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A MODIFIED real-coded genetic algorithm CONSIDERING WITH FITNESS-BASED VARIABILITY

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2014年第4期10卷 1509-1518页

作者： Ueda, Yoshiaki Horio, Keiichi Kubota, Ryosuke Ube Natl Coll Technol Adv Course Prod Syst 2-14-1 Tokiwadai Ube Yamaguchi 7558555 Japan Ube Natl Coll Technol Dept Intelligent Syst Engn Ube Yamaguchi 7558555 Japan Kyushu Inst Technol Grad Sch Life Sci & Syst Engn Wakamatsu Ku Kitakyushu Fukuoka 8080196 Japan

A genetic algorithm (GA) is a search algorithm based on the mechanism of natural genetics. In various GAs, a real-coded GA (RCGA) employing individuals represented by real valued-genes has been proposed to solve the optimization problem in the continuous searching space. However, the conventional RCGA yields ineffective searches due to insufficient genetic diversity in the selection process. In this paper, we propose a modified RCGA with variability operator maintaining the genetic diversity of the population. In the proposed method, a variability term is newly added to the individuals selected by the ordinary selection. The degree of the variability is decided considering the fitness value of the individual. The searching performance of the proposed method is better than the conventional methods. The effectiveness and the validity of the proposed method are verified by applying it to optimization problems of continuous benchmark functions and signal sources localization.

关键词： genetic algorithm real-coded genetic algorithm genetic diversity Mutation Variability operator

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real coded genetic algorithm FOR STOCHASTIC HYDROTHERMAL GENERATION SCHEDULING

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Journal of Systems Science and Systems Engineering 2011年第1期20卷 87-109页

作者： Jarnail S.DHILLON J.S.DHILLON D.P.KOTHARI G.Z.S.College of Engineering and Technology Sant Longowal Institute of Engineering and Technology Vindhya Institute of Technology and Science

The intent of this paper is to schedule short-term hydrothermal system probabilistically considering stochastic operating cost curves for thermal power generation units and uncertainties in load demand and reservoir water inflows. Therefore, the stochastic multi-objective hydrothermal generation scheduling problem is formulated with explicit recognition of uncertainties in the system production cost coefficients and system load, which are treated as random variable. Fuzzy methodology has been exploited for solving a decision making problem involving multiplicity of objectives and selection criterion for best compromised solution. A real-coded genetic algorithm with arithmetic-average-bound-blend crossover and wavelet mutation operator is applied to solve short-term variable-head hydrothermal scheduling problem. Initial feasible solution has been obtained by implementing the random heuristic search. The search is performed within the operating generation limits. Equality constraints that satisfy the demand during each time interval are considered by introducing a slack thermal generating unit for each time interval. Whereas the equality constraint which satisfies the consumption of available water to its full extent for the whole scheduling period is considered by introducing slack hydro generating unit for a particular time interval. Operating limit violation by slack hydro and slack thermal generating unit is taken care using exterior penalty method. The effectiveness of the proposed method is demonstrated on two sample systems.

关键词： Stochastic multi-objective optimization real-coded genetic algorithm fuzzy set economicload dispatch

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Path planning of a Mobile Robot Using real-coded genetic algorithm Based Simultaneous Exploration

Path planning of a Mobile Robot Using Real-coded Genetic Alg...

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2013 2nd International Conference on Advances in Computer Science and Engineering(CSE 2013)

作者： Chih-Jer Lin Yen-Lin Chen Cheng-Hsin Liu Shen Kai Yu National Taipei University of Technology

In mobile robot researches, path planning and obstacle avoidance plays a very important role and has been a very challenging research topic. For path planning, it should produce continuous path from the starting point to the destination without colliding obstacles. Therefore, we proposes a genetic algorithm to search the path with the shortest path in Labview environment. The difficulty of the genetic algorithms applied to the mobile robot is how to reduce the complexity of the genetic operations, and how to avoid the region optimal solution and adaptation of environmental change. Many researchers studied genetic algorithms to determine the optimal solution such as path planning, but the past literatures mostly used binary coding for the gene encoding. If the path is longer or the number of obstacles is larger, the binary coding will be a lengthy string of series. This will result in the longer evolution of computing time. As a result, we propose a novel method which is a serial number encoded as a gene encoding to effectively reduce the evolution of computing time for the path planning applications.

关键词： Labview real-coded genetic algorithm path planning mobile robot

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Integrated real-coded genetic algorithm and Particle Swarm Optimization for Solving Constrained Global Optimization Problems

Integrated Real-Coded Genetic Algorithm and Particle Swarm O...

