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Parameter estimation in mathematical models using the real coded genetic algorithms

EXPERT SYSTEMS WITH APPLICATIONS 2009年第2期36卷 3342-3345页

作者： Tutkun, Nedim Univ Zonguldak Dept Elect & Elect Engn TR-67100 Zonguldak Turkey

In this study, parameter estimation in mathematical models using the real coded genetic algorithms (RCGA) approach is presented. Although the RCGA is similar with the binary coded genetic algorithms (BCGA) in terms of genetic process, it has few advantages such as high precision, non-existence of Hamming's cliff etc., over the BCGA. In this approach, creating initial population and selection procedure are almost the same with the BCGA, but crossover and mutation operations. The proposed approach is implemented on the second order ordinary differential equations modeling the enzyme effusion problem and it is compared with previous approaches. The results indicate that the proposed approach produced better estimated results with respect to previous findings. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： real coded genetic algorithms Binary coded genetic algorithms Nonlinear curve fitting Parameter estimation Ordinary differential equations Dynamic system identification

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Globally Multimodal Function Optimization by real-coded genetic algorithms using Traps

Globally Multimodal Function Optimization by Real-coded Gene...

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2010 IEEE World Congress on Computational Intelligence

作者： Karatsu, Naoya Nagata, Yuichi Ono, Isao Kobayashi, Shigenobu Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku Yokohama Kanagawa 2268503 Japan

ISBN: (纸本)9781424481262

real-coded genetic algorithms (RCGAs) have been extensively studied for last two decades because RCGAs have advantages over conventional continuous function optimization methods when multimodal functions are optimized. Innately Split Model (ISM) is one of promising approaches to enhance RCGAs where a set of population groups are evolved in parallel and groups are re-initialized if two groups searches a similar region (it is called redundant searches). In this paper, we propose a new strategy for the re-initialization of groups to improve the performance of ISM. In our method, redundant searches are detected by using the information of the search histories of the groups. This information is called traps and is stored as a set of hyper-ellipsoids representing the distributions of the previous groups. We demonstrate that the proposed method is robust and superior to the original ISM.

关键词： ISM RCGA codes conventional continuous function optimization function approximation genetic algorithms hyper-ellipsoids represention innately split model multimodal functions real coded genetic algorithms redundancy redundant searches traps

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real/binary-like coded versus binary coded genetic algorithms to automatically generate fuzzy knowledge bases: a comparative study

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2004年第4期17卷 313-325页

作者： Achiche, S Baron, L Balazinski, M Ecole Polytech Dept Mech Engn Montreal PQ H3C 3A7 Canada

Nowadays fuzzy logic is increasingly used in decision-aided systems since it offers several advantages over other traditional decision-making techniques. The fuzzy decision support systems can easily deal with incomplete and/or imprecise knowledge applied to either linear or nonlinear problems. This paper presents the implementation of a combination of a real/Binary-Like coded genetic Algorithm (RBLGA) and a Binary coded genetic Algorithm (BGA) to automatically generate Fuzzy Knowledge Bases (FKB) from a set of numerical data. Both algorithms allow one to fulfill a contradictory paradigm in terms of FKB precision and simplicity (high precision generally translates into a higher level of complexity) considering a randomly generated population of potential FKBs. The RBLGA is divided into two principal coding methods: (1) a real coded genetic algorithm that maps the fuzzy sets repartition and number (which drives the number of fuzzy rules) into a set of real numbers and (2) a binary like coded genetic algorithm that deals with the fuzzy rule base relationships (a set of integers). The BGA deals with the entire FKB using a single bit string, which is called a genotype. The RBLGA uses three reproduction mechanisms, a BLX-alpha, a simple crossover and a fuzzy set reducer. while the BGA uses a simple crossover, a fuzzy set displacement mechanism and a rule reducer. Both GAs are tested on theoretical surfaces, a comparison study of the performances is discussed, along with the influences of some evolution criteria. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： artificial intelligence fuzzy logic systems fuzzy knowledge bases automatic learning real coded genetic algorithms binary coded genetic algorithms

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real-coded genetic algorithm with uniform random local search

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APPLIED MATHEMATICS AND COMPUTATION 2014年 228卷 589-597页

