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

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.     

A simple and efficient real-coded genetic algorithm for constrained optimization

This paper presents a simple and efficient real-coded genetic algorithm (RCGA) for constrained real-parameter optimization. Different from some conventional RCGAs that operate evolutionary operators in a series framework, the proposed RCGA implements three specially designed evolutionary operators, named the ranking selection (RS), direction-based crossover (DBX), and the dynamic random mutation (DRM), to mimic a specific evolutionary process that has a parallel-structured inner loop. A variety of benchmark constrained optimization problems (COPs) are used to evaluate the effectiveness and the applicability of the proposed RCGA. Besides, some existing state-of-the-art optimization algorithms in the same category of the proposed algorithm are considered and utilized as a rigorous base of performance evaluation. Extensive comparison results reveal that the proposed RCGA is superior to most of the comparison algorithms in providing a much faster convergence speed as well as a better solution accuracy, especially for problems subject to stringent equality constraints. Finally, as a specific application, the proposed RCGA is applied to optimize the GaAs film growth of a horizontal metal-organic chemical vapor deposition reactor. Simulation studies have confirmed the superior performance of the proposed RCGA in solving COPs.     

Parameter estimation in mathematical models using the real coded genetic algorithms

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.     

A comparison study of self-adaptation in evolution strategies and real-coded genetic algorithms

This paper discusses the self-adaptive mechanisms of evolution strategies (ES) and real-coded genetic algorithms (RCGA) for optimization in continuous search spaces. For multi-membered evolution strategies, a self-adaptive mechanism of mutation parameters has been proposed by Schwefel. It introduces parameters such as standard deviations of the normal distribution for mutation into the genetic code and lets them evolve by selection as well as the decision variables. In the RCGA, crossover or recombination is used mainly for search. It utilizes information on several individuals to generate novel search points, and therefore, it can generate offspring adaptively according to the distribution of parents without any adaptive parameters. The present paper discusses characteristics of these two self-adaptive mechanisms through numerical experiments. The self-adaptive characteristics such as translation, enlargement, focusing, and directing of the distribution of children generated by the ES and the RCGA are examined through experiments.     

A taxonomy for the crossover operator for real‐coded genetic algorithms: An experimental study

The main real‐coded genetic algorithm (RCGA) research effort has been spent on developing efficient crossover operators. This study presents a taxonomy for this operator that groups its instances in different categories according to the way they generate the genes of the offspring from the genes of the parents. The empirical study of representative crossovers of all the categories reveals concrete features that allow the crossover operator to have a positive influence on RCGA performance. They may be useful to design more effective crossover models. © 2003 Wiley Periodicals, Inc.     

改进的自适应遗传算法在人体行为识别中的应用

为提高传统自适应遗传算法优化的BP神经网络对人体行为的识别率,提出了一种改进的自适应遗传算法优化的BP神经网络预测方法.该算法使用新的动态变化的交叉和变异分布指数计算公式来优化传统的二进制交叉和多项式变异操作,根据种群集中和分散的剧烈程度自适应地增大或减小交叉和变异的概率,极大地弥补了传统的交叉和变异操作所造成的破坏优...     

Investigation of self-organizing map for genetic algorithm

This paper describes self-organizing maps for genetic algorithm (SOM-GA) which is the combinational algorithm of a real-coded genetic algorithm (RCGA) and self-organizing map (SOM). The self-organizing maps are trained with the information of the individuals in the population. Sub-populations are defined by the help of the trained map. The RCGA is performed in the sub-populations. The use of the sub-population search algorithm improves the local search performance of the RCGA. The search performance is compared with the real-coded genetic algorithm (RCGA) in three test functions. The results show that SOM-GA can find better solutions in shorter CPU time than RCGA. Although the computational cost for training SOM is expensive, the results show that the convergence speed of SOM-GA is accelerated according to the development of SOM training.     

Adaptive directed mutation for real-coded genetic algorithms

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.     

