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Self-calibration of vision parameters via genetic algorithms with simulated binary crossover and laser line projection

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OPTICAL ENGINEERING 2015年第5期54卷 053115-1-053115-9页

作者： Mejia Alanis, Francisco Carlos Apolinar Munoz Rodriguez, J. Ctr Invest Opt AC Leon 37150 GTO Mexico

A self-calibration technique based on genetic algorithms (GAs) with simulated binary crossover (SBX) and laser line imaging is presented. In this technique, the GA determines the vision parameters based on perspective projection geometry. The GA is constructed by means of an objective function, which is deduced from the equations of the laser line projection. To minimize the objective function, the GA performs a recombination of chromosomes through the SBX. This procedure provides the vision parameters, which are represented as chromosomes. The approach of the proposed GA is to achieve calibration and recalibration without external references and physical measurements. Thus, limitations caused by the missing of references are overcome to make self-calibration and three-dimensional (3-D) vision. Therefore, the proposed technique improves the self-calibration obtained by GAs with references. Additionally, 3-D vision is carried out via laser line position and vision parameters. The contribution of the proposed method is elucidated based on the accuracy of the self-calibration, which is performed with GAs. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： self-calibration genetic algorithms simulated binary crossover three-dimensional vision

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Self-adaptive genetic algorithms with simulated binary crossover

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EVOLUTIONARY COMPUTATION 2001年第2期9卷 197-221页

作者： Deb, K Beyer, HG Indian Inst Technol Dept Mech Engn Kanpur Genet Algorithms Lab Kanpur 208016 Uttar Pradesh India Univ Dortmund Dept Comp Sci 11 Syst Anal Grp D-44221 Dortmund Germany

Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored mainly with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using a simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with the SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need for emphasizing further studies on self-adaptive GAs.

关键词： self-adaptation genetic algorithms simulated binary crossover blend crossover real-coded GAs evolution strategies

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Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2007年第10期29卷 738-747页

作者： Kumar, Sushil Naresh, R. Natl Inst Technol Dept Elect Engn Hamirpur 177005 HP India

A simple and efficient optimisation procedure based on real coded genetic algorithm is proposed for the solution of short-term hydrothermal scheduling problem with continuous and non-smooth/non-convex cost function. The constraints like load-generation balance, unit generation limits, reservoir flow balance, reservoir physical limitations and reservoir coupling are also considered. The effectiveness of the proposed algorithm is demonstrated on a multichain-cascaded hydrothermal system that uses non-linear hydro generation function, includes water travel times between the linked reservoirs, and considers the valve point loading effect in thermal units. The proposed algorithm is equipped with an effective constraint-handling technique, which eliminates the need for penalty parameters. A simple strategy based on allowing infeasible solutions to remain in the population is used to maintain diversity. The same problem is also solved using binary coded genetic algorithm. The features of both algorithms are same except the crossover and mutation operators. In real coded genetic algorithm, simulated binary crossover and polynomial mutation are used against the single point crossover and bit-flipping mutation in binary coded genetic algorithm. The comparison of the two genetic algorithms reveals that real coded genetic algorithm is more efficient in terms of thermal cost minimisation for a short-term hydrothermal scheduling problem with continuous search space. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： short-term hydrothermal scheduling constraint-handling valve point loading effect real coded genetic algorithm simulated binary crossover polynomial mutation

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Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm

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APPLIED ENERGY 2009年第6期86卷 915-921页

作者： Subbaraj, P. Rengaraj, R. Salivahanan, S. SSN Coll Engn Kancheepuram 603110 Tamil Nadu India Kalasalingam Univ Srivilliputhur 626190 Tamil Nadu India