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Advances in Information Technology and Education

作者： Jui-Yu Wu Department of Business Administration Lunghwa University of Science and Technology

This work presents an integrated real-coded genetic algorithm and particle swarm optimization with a penalty function method(RGA-PSO) to solve constrained global optimization(CGO) problems. An outer RGA is used to optimize the parameter settings of an inner PSO algorithm, which includes the constriction coefficient, cognitive parameter, social parameter, penalty parameter and mutation probability. To increase the diversity of particles, the inner PSO method uses a multi-non-uniform operator. Performance of the proposed RGA-PSO algorithm is evaluated using a set of benchmark CGO problems. Additionally, this work compares the numerical results obtained using the proposed RGA-PSO algorithm with those of GAs and artificial immune algorithms(AIAs). Experimental results indicate that the proposed RGA-PSO algorithm converges to a global optimum solution to a CGO problem, and that the performance of the proposed RGA-PSO algorithm is competitive with those of GAs and AIAs. Although a hybrid method increases the complexity of an algorithm, the proposed RGA-PSO algorithm can obtain the optimum parameter settings for the inner PSO algorithm by using the outer RGA and can find a global optimum solution to a CGO problem by using the inner PSO algorithm, simultaneously. Hence, the proposed RGA-PSO algorithm can be considered as an alternative stochastic global optimization method for solving CGO problems.

关键词： real-coded genetic algorithm particle swarm optimization nonlinear programming constrained global optimization

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Optimal design of FIR high pass filter based on L-1 error approximation using real coded genetic algorithm

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2015年第4期18卷 594-602页

作者： Aggarwal, Apoorva Rawat, Tarun Kumar Kumar, Manjeet Upadhyay, D. K. Netaji Subhas Inst Technol Dept Elect & Commun Delhi India

In this paper, an optimal design of linear phase digital finite impulse response (FIR) highpass (HP) filter using the L-1-norm based real-coded genetic algorithm (RCGA) is investigated. A novel fitness function based on L-1 norm is adopted to enhance the design accuracy. Optimized filter coefficients are obtained by defining the filter objective function in L-1 sense using RCGA. Simulation analysis unveils that the performance of the RCGA adopting this fitness function is better in terms of signal attenuation ability of the filter, flatter passband and the convergence rate. Observations are made on the percentage improvement of this algorithm over the gradient-based L-1 optimization approach on various factors by a large amount. It is concluded that RCGA leads to the best solution under specified parameters for the FIR filter design on account of slight unnoticeable higher transition width. (C) 2015 Karabuk University. Production and hosting by Elsevier B.V.

关键词： Finite impulse response L-1 norm Digital filter Bio-inspired algorithm real-coded genetic algorithm

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Extraction of the liver tumor in CT images by real coded genetic algorithm (RGA)

Extraction of the liver tumor in CT images by real coded gen...

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2nd IASTED International Conference on Computational Intelligence

作者： Nakamichi, Koh Karungaru, Stephen Fukumi, Minoru Akashi, Takuya Mitsukura, Yasue Yasutomo, Motokatsu Univ Tokushima 2-1 Minami Josanjima Tokushima 7708506 Japan Yamaguchi Univ Ube Yamaguchi 7558611 Japan Tokyo Univ Agr & Technol Koganei Tokyo 1848588 Japan Higashi Tokushima Natl Hosp Tokushima 7790193 Japan

ISBN: (纸本)9780889866027

in Japan, diet-linked diseases are increasing owing to the reasons of diversified and meat-oriented diet. Disease of an internal organ and brain disease are those instances. Furthermore regardless of race, increase in fatalities by cancer, cardiac disease and cerebropathy is a social issue. Medical equipments as the CT, MRI and ultrasonography (US) is used for these sick discoveries. The demand for Medical Equipments has increased. The doctor's load is increasing as it increases. The purpose of this work is the construction of an automatic diagnosis support system for CT images in order to reduce the doctor's load. Toward this end, in this paper, a method to extract liver tumors in CT images using a real-coded genetic algorithm is proposed. Conventionally, a threshold is necessary to extract an object from an image. However, such a method is not effective for CT images because gray scale values are different in each image. Therefore, in this paper, we propose the method for extracting the tumor in the liver from the CT image without the need of a threshold. In this method, a polygon enclosure of the liver tumor is extracted using a GA.

