DOCSLIB.ORG

Modeling and Optimization of Urban Rail Transit Scheduling with Adaptive Fruit Fly Optimization Algorithm

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Modeling and Optimization of Urban Rail Transit Scheduling with Adaptive Fruit Fly Optimization Algorithm Open Phys. 2019; 17:888–896 Research Article Jin Li, Guangyin Xu*, Zhengfeng Wang, and Zhanwu Wang Modeling and optimization of urban rail transit scheduling with adaptive fruit fly optimization algorithm https://doi.org/10.1515/phys-2019-0094 Received Oct 23, 2019; accepted Dec 02, 2019 1 Introduction Abstract: Despite the rapid development of urban rail tran- Along with the explosive growth of economy and the ris- sit in China, there are still some problems in train oper- ing level of urbanization in China, urban rail transit plays ation, such as low efficiency and poor punctuality. Tore- an increasingly important role in domestic public trans- alize a proper allocation of passenger flows and increase port system and it develops rapidly in major and medium train frequency, this paper has proposed an improved ur- cities. Its advantages include less pollution, higher pas- ban rail transit scheduling model and solved the model senger carrying capacity, good travel speed and punctu- with an adaptive fruit fly optimization algorithm (AFOA). ality. As one of the core tasks of urban rail transit, depar- For the benefits of both passengers and operators, the ture scheduling heavily affects the operating costs, service shortest average waiting time of passengers and the least quality and transport efficiency. Therefore, the optimiza- train frequency are chosen as the optimization objective, tion of urban rail transit scheduling has become a bench- and train headway is taken as the decision variable in mark problem in this field. the proposed model. To obtain higher computational effi- How to accurately and efficiently allocate reasonable ciency and accuracy, an adaptive dynamic step size is built train intervals (train headway) becomes one of the op- in the conventional FOA. Moreover, the data of urban rail timization tasks for urban rail transit scheduling. Since transit in Zhengzhou was simulated for case study. The scheduling optimization is a NP-complete problem, the comparison results reveal that the proposed AFOA exhibits traditional methods are ineffective to solve it. The nature- faster convergence speed and preferable accuracy than the inspired metaheuristic algorithm is proved to be an effi- conventional FOA, particle swarm optimization, and bacte- cient way of optimizing public transit scheduling. In [1– rial foraging optimization algorithms. Due to these superi- 3], a bus scheduling optimization model is built on the ba- orities, the proposed AFOA is feasible and effective for op- sis of actual passenger flows and solved by an improved timizing the scheduling of urban rail transit. genetic algorithm. Ref. [4] takes into account the number of trains, waiting time and deadheading time to establish Keywords: Urban rail transit; Scheduling model; Train a mixed integer programming model and proposes an im- headway; Fruit fly optimization algorithm proved fruit fly optimization algorithm for the optimiza- PACS: 89.40.Bb, 07.05.Tp tion of regional bus scheduling. In Ref. [5], a modified bac- terial foraging optimization algorithm is used to decide the headways of bus services. However, the factors consid- ered wherein are not comprehensive enough for all prac- tical purposes. Ref. [6] applies the integer programming theory to study electric bus timetables. It has established a network model to optimize the solution with column gen- eration algorithm and improved the efficiency of electric bus timetables. In ref. [7], a scheduling model is estab- *Corresponding Author: Guangyin Xu: College of Mechanical & lished based on the framework of intelligent bus schedul- Electrical Engineering, Henan Agricultural University, Zhengzhou ing system. It uses the hybrid of particle swarm optimiza- 450002, China; Email: [email protected] tion and ant colony optimization to prove the feasibility. Jin Li, Zhengfeng Wang, Zhanwu Wang: College of Mechanical & In ref. [8], genetic algorithm is used to solve the coordi- Electrical Engineering, Henan Agricultural University, Zhengzhou nation optimization model of departure times of differ- 450002, China Open Access. © 2019 J. Li et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 License Modeling and optimization of urban rail transit scheduling Ë 889 ent lines. Ref. [9] has developed an application-oriented sales are recorded as company income. The price of model to estimate the waiting times so as to calculate subway ticket for one-way travel is fixed and pas- bus departure time intervals. Ref. [10] presents a mixed- sengers could go anywhere by transferring between integer-programming optimization model for