In this paper, a self adaptive real-coded genetic algorithm (SARGA) is implemented to solve the combined heat and power economic dispatch (CHPED) problem. The self adaptation is achieved by means of tournament selection along with simulated binary crossover (SBX). The selection process has a powerful exploration capability by creating tournaments between two solutions. The better solution is chosen and placed in the mating pool leading to better convergence and reduced computational burden. The SARGA integrates penalty parameterless constraint handling strategy and simultaneously handles equality and inequality constraints. The population diversity is introduced by making use of distribution index in SBX operator to create a better offspring. This leads to a high diversity in population which can increase the probability towards the global optimum and prevent premature convergence. The SARGA is applied to solve CHPED problem with bounded feasible operating region which has large number of local minima. The numerical results demonstrate that the proposed method can find a solution towards the global optimum and compares favourably with other recent methods in terms of solution quality, handling constraints and computation time. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： simulated binary crossover Polynomial mutation Co-generation Combined heat and power economic dispatch

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Bolstering efficient SSGAs based on an ensemble of probabilistic variable-wise crossover strategies

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SOFT COMPUTING 2016年第6期20卷 2149-2176页

作者： Gwak, Jeonghwan Jeon, Moongu Pedrycz, Witold Gwangju Inst Sci & Technol Sch Informat & Commun Gwangju 500712 South Korea Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2G6 Canada Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21413 Saudi Arabia

A crossover operator in genetic algorithms (GAs) plays an essential role as the main search operator to breed offspring by exchanging information between individuals. Although different types of crossover operators have been developed for real-coded GAs (RCGAs), there has been very little research on combining different crossover operators to build more effective and efficient RCGAs. In this work, we propose new steady-state generation alternation-based RCGAs (SSGAs) ameliorated with (i) an ensemble of different probabilistic variable-wise crossover strategies, which is realized by the corresponding parallel populations, to utilize synergetic and complementary effect with their efficient operations, and (ii) efficient operation at each evolution step to obtain further performance enhancement. To investigate the performance of this ensemble with respect to search abilities and computation time, we compare the proposed algorithms against various SSGAs when running 27 benchmark functions. Empirical studies showed that the proposed algorithms exhibit better performance than the contestant SSGAs on these functions. Moreover, a comparison with the state-of-the-art evolutionary algorithms on eight difficult benchmark functions clearly demonstrated outperformance of the proposed algorithms.

关键词： Real-coded genetic algorithms Steady state genetic algorithms Blend crossover Parent centric crossover simulated binary crossover Ensemble

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Nonconvex economic load dispatch using an efficient real-coded genetic algorithm

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APPLIED SOFT COMPUTING 2009年第1期9卷 321-329页

作者： Kumar, Sushil Naresh, R. Natl Inst Technol Dept Elect Engn Hamirpur 177005 HP India

An efficient optimisation procedure based on real-coded genetic algorithm (RCGA) is proposed for the solution of economic load dispatch (ELD) problem with continuous and nonsmooth/nonconvex cost function and with various constraints being considered. The effectiveness of the proposed algorithm has been demonstrated on different systems considering the transmission losses and valve point loading effect in thermal units. The proposed algorithm is equipped with an effective constraint handling technique, which eliminates the need for penalty parameters. For the purpose of comparison, the same problem has also been solved using binary-coded genetic algorithm (BCGA) and three other popular RCGAs. In the proposed RCGA, simulated binary crossover and polynomial mutation are used against the single point crossover and bit-flipping mutation in BCGA. It has been observed from the test results that the proposed RCGA is more efficient in terms of thermal cost minimisation and execution time for ELD problem with continuous search space than BCGA and some other popular RCGAs. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Economic load dispatch Constraint handling Valve point loading effect Real-coded genetic algorithm simulated binary crossover Polynomial mutation

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Efficient NURBS surface fitting via GA with SBX for free-form representation