作者： Sawyerr, B. A. Adewumi, A. O. Ali, M. M. Univ Lagos Dept Comp Sci Lagos Nigeria Univ Kwazulu Natal Sch Math Stat & Comp Sci Durban Kwazulu Natal South Africa Univ Witwatersrand Fac Sci Sch Computat & Appl Math Johannesburg South Africa Univ Witwatersrand Fac Engn & Built Environm TCSE Johannesburg South Africa

genetic algorithms are efficient global optimizers, but they are weak in performing fine-grained local searches. In this paper, the local search capability of genetic algorithm is improved by hybridizing real coded genetic algorithm with 'uniform random' local search to form a hybrid real coded genetic algorithm termed 'RCGAu'. The incorporated local technique is applied to all newly created offspring so that each offspring solution is given the opportunity to effectively search its local neighborhood for the best local optimum. Numerical experiments show that the performance of RCGA is remarkably improved by the uniform random local search technique. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Global optimization real coded genetic algorithms Uniform random local search

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A modified real coded genetic algorithm for constrained optimization

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APPLIED MATHEMATICS AND COMPUTATION 2014年 235卷 292-317页

作者： Thakur, Manoj Meghwani, Suraj S. Jalota, Hemant Indian Inst Technol Mandi Sch Basic Sci Mandi 175001 India

The performance of a genetic algorithm (GA) largely depends upon crossover and mutation operators. Deep and Thakur (2007) [14,15] proposed a real coded genetic algorithm (RCGA) incorporating Laplace crossover (LX) and power mutation (PM) operator and shown that the resulting GA (named LX-PM) outperforms many existing RCGAs on a large set of scalable test problems of varying difficulty level. In this paper, LX-PM is modified by improving the LX operator. The modified LX operator, named as bounded exponential crossover (BEX) operator, always creates offspring within the variable bounds. A new RCGA (named BEX-PM) incorporating BEX and PM operator is proposed. The performance of the modified GA is tested against the original algorithm LX-PM and three other popular constrained optimization algorithms (HX-NUM, HX-MPTM and SBX-POL) over a test suite containing twenty five constrained optimization problems collected from global optimization literature. The performance of all RCGAs and quality of solution obtained is compared on the basis of standard criteria used in GA literature. The comparative study shows that BEX-PM performs significantly better than the original algorithm LX-PM and outperforms all RCGAs considered in this study. (C) 2014 Elsevier Inc. All rights reserved.

关键词： real coded genetic algorithms Laplace crossover Power mutation Constrained optimization

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Installation of Smog-Free Towers Using Novel real coded genetic Algorithm

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IEEE ACCESS 2024年 12卷 15056-15074页

作者： Kumar, Yogesh Deep, Kusum Nagar, Atulya K. Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India Liverpool Hope Univ Sch Math Comp Sci & Engn Liverpool L16 9JD England

The circle packing problem involves finding the best way to place non-overlapping circles within a given space, while the smog-free tower installation problem aims to minimize the exposure of residents to secondhand smog by identifying the optimal tower locations. This study proposes a real coded genetic Algorithm (RCGA) that uses real-valued representations of circle positions to solve the smog-free tower installation problem. A new crossover operator is introduced, combining the information from two parent solutions to generate two new offspring solutions. The operator uses a random crossover point and two scaling factors to control the amount of information exchanged. The performance of the operator is evaluated on CEC-2017 benchmark problem set and compared to other commonly used operators, with results indicating that it produces high-quality solutions and outperforms other operators in terms of solution quality and convergence speed. This research contributes to developing effective optimization algorithms that can have important implications for improving public health and reducing the negative effects of secondhand smog.

关键词： Burr distribution crossover operators real coded genetic algorithms smog free tower

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Optimization of parameters of micro-plasma transferred arc additive manufacturing process using real coded genetic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第3-4期106卷 1239-1252页

作者： Nikam, Sagar H. Jain, Neelesh Kumar Sawant, Mayur S. Ulster Univ Sch Comp Engn & Intelligent Syst Derry Londonderry BT48 7JL North Ireland Indian Inst Technol Indore Discipline Mech Engn Simrol 453552 MP India