solving the trains. problem of schedule synchronization and minimizing the interchange waiting times of all passengers. Ref. [11] em- ploys genetic algorithm to solve the optimization model of 2.2 Scheduling model train operation scheme during rush hour. Ref. [12] adopts an improved Lagrangian algorithm to solve the schedule The scheduling of urban rail transit is a comprehensive optimization model of train. Ref. [13] uses particle swarm work. The goal of determining the train headway is to effi- optimization algorithm to optimize the operation adjust- ciently use the transport capacity so that lower operating ment model in urban rail transit. Ref. [14] combines linear costs and better passenger experience could be achieved weighting method and genetic algorithm to optimize the at the same time. timetable of urban rail transit. Passengers’ satisfaction depends mainly upon com- At present, intelligent optimization algorithms have muting time and waiting time. As it is assumed that trains been widely used in bus transit scheduling, but few new al- travel at a constant speed and travel time is not considered, gorithms are applied to optimize urban rail transit schedul- waiting time becomes the key factor to ensure passengers’ ing. Based on the real condition of urban rail transit in satisfaction. The less the waiting time, the more passenger Zhengzhou, this paper has proposed an improved schedul- will get satisfied. Since passengers arrive at different times, ing model and solved it with an adaptive fruit fly optimiza- it is difficult to decompose the waiting time of individual tion algorithm (AFOA). The comparison results validate passengers. Therefore, average waiting time is used in this the feasibility and effectiveness of the proposed AFOA. paper. As the passenger flows are evenly distributed, pas- sengers’ waiting time equals to half of the headway time. 1. The satisfaction model of passengers is as follows: 2 Model of urban rail transit K P mk k=1 scheduling fp = Ts (1) (hmax+hmin)/2 2.1 Model hypotheses where fp denotes passengers’ satisfaction; k is the kth period in a day; K is the set for time segments and Urban rail transit system is much different from the bus th k = f1, 2, ...Kg; mk is the departure times in the k transit system. It requires less factors to optimize the train period; Ts is the train service duration in a day; hmax schedule than the bus transit system. Road smoothness, and h are the maximum and minimal departure traffic jam and overtaking station are not considered inthe min interval, respectively. analysis of the train headway for urban rail transit system. 2. Model of enterprise costs Therefore, the results are easier and more accurate. How- The operation costs of an enterprise mainly include ever, to make the model more universal, some external fac- fixed personnel and equipment inputs. The number tors need to be appropriately simplified. The following hy- of trains is the most influential factor to the costs of potheses are proposed: an enterprise. Since there is enough travel capacity 1. The stopover time is fixed; the passenger flow in to carry passengers, the costs of an enterprise will each period is uniformly distributed; the time when be reduced as the number of trains declines. There- passengers get stranded at the station is not consid- fore, the model of enterprise costs is to minimize the ered; and the boarding time and the time from open- number of trains in use. ing to closing the doors are not taken into account; K J P P 2. Trains travel at a constant speed and no accident oc- (mk ρkj ∆tk)∆tk 2 k=1 j=1 curs; fv η = K J (2) 3. Since urban rail transit system is a public trans- P P ukj(hmax + hmin) 2 port infrastructure, its construction costs, revenue k=1 j=1 and expenditure are neglected, and its operating ex- th where fv denotes the enterprise cost, j is the j sta- pense only include fuel and staff wages. Only ticket tion, J is the station set and j = f1, 2, ...Jg, ρkj is 890 Ë J. Li et al. the passenger flow density at the jth station during will decrease, and η equals to the actual passenger th th the k period, ∆tk is the train headway at k period, rate of f1. ukj is the number of passenger arrivals at the station ( th 1, f ≤ 1 j during the k period. η = 1 (5) f f Therefore, the optimization objective is to minimize 1, 1 < 1 ≤ 1.2 the passengers’ waiting time and departure times: 3. Income from ticket sales is set to be higher than op- K erating costs. P mk K J k=1 F αfp βfv α P P min = + = Ts (3) ukj k=1 j=1 (hmax+hmin)/2 f2 = G1 − G2L f2 ≥ 0 (6) K J K P P P m (mk ρkj ∆tk)∆tk 2 k k k=1 j=1 =1 + βη K J where f denotes the revenue of the urban rail com- P P u h h 2 kj( max + min) 2 L k=1 j=1 panies, is the total mileage of lines in operation, G1 is average ticket price and G2 is the average oper- α where is the weighting coefficient of operation ating cost per kilometer.