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2017年第9期30卷 981-994页

作者： Munoz Rodriguez, J. Apolinar Ctr Invest Opt AC Opt Metrol Dept Leon Mexico

An accurate technique to perform NURBS surface fitting via genetic algorithms is presented. In this technique, the initial NURBS surface is generated by using object points as control points. Then, the genetic algorithm computes the weights and control points to obtain the NURBS surface fitting. The genetic algorithm is implemented through an objective function, which is deduced from NURBS surface and object points. The objective function is minimised by means of simulated binary crossover. This procedure is carried out based on the initial NURBS surface and NURBS surface constructed by employing the object height average as control point. Thus, the genetic algorithm provides the weights and control points of the NURBS surface that represent the object shape. The proposed algorithm improves the accuracy and speed of the NURBS fitting, which is created via genetic algorithms and gradient methods. It is because the proposed algorithm calculates the weights and control points from a known search space, which is produced by NURBS surfaces. Thus, the genetic algorithm minimises the objective function in fast form with high accuracy. The contribution of the proposed method is corroborated by an evaluation based on accuracy and speed of the traditional genetic algorithms and gradient methods.

关键词： surface fitting NURBS surface genetic algorithms simulated binary crossover

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Enhancement of Self-adaptive real-coded genetic algorithm using Taguchi method for Economic dispatch problem

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APPLIED SOFT COMPUTING 2011年第1期11卷 83-92页

作者： Subbaraj, P. Rengaraj, R. Salivahanan, S. SSN Coll Engn Kalavakkam 603110 Tamil Nadu India Kalasalingam Univ Srivilliputhur Tamil Nadu India

In this paper, a new optimization algorithm, namely Taguchi self-adaptive real-coded genetic algorithm (TSARGA) is proposed and implemented to solve economic dispatch (ED) problem with valve-point loading. The TSARGA combines the self-adaptive real-coded genetic algorithm with Taguchi method which can exploit the potential offspring. The self-adaptation is achieved by means of simulated binary crossover (SBX). Moreover, powerful exploration capability is achieved through tournament selection by creating tournaments between two solutions. The better solution is chosen and placed in the mating pool leading to better convergence and reduced computational burden. The systematic reasoning ability of the Taguchi method is incorporated after SBX operations to select the potential genes to achieve polynomial mutation, and consequently, enhance the robustness of the solution. The proposed TSARGA is effectively applied to solve the ED problem with valve-point loading with 6, 13 and 40-generator systems. The proposed method yields solutions towards global optimum and it compares far better with other methods in terms of solution quality, handling constraints and computation time. (c) 2009 Elsevier B.V. All rights reserved.

关键词： Self-adaptive real-coded genetic algorithm Taguchi method simulated binary crossover Polynomial mutation and valve-point loading

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On self-adaptive features in real-parameter evolutionary algorithms

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2001年第3期5卷 250-270页

作者： Beyer, HG Deb, K Univ Dortmund Dept Comp Sci 11 Syst Anal Grp D-44221 Dortmund Germany Indian Inst Technol Dept Mech Engn Kanpur Genet Algorithms Lab Kanpur 208016 Uttar Pradesh India

Due to the flexibility in adapting to different fitness landscapes, self-adaptive evolutionary algorithms (SA-EAs) have been gaining popularity in the recent past. In this paper, we postulate the properties that SA-EA operators should have for successful applications in real-valued search spaces, Specifically, population mean and variance of a number of SA-EA operators such as various real-parameter crossover operators and self-adaptive evolution strategies are calculated for this purpose. Simulation results are shown to verify the theoretical calculations. The postulations and population variance calculations explain why self-adaptive genetic algorithms and evolution strategies have shown similar performance in the past and also suggest appropriate strategy parameter values, which must be chosen while applying and comparing different SA-EAs.

关键词： blind crossover operator evolution strategies fuzzy recombination operator genetic algorithms population mean population variance self-adaptation simulated binary crossover test fitness landscapes

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Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

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Computers, Materials & Continua 2024年第1期78卷 375-392页

作者： Liang Zeng Junyang Shi Yanyan Li Shanshan Wang Weigang Li School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China School of Information Science and Engineering Wuhan University of Science and TechnologyWuhan430081China

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing *** involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple *** Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling ***,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and *** enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence ***,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex *** validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test *** experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

关键词： Multi-objective job shop scheduling non-dominated sorting genetic algorithm differential evolution simulated binary crossover

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