses mechanisms of the quantum and cultural evolution to update the quantum pollens, which may improve the capability of algorithm to find the optimal solution. Computer simulations have showed that IIR digital filters based on the CQFPA are superior to previous filters based on quantum-behaved particle swarm optimization (QPSO) and adaptive quantum-behaved particle swarm optimization (AQPSO) in the convergence speed and optimization results. The effectiveness and superiority of the CQFPA are also demonstrated by simulation experiments.

关键词： quantum flower pollination algorithm cultural algorithm IIR digital filter filter design

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