Load more

Recommended publications
  • Data Exploration in Evolutionary Reconstruction of PET Images Gray
    Data exploration in evolutionary reconstruction of PET images ANGOR UNIVERSITY Gray, Cameron; Al-Maliki, Shatha; Vidal, Franck Genetic Programming and Evolvable Machines DOI: 10.1007/s10710-018-9330-7 PRIFYSGOL BANGOR / B Published: 01/09/2018 Peer reviewed version Cyswllt i'r cyhoeddiad / Link to publication Dyfyniad o'r fersiwn a gyhoeddwyd / Citation for published version (APA): Gray, C., Al-Maliki, S., & Vidal, F. (2018). Data exploration in evolutionary reconstruction of PET images. Genetic Programming and Evolvable Machines, 19(3), 391-419. https://doi.org/10.1007/s10710-018-9330-7 Hawliau Cyffredinol / General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. 24. Sep. 2021 Genetic Programming and Evolvable Machines manuscript No. (will be inserted by the editor) Data exploration in evolutionary reconstruction of PET images Cameron C. Gray · Shatha F. Al-Maliki · Franck P. Vidal the date of receipt and acceptance should be inserted later Abstract This work is based on a cooperative co-evolution algorithm called `Fly Algorithm', which is an evolutionary algorithm (EA) where individuals are called ‘flies’.
    [Show full text]
  • Threshold Selection, Mitosis and Dual Mutation in Cooperative Co-Evolution
    Threshold selection, mitosis and dual mutation in cooperative co-evolution: application to medical 3d tomography Franck Vidal, Evelyne Lutton, Jean Louchet, Jean-Marie Rocchisani To cite this version: Franck Vidal, Evelyne Lutton, Jean Louchet, Jean-Marie Rocchisani. Threshold selection, mitosis and dual mutation in cooperative co-evolution: application to medical 3d tomography. PPSN 2010, 11th International Conference on Parallel Problem Solving From Nature., Sep 2010, Krakow, Poland. hal-00783841 HAL Id: hal-00783841 https://hal.inria.fr/hal-00783841 Submitted on 1 Feb 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Threshold selection, mitosis and dual mutation in cooperative co-evolution: application to medical 3D tomography Franck P. Vidal1, Evelyne Lutton2, Jean Louchet3, and Jean-Marie Rocchisani4 1 Department of Radiation Oncology, University of California, San Diego, CA. [email protected] 2 INRIA - Saclay-ˆIle-de-France, AVIZ team, Orsay, France. [email protected] 3 Artenia, Chˆatillon, France. [email protected] 4 Paris XIII University, UFR SMBH & Avicenne hospital, Bobigny, France. [email protected] Abstract. We present and analyse the behaviour of specialised opera- tors designed for cooperative coevolution strategy in the framework of 3D tomographic PET reconstruction.