Real-coded genetic algorithm with variable rates of cross-over and mutation: a basis of global optimization for multi-modal functions via interval technique

A hybrid method consisting of a real-coded genetic algorithm (RCGA) and an interval technique is proposed for optimizing bound constrained non-linear multi-modal functions. This method has two different phases. In phase I, the search space is divided into several subregions and the simple genetic algorithm (SGA) is applied to each subregion to find the one(s) containing the best value of the objective function. In phase II, the selected subregion is divided into two equal halves and the advanced GA, i.e. the RCGA, is applied in each half to reject the subregion where the global solution does not exist. This process is repeated until the interval width of each variable is less than a pre-assigned very small positive number. In the RCGA, we consider rank-based selection, multi-parent whole arithmetical cross-over, and non-uniform mutation depending on the age of the population. However, the cross-over and mutation rates are assumed as variables. Initially, these rates are high and then decrease from generation to generation. Finally, the proposed hybrid method is applied to several standard test functions used in the literature; the results obtained are encouraging. Sensitivity analyses are shown graphically with respect to different parameters on the lower bound of the interval valued objective function of two different problems.     

Multi-objective evolutionary algorithm using problem-specific genetic operators for community detection in networks

Automatic network clustering is an important method for mining the meaningful communities of complex networks. Uncovered communities help to understand the potential system structure and functionality. Many algorithms that use multiple optimization criteria and optimize a population of solutions are difficult to apply to real systems because they suffer a long optimization process. In this paper, in order to accelerate the optimization process and to uncover multiple significant community structures more effectively, a multi-objective evolutionary algorithm is proposed and evaluated using problem-specific genetic mutation and group crossover, and problem-specific initialization. Since crossover operators mainly contribute to performance of genetic algorithms, more problem-specific group crossover operators are introduced and evaluated for intelligent evolution of population. The experiments on both artificial and real-world networks demonstrate that the proposed evolutionary algorithm with problem-specific genetic operations has effective performance on discovering the community structure of networks.

     

Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process

Traditional genetic algorithms use only one crossover and one mutation operator to generate the next generation. The chosen crossover and mutation operators are critical to the success of genetic algorithms. Different crossover or mutation operators, however, are suitable for different problems, even for different stages of the genetic process in a problem. Determining which crossover and mutation operators should be used is quite difficult and is usually done by trial-and-error. In this paper, a new genetic algorithm, the dynamic genetic algorithm (DGA), is proposed to solve the problem. The dynamic genetic algorithm simultaneously uses more than one crossover and mutation operators to generate the next generation. The crossover and mutation ratios change along with the evaluation results of the respective offspring in the next generation. By this way, we expect that the really good operators will have an increasing effect in the genetic process. Experiments are also made, with results showing the proposed algorithm performs better than the algorithms with a single crossover and a single mutation operator.     

一种具有自然血亲排斥的遗传算法研究

分析了选择、交叉和变异操作下遗传算法早熟收敛问题,提出了一种具有自然血亲排斥的遗传算法,利用个体通婚记录信息和婚史继承方法有效地避免了三代自然血亲之间的近亲繁殖,并依据群体信息熵实现交叉率和变异率的自适应变化以及交叉变异位置的自适应确定,保证了群体的多样性和重要构造块的保护,扩大搜索空间,较好地解决了早熟收敛问题,从而实现全局收敛。仿真结果表明,所提出算法有较好的全局收敛能力。     

Structural inverse analysis by hybrid simplex artificial bee colony algorithms

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.     

参数可变自适应遗传算法在军事地形图矢量化中的应用

针对纸制军事地图矢量化问题进行研究,提出一种基于自适应遗传算法的军事地形图矢量化方法,并在生成新的个体后,增加个体修正操作,提高个体的适应度。在该算法中,提出一种即考虑进化代数对算法的影响,又考虑到每代不同个体适应度作用的自适应交叉概率和变异概率。通过与传统方法的实验比较,证明该方法的可用性和有效性。     

An adaptive fuzzy-genetic algorithm approach for building detection using high-resolution satellite images

We propose a new approach for building detection using high-resolution satellite imagery based on an adaptive fuzzy-genetic algorithm. This novel approach improves object detection accuracy by reducing the premature convergence problem encountered when using genetic algorithms. We integrate the fundamental image processing operators with genetic algorithm concepts such as population, chromosome, gene, crossover and mutation. To initiate the approach, training samples are selected that represent the specified two feature classes, in this case “building” and “non-building”. The image processing operations are carried out on a chromosome-by-chromosome basis to reveal the attribute planes. These planes are then reduced to one hyperplane that is optimal for discriminating between the specified feature classes. For each chromosome, the fitness values are calculated through the analysis of detection and mis-detection rates. This analysis is followed by genetic algorithm operations such as selection, crossover and mutation. At the end of each generation cycle, the adaptive-fuzzy module determines the new (adjusted) probabilities of crossover and mutation. This evolutionary process repeats until a specified number of generations has been reached. To enhance the detected building patches, morphological image processing operations are applied. The approach was tested on ten different test scenes of the Batikent district of the city of Ankara, Turkey using 1 m resolution pan-sharpened IKONOS imagery. The kappa statistics computed for the proposed adaptive fuzzy-genetic algorithm approach were between 0.55 and 0.88. The extraction performance of the algorithm was better for urban and suburban buildings than for buildings in rural test scenes.     