关键词： real-coded genetic algorithm medical Imaging CT images liver tumor extraction segmentation cancer

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real coded genetic algorithm in operational optimization of a district cooling system: An inceptive applicability assessment and power saving evaluation

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BUILDING SERVICES ENGINEERING RESEARCH & TECHNOLOGY 2024年第6期45卷 755-773页

作者： Reshi, Mubashir A. Mursaleen, M. Natl Inst Technol Dept Mech Engn Srinagar 190006 India

District Cooling Systems are progressively becoming a standard feature of smart cities. This is attributed to their inherent feature of low operating cost and high energy efficiency. Given the constantly increasing energy prices worldwide and the target of the Conference of the Parties-28th Session for reducing emissions, the District Cooling System technology is quite promising in this direction. Various studies are available that have particularly focused on the design phase optimization of the systems, while in-process operational optimization is still in its miniature phase. This paper presents a model-based metaheuristic optimization approach to cooling water system towards an inceptive control strategy to explore and exploit the energy-saving potential using a real coded genetic algorithm. The algorithm is implemented in MATLAB to search for high-performance settings in real-time scenarios. The results showed that an energy saving from 9.66% to 26.54% can be obtained across 6 cases in the study, compared to the supervisory *** application District cooling technology is expected to gain more credibility as the most sustainable alternative to air conditioning in the upcoming decades due to the world's rapidly expanding need for cooling combined with the need to reduce carbon dioxide emissions. The current research and development efforts are yielding promising results for the fifth generation of this technology. Meanwhile, the study validates the enormous potential of operational optimization with contemporary artificial intelligence tools. This paper paves the way for future research by showing how the operation of a large-scale district cooling plant can be solved for energy saving.

关键词： District cooling system energy modelling real-coded genetic algorithm optimization, energy saving

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Adaptive directed mutation for real-coded genetic algorithms

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APPLIED SOFT COMPUTING 2013年第1期13卷 600-614页

作者： Tang, Ping-Hung Tseng, Ming-Hseng Chung Shan Med Univ Sch Med Informat Taichung Taiwan Chung Shan Med Univ Hosp Informat Technol Off Taichung Taiwan

Adaptive directed mutation (ADM) operator, a novel, simple, and efficient real-coded genetic algorithm (RCGA) is proposed and then employed to solve complex function optimization problems. The suggested ADM operator enhances the abilities of GAs in searching global optima as well as in speeding convergence by integrating the local directional search strategy and the adaptive random search strategies. Using 41 benchmark global optimization test functions, the performance of the new algorithm is compared with five conventional mutation operators and then with six genetic algorithms (GAs) reported in literature. Results indicate that the proposed ADM-RCGA is fast, accurate, and reliable, and outperforms all the other GAs considered in the present study. (C) 2012 Elsevier B. V. All rights reserved.

关键词： real-coded genetic algorithm Function optimization Adaptive directed mutation

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Optimally tuned local and wide area power system stabilizers for damping inter-area oscillations in power system with solar PV

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2025年

作者： Nivetha, T. Roselyn, J. Preetha SRM Inst Sci & Technol Dept EEE Kattankulathur 603203 India

This paper is focused on the development of optimally tuned power system stabilizers for enhancing the rotor angle stability in low frequency oscillation modes in renewables integrated power network. This work concentrates in coordination of local PSS and wide area PSS in damping local and inter-area oscillations in power system using optimal tuning of power system stabilizer by employing real-coded genetic algorithm. The input signal to local PSS and wide area PSS are the rotor speed deviation and tie line power which is obtained from the Phasor Measurement Unit in WAMS. The optimal parameters are obtained by the formulation of objective function which minimizes the rotor speed deviation by concentrating the eigen values and damping ratio of the power system. The proposed algorithm is developed and implemented in MATLAB/SIMULINK 2022b-version and Digsilent Power factory-2023. In addition, the impact of PV penetration on the small signal stability of the power system with the design of controllers is also investigated, and the allowable PV penetration during system disturbances is estimated in two area power system. The proposed algorithm for small signal stability enhancement in the test system is deployed in OPALRT processor, OP4510 board for validation of simulated controllers in HIL-simulation platform.

关键词： Phasor measurement unit power system stabilizer small signal stability enhancement multi machine power systems real-coded genetic algorithm

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A Novel Crossover Operator Designed to Exploit Synergies of Two Crossover Operators for real-coded genetic algorithms 5th

A Novel Crossover Operator Designed to Exploit Synergies of ...

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5th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Shashi Deep, Kusum Indian Inst Sci Dept Aerosp Engn Control & Decis Syst Lab Bangalore Karnataka India Indian Inst Technol Roorkee Dept Math Roorkee Uttar Pradesh India

ISBN: (纸本)9789811004513;9789811004506

In this paper a new crossover operator called the double distribution crossover (DDX) is proposed. The performance of DDX is compared with existing real-coded crossover operator namely Laplace crossover (LX). DDX is used in conjunction with a well-known mutation operator;Power mutation (PM) to obtain a new generational real-coded genetic algorithm called DDX-PM. DDX-PM is compared with the existing LX-PM. The performance of both the genetic algorithms is compared on the basis of success rate, average function evaluation, average error and computational time, and the preeminence of the proposed crossover operator is established.

关键词： real-coded genetic algorithm Mutation operator Crossover operator

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