    [Show full text]
  • Artificial Evolution Strategy for PET Reconstruction
    1 Artificial Evolution Strategy for PET Reconstruction Franck P. Vidal, Yoann L. Pavia, Jean-Marie Rocchisani, Jean Louchet, and Evelyne´ Lutton Abstract—This paper shows new resutls of our artificial PET evolution algorithm for positron emission tomography (PET) reconstruction. This imaging technique produces datasets cor- CT responding to the concentration of positron emitters within the PET CT PET/CT patient. Fully three-dimensional (3D) tomographic reconstruction requires high computing power and leads to many challenges. Our aim is to produce high quality datasets in a time that is clin- PET/CT ically acceptable. Our method is based on a co-evolution strategy called the “Fly algorithm”. Each fly represents a point in space PET CT PET/CT and mimics a positron emitter. Each fly position is progressively (a) PET-CT scan of a patient with lung (b) Schema of a PET acquisition optimised using evolutionary computing to closely match the data cancer and showing hilar lymph nodes. process. measured by the imaging system. The performance of each fly is assessed based on its positive or negative contribution to the Fig. 1. PET imaging. performance of the whole population. The final population of flies approximates the radioactivity concentration. This approach has shown promising results on numerical phantom models. The size of objects and their relative concentrations can be calculated in two-dimensional (2D) space. In 3D, complex shapes can be reconstructed. In this paper, we demonstrate the ability of the algorithm to fidely reconstruct more anatomically realistic volumes. Index Terms—Evolutionary computation, inverse problems, adaptive algorithm, Nuclear medicine, Positron emission tomog- Fig.
    [Show full text]
  • A New Improved Fruit Fly Optimization Algorithm IAFOA and Its Application
    Knowledge-Based Systems 144 (2018) 153–173 Contents lists available at ScienceDirect Knowle dge-Base d Systems journal homepage: www.elsevier.com/locate/knosys A new improved fruit fly optimization algorithm IAFOA and its application to solve engineering optimization problems ∗ Lei Wu a,b, Qi Liu a, Xue Tian c, Jixu Zhang d, Wensheng Xiao a, a College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao 266580, China b School of Civil and Environmental Engineering, Maritime Institute @NTU, Nanyang Technological University, Singapore 639798, Singapore c Marine Design & Research Institute of China, Shanghai 20 0 011, China d CNOOC Safety Technology Services Company Limited, Tianjin 300456, China a r t i c l e i n f o a b s t r a c t Article history: Nature-inspired algorithms are widely used in mathematical and engineering optimization. As one of the Received 14 July 2017 latest swarm intelligence-based methods, fruit fly optimization algorithm (FOA) was proposed inspired Revised 24 November 2017 by the foraging behavior of fruit fly. In order to overcome the shortcomings of original FOA, a new im- Accepted 27 December 2017 proved fruit fly optimization algorithm called IAFOA is presented in this paper. Compared with original Available online 27 December 2017 FOA, IAFOA includes four extra mechanisms: 1) adaptive selection mechanism for the search direction, 2) Keywords: adaptive adjustment mechanism for the iteration step value, 3) adaptive crossover and mutation mech- Fruit fly optimization algorithm anism, and 4) multi-sub-swarm mechanism. The adaptive selection mechanism for the search direction Optimal search direction allows the individuals to search for global optimum based on the experience of the previous iteration Iteration step value generations.
    [Show full text]
  • Applying Evolutionary Optimisation to Robot Obstacle Avoidance
    ISCIIA04, December 20-24, 2004, Haikou, China APPLYING EVOLUTIONARY OPTIMISATION TO ROBOT OBSTACLE AVOIDANCE Olivier Pauplin, Jean Louchet, Evelyne Lutton, Michel Parent INRIA, IMARA & COMPLEX Projects, BP 105, 78153 Le Chesnay Cedex France [email protected], [email protected], [email protected], [email protected] Abstract: This paper presents an artificial evolution- representation and evolving its components based method for stereo image analysis and its following principles inspired from Darwin's application to real-time obstacle detection and avoidance descriptions of biological evolution. for a mobile robot. It uses the Parisian approach, which consists here in splitting the representation of the robot's environment into a large number of simple primitives, 2 Evolutionary algorithms. the “flies”, which are evolved following a biologically Darwin’s theory assumes that a population of inspired scheme and give a fast, low-cost solution to the obstacle detection problem in mobile robotics. individuals, characterised by their genes, will evolve towards a better adaptation to its Keywords: evolutionary algorithm, stereovision, vision environment according to laws of natural systems for robotics, obstacle detection selection. Genes mutations may occur and 1 Introduction. maintain diversity in the population. Artificial Vision, an important element in the Population initialised at random design of autonomous robots, can be approached as the resolution of the reverse Evaluation problem of reconstructing a probable model of Offspring the scene from the images. Although probabilistic optimisation methods like Genetic Stop criterion Evolutionary Algorithms [1],[2],[3] are in operators is reached ? theory well adapted to the resolution of such no inverse problems, their use in real applications Parents Selection has been relatively neglected because of their yes reputation of low speed and complexity.