基于RCGA优化GRNN的实时动态目标追踪模型

针对三维环境中导弹追踪目标时制导和控制算法复杂而导致计算量非常大的问题,提出了一种基于隐性交叉遗传算法优化广义回归神经网络的实时动态目标追踪模型。通过将导弹防御区离散化为多个小模块生成输入数据,并针对每个可接受的目标参数数据集,使用RCGA估算导航常量和导弹注意时间;利用输入和输出的目标参数集生成GRNN所需的训练数据集;针对任意位置的目标轨道,将训练后的GRNN应用于实时导弹导引系统的实现中。通过战术目标仿真模型验证了所提算法的有效性及可靠性,仿真结果表明,相比其他几种目标追踪算法,算法取得了更好的实时性和更高的目标定位精度,脱靶率接近零。     

Reinforcement Hybrid Evolutionary Learning for Recurrent Wavelet-Based Neurofuzzy Systems

This paper proposes a recurrent wavelet-based neurofuzzy system (RWNFS) with the reinforcement hybrid evolutionary learning algorithm (R-HELA) for solving various control problems. The proposed R-HELA combines the compact genetic algorithm (CGA), and the modified variable-length genetic algorithm (MVGA) performs the structure/parameter learning for dynamically constructing the RWNFS. That is, both the number of rules and the adjustment of parameters in the RWNFS are designed concurrently by the R-HELA. In the R-HELA, individuals of the same length constitute the same group. There are multiple groups in a population. The evolution of a population consists of three major operations: group reproduction using the compact genetic algorithm, variable two-part crossover, and variable two-part mutation. Illustrative examples were conducted to show the performance and applicability of the proposed R-HELA method.     

改进的蝴蝶优化聚类算法

针对当前算法在求解聚类问题时存在精度低、速度慢及鲁棒性差等问题,提出一种改进的蝴蝶优化聚类算法,借鉴精英策略思想重新定义蝴蝶优化算法的局部搜索迭代公式,然后融合遗传算法的选择、交叉和变异操作.在1个人工数据集和5个UCI数据集上的测试结果表明所提出算法的性能,且与其他算法相比具有一定优势.     

Coefficient estimation of IIR filter by a multiple crossover genetic algorithm

This paper proposes an improved genetic algorithm (GA) with multiple crossovers to estimate the system coefficients for the infinite impulse response (IIR) digital filter. In the traditional crossover operation, it needs two parent chromosomes to achieve the crossover work, whereas in this paper the proposed algorithm selects three chromosomes for crossover in order to generate more promising offspring toward the problem solution. Each of unknown IIR coefficients is called a gene and the collection of genes forms a chromosome. A population of chromosomes is evolved by the genetic operations of reproduction, multiple crossover, and mutation. Finally, two illustrative examples including the band pass and band stop IIR filters are demonstrated to verify the proposed method.     

A Genetic Algorithm for Hybrid Flow-shop Scheduling with Multiprocessor Tasks

The hybrid flow-shop scheduling problem with multiprocessor tasks finds its applications in real-time machine-vision systems among others. Motivated by this application and the computational complexity of the problem, we propose a genetic algorithm in this paper. We first describe the implementation details, which include a new crossover operator. We then perform a preliminary test to set the best values of the control parameters, namely the population size, crossover rate and mutation rate. Next, given these values, we carry out an extensive computational experiment to evaluate the performance of four versions of the proposed genetic algorithm in terms of the percentage deviation of the solution from the lower bound value. The results of the experiments demonstrate that the genetic algorithm performs the best when the new crossover operator is used along with the insertion mutation. This genetic algorithm also outperforms the tabu search algorithm proposed in the literature for the same problem.