Micro-plasma transferred arc additive manufacturing (mu-PTAAM) process developed at IIT Indore has proven be an energy and material efficient additive manufacturing process for various meso-scale ALM applications of high melting point metallic materials. This paper reports on optimization of three most important parameters (i.e. micro-plasma power, worktable travel rate and wire feed rate) of mu-PTAAM process by real coded genetic algorithms so as to minimize the aspect ratio (i.e. ratio of deposition width to deposition height) with an overall objective to increase productivity of this process. Objective function for aspect ratio was formulated using generic theoretical thermal developed in terms of mu-PTAAM process parameters and properties of the substrate and deposition material and models developed using regression analysis and artificial neural networks (ANN). It gave optimized values of micro-plasma power as 370, 355 and 360 W, respectively, by the thermal model, regression model and ANN model, and that of travel speed of worktable and wire feed rate as 100 mm/min and as 1700 mm/min by all three models. The optimized results were validated experimentally by depositing 0.3-mm diameter wire of P20 on 5-mm-thick substrate of the same material. The optimized values of the aspect ratio using objective function based generic thermal model, regression model and ANN model are 1.15, 1.31 and 1.36, respectively, with corresponding experimental values being 1.48, 1.5 and 1.48, respectively. Use of optimum process parameters resulted in very good quality and accuracy of the deposition which has excellent bonding with the substrate material and no internal defects.

关键词： Additive manufacturing Micro-plasma transferred arc Optimization Thermal model Regression ANN real coded genetic algorithms

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A real coded genetic algorithm for solving integer and mixed integer optimization problems

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APPLIED MATHEMATICS AND COMPUTATION 2009年第2期212卷 505-518页

作者： Deep, Kusum Singh, Krishna Pratap Kansal, L. Mohan, C. Indian Inst Technol Dept Math Roorkee 247667 Uttarakhand India Indian Inst Technol Dept Water Resources Dev & Management Roorkee 247667 Uttarakhand India Ambala Coll Engn & Appl Res Ambala Haryana India

In this paper, a real coded genetic algorithm named MI-LXPM is proposed for solving integer and mixed integer constrained optimization problems. The proposed algorithm is a suitably modified and extended version of the real coded genetic algorithm, LXPM, of Deep and Thakur [K. Deep, M. Thakur, A new crossover operator for real coded genetic algorithms, Applied Mathematics and Computation 188 (2007) 895-912;K. Deep, M. Thakur, A new mutation operator for real coded genetic algorithms, Applied Mathematics and Computation 193 (2007) 211-230]. The algorithm incorporates a special truncation procedure to handle integer restrictions on decision variables along with a parameter free penalty approach for handling constraints. Performance of the algorithm is tested on a set of twenty test problems selected from different sources in literature, and compared with the performance of an earlier application of genetic algorithm and also with random search based algorithm, RST2ANU, incorporating annealing concept. The proposed MI-LXPM outperforms both the algorithms in most of the cases which are considered. (C) 2009 Elsevier Inc. All rights reserved.

关键词： real coded genetic algorithms Random search based techniques Constrained optimization Integer and mixed integer optimization problems

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Structural inverse analysis by hybrid simplex artificial bee colony algorithms

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COMPUTERS & STRUCTURES 2009年第13-14期87卷 861-870页

作者： Kang, Fei Li, Junjie Xu, Qing Dalian Univ Technol Sch Civil & Hydraul Engn Dalian 116024 Peoples R China

A hybrid simplex artificial bee colony algorithm (HSABCA) which combines Nelder-Mead simplex method with artificial bee colony algorithm (ABCA) is proposed for inverse analysis problems. The proposed algorithm is applied to parameter identification of concrete dam-foundation systems. To verify the performance of HSABCA, it is compared with the basic ABCA and a real coded genetic algorithm (RCGA) on two examples: a gravity dam and an arc dam. Results show that the proposed algorithm is an efficient tool for inverse analysis and it performs much better than ABCA and RCGA on such problems. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Inverse/back analysis Artificial bee colony algorithms Nelder-Mead simplex search method real coded genetic algorithms Parameter identification Concrete dams

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Global optimum spacecraft orbit control subject to bounded thrust in presence of nonlinear and random disturbances in a low earth orbit

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EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES 2012年第1期15卷 1-8页

作者： Habib, Tamer Mekky Ahmed Egyptian Natl Author Remote Sensing & Space Sci Cairo Egypt

The primary objective of this work is to develop an effective spacecraft orbit control algorithm suitable for spacecraft orbital maneuver and/or rendezvous. The actual governing equation of a spacecraft orbiting the earth is merely nonlinear. Disturbance forces resulting from aerodynamic drag, oblateness of the earth till the fourth order (i.e. J4), and random disturbances are modeled for the initial and target orbits. These disturbances increase the complexity of nonlinear governing equations. Global optimum solutions of the control algorithm parameters are determined throughout real coded genetic algorithms such that the steady state difference between the actual and desired trajectories is minimized. The resulting solutions are constrained to avoid spacecraft collision with the surface of the earth taking into account limited thrust budget. (C) 2011 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Orbit control Earth oblateness Drag Random disturbance real coded genetic algorithms

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