    [Show full text]
  • Fly4arts : Evolutionary Digital Art with the Fly Algorithm
    Fly4Arts : Evolutionary Digital Art with the Fly Algorithm Zainab Ali Abbood1 and Franck P. Vidal1 1School of Computer Science, Bangor University, UK, [email protected], [email protected] ABSTRACT. The aim of this study is to generate artistic images, such as digital mosaics, as an optimisation problem without the introduction of any a priori knowledge or constraint other than an input image. The usual practice to produce digital mosaic images heavily relies on Centroidal Voronoi diagrams. We demonstrate here that it can be modelled as an optimisation problem solved using a cooperative co-evolution strategy based on the Parisian evolution approach, the Fly algorithm. An individual is called a fly. Its aim of the algorithm is to optimise the position of infinitely small 3-D points (the flies). The Fly algorithm has been initially used in real-time stereo vision for robotics. It has also demonstrated promising results in image reconstruction for tomography. In this new application, a much more complex representation has been study. A fly i s a t ile. I t h as i ts o wn p osition, s ize, c olour, a nd r otation a ngle. O ur m ethod t akes a dvantage o f graphics processing unit (GPU) to generate the images using the modern OpenGL Shading Language (GLSL) and Open Computing Language (OpenCL) to compute the difference between the input image and simulated image. Different types of tiles are implemented, some with transparency, to generate different visual effects, such as digital mosaic and spray paint. An online study with 41 participants has been conducted to compare some of our results with those generated using an open-source software for image manipulation.
    [Show full text]
  • A Novel Hybrid Firefly Algorithm for Global Optimization
    RESEARCH ARTICLE A Novel Hybrid Firefly Algorithm for Global Optimization Lina Zhang1, Liqiang Liu1*, Xin-She Yang2, Yuntao Dai3 1 College of Automation, Harbin Engineering University, Harbin, China, 2 School of Science and Technology, Middlesex University, London, United Kingdom, 3 College of Science, Harbin Engineering University, Harbin, China * [email protected] a11111 Abstract Global optimization is challenging to solve due to its nonlinearity and multimodality. Tradi- tional algorithms such as the gradient-based methods often struggle to deal with such prob- lems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential OPEN ACCESS evolution (DE). FA and DE are executed in parallel to promote information sharing among Citation: Zhang L, Liu L, Yang X-S, Dai Y (2016) A Novel Hybrid Firefly Algorithm for Global the population and thus enhance searching efficiency. In order to evaluate the performance Optimization. PLoS ONE 11(9): e0163230. and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are doi:10.1371/journal.pone.0163230 employed and these functions fall into two groups: unimodal and multimodal. The experi- Editor: Wen-Bo Du, Beihang University, CHINA mental results show better performance of the proposed algorithm compared to the original Received: June 22, 2016 version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimiza- tion (PSO) in the sense of avoiding local minima and increasing the convergence rate.
    [Show full text]
  • Improvement of Traveling Salesman Problem Solution Using Hybrid Algorithm Based on Best-Worst Ant System and Particle Swarm Optimization
    applied sciences Article Improvement of Traveling Salesman Problem Solution Using Hybrid Algorithm Based on Best-Worst Ant System and Particle Swarm Optimization Muhammad Salman Qamar 1,2, Shanshan Tu 3,* , Farman Ali 1 , Ammar Armghan 4, Muhammad Fahad Munir 2, Fayadh Alenezi 4 , Fazal Muhammad 5 , Asar Ali 1 and Norah Alnaim 6 1 Department of Electrical Engineering, Qurtuba University of Science and IT, Dera Ismail Khan 29050, Pakistan; [email protected] (M.S.Q.); [email protected] (F.A.); [email protected] (A.A.) 2 Department of Electrical Engineering, International Islamic University, Islamabad 44000, Pakistan; [email protected] 3 Engineering Research Center of Intelligent Perception and Autonomous Control, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China 4 Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia; [email protected] (A.A.); [email protected] (F.A.) 5 Department of Electrical Engineering, University of Engineering Technology, Mardan 23200, Pakistan; [email protected] 6 Department of Computer Science, College of Sciences and Humanities in Jubail, Imam Abdulrahman bin Faisal University, Dammam 31441, Saudi Arabia; [email protected] * Correspondence: [email protected] Citation: Qamar, M.S.; Ali, F.; Abstract: This work presents a novel Best-Worst Ant System (BWAS) based algorithm to settle Armghan, A.; Munir, M.F.; Alenezi, F.; the Traveling Salesman Problem (TSP). The researchers has been involved in ordinary Ant Colony Muhammad, F.; Ali, A.; Alnaim, N. Optimization (ACO) technique for TSP due to its versatile and easily adaptable nature.
    [Show full text]
  • Journal of Science the Use of Artificial Neural Networks Optimized with Fire Fly Algorithm in Cancer Diagnosis
    Research Article GU J Sci 32(3): 823-831 (2019) DOI: 10.35378/gujs.471859 Gazi University Journal of Science http://dergipark.gov.tr/gujs The Use of Artificial Neural Networks Optimized with Fire Fly Algorithm in Cancer Diagnosis Mehmet BILEN1,* , Ali Hakan ISIK2 , Tuncay YIGIT3 1Mehmet Akif Ersoy University, Cavdir Vocational High School, 15000 Burdur, Turkey 2Mehmet Akif Ersoy University, Faculty of Engineering and Architecture, 15000, Burdur, Turkey 3Süleyman Demirel University, Faculty of Engineering, 32000, Isparta, Turkey Highlights • This paper focuses on classification process for central nervous system tumor. • A hybrid approach is proposed for classification Microarray Data in the study. • A highly precise and more efficient classification accuracy were obtained. Article Info Abstract Today, the amount of biological data types obtained are increasing every day. Among these data Received: 17/10/2018 types are micro arrays that play an important role in cancer diagnosis. The data analysis that are Accepted: 05/06/2019 carried out through traditional approaches have proven unsuccessful in delivering efficient results on data types where data complexity is high and where sampling is low. For this reason, using a hybrid algorithm by merging the effective features of two distinct algorithms will yield Keywords effective results. In this study, a classification process was performed firstly by dimension Cancer Diagnosis reduction on micro array data that were obtained from the tissues from patients with a tumor in Firefly Algorithm their central nervous system and then by using an artificial neural network algorithm that was Hybrid Algorithms optimized through Fire Fly Algorithm (FF), a hybrid approach. The data obtained were Artificial Neural compared to K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Artificial Networks Neural Networks (ANN) classification algorithms, which are frequently used in the literature.
    [Show full text]
  • A New Fruit Fly Optimization Algorithm Enhanced Support Vector Machine for Diagnosis of Breast Cancer Based on High-Level Featur
    Huang et al. BMC Bioinformatics 2019, 20(Suppl 8):290 https://doi.org/10.1186/s12859-019-2771-z RESEARCH Open Access A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features Hui Huang1,Xi’an Feng1, Suying Zhou2, Jionghui Jiang3, Huiling Chen4*, Yuping Li5 and Chengye Li5* From International Conference on Data Science, Medicine and Bioinformatics Wenzhou, China. 22- 24 June 2018 Abstract Background: It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. In this study, an enhanced machine learning framework is established to diagnose the breast cancer. The core of this framework is to adopt fruit fly optimization algorithm (FOA) enhanced by Levy flight (LF) strategy (LFOA) to optimize two key parameters of support vector machine (SVM) and build LFOA-based SVM (LFOA-SVM) for diagnosing the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time. Results: In order to verify the effectiveness of the proposed method, 10-fold cross-validation method is used to make comparison among the proposed method, FOA-SVM (model based on original FOA), PSO-SVM (model based on original particle swarm optimization), GA-SVM (model based on genetic algorithm), random forest, back propagation neural network and SVM. The main novelty of LFOA-SVM lies in the combination of FOA with LF strategy that enhances the quality for FOA, thus improving the convergence rate of the FOA optimization process as well as the probability of escaping from local optimal solution.
    [Show full text]
  • Journal of Science a New Meta Heuristic Dragonfly Optimizaion Algorithm for Optimal Reactive Power Dispatch Problem
    Research Article GU J Sci 31(4): 1107-1121 (2018) Gazi University Journal of Science http://dergipark.gov.tr/gujs A New Meta Heuristic Dragonfly Optimizaion Algorithm for Optimal Reactive Power Dispatch Problem Anbarasan PALAPPAN1,*, Jayabarathi THANGAVELU1 1School of Electrical Engineering VIT University, Vellore, India Article Info Abstract This research accesses a novel approach of utilising an advanced Meta–heuristic Optimization Received: 03/07/2017 technique with a single objective to pledge with optimal reactive power dispatch problem in Accepted: 23/12/2017 electrical power system network. The prime focus of reactive power dispatch is to curtail the total active power loss in transmission lines. In this detailed study, the dragonfly algorithm was realized on standard IEEE-14 bus and 30 bus systems. The outcome of dragonfly algorithm Keywords lucidly indicate the capablity of increasing the antecedent random population size for a liable Optimal reactive power global optimization problem, focalized close to the global optimum and contributing precise dispatch outcome results related to another popular algorithm. Dragonfly algorithm Real power loss minimization and Swarm intelligence 1. INTRODUCTION Generally the electrical power is generated, transmitted, distributed and utilized in day-to-day activities in bulk amount in power system network. Transferring the electrical power from generation end to utilizing end is a great challenge for power system operators in routine task due to load variations. The load variations are arising in power system network due to weather conditions, social activities and industrial need- based. Therefore, the power system structure is complicated in nature. In such a complicated network of large scale power system network, during the past few decacdes, the role of a system operator has been of offering considerable challenges.
    [Show full text]
  • Improved Fruit Fly Algorithm on Structural Optimization
    Li and Han Brain Inf. (2020) 7:1 https://doi.org/10.1186/s40708-020-0102-9 Brain Informatics RESEARCH Open Access Improved fruit fy algorithm on structural optimization Yancang Li1* and Muxuan Han2 Abstract To improve the efciency of the structural optimization design in truss calculation, an improved fruit fy optimiza- tion algorithm was proposed for truss structure optimization. The fruit fy optimization algorithm was a novel swarm intelligence algorithm. In the standard fruit fy optimization algorithm, it is difcult to solve the high-dimensional nonlinear optimization problem and easy to fall into the local optimum. To overcome the shortcomings of the basic fruit fy optimization algorithm, the immune algorithm self–non-self antigen recognition mechanism and the immune system learn–memory–forgetting knowledge processing mechanism were employed. The improved algorithm was introduced to the structural optimization. Optimization results and comparison with other algorithms show that the stability of improved fruit fy optimization algorithm is apparently improved and the efciency is obviously remark- able. This study provides a more efective solution to structural optimization problems. Keywords: Truss structure optimization, Fruit fy algorithm, Improvement, Immune response 1 Introduction an improved software package for stamping die struc- With the rapid development of computer technology, the ture that can greatly reduce the weight. Ide [3] designed efciency of structural optimization is greatly improved, the lightweight structure with the structural optimiza- and structural designers can have more time and energy tion method, and successfully realized the lightweight to consider how to get better structural design scheme. gear box design by using the design method of reduc- In 1974, Schimit and Farshi proposed to combine fnite ing contact constraint stress.
    [Show full text]