AN OPTIMAL DESIGN OF IIR DIGITAL FILTER USING PARTICLE SWARM OPTIMIZATION

In this article, a novel approach for infinite-impulse response (IIR) digital filters using particle swarm optimization (PSO) is presented. IIR filter is essentially a digital filter with recursive responses. Because the error surface of digital IIR filters is generally nonlinear and multimodal, so global optimization techniques are required in order to avoid local minima. This study is based on a heuristic way to design IIR filters. PSO is a powerful global optimization algorithm introduced in combinatorial optimization problems. This study finds the optimum coefficients of the IIR digital filter through PSO. It is found that the calculated values are more optimal than the FDA tool and GA available for the design of the filter in MATLAB. Design of low-pass and high-pass IIR digital filters is proposed in order to provide an estimate of the transition band. The simulation results of the employed examples show an improvement on the transition band. The stability of designed filters is described by the position of Pole-Zeros.     

求解k条最优路径问题的遗传算法

文章提出的任意两点间k条最优路径问题的遗传算法,采用节点的自然路径作为染色体编码,根据路径节点的连接实施染色体的交叉操作,将节点路径块作为染色体的变异基因块实施变异操作。算法结构简明,收敛速度快,可应用于求解大规模网络中的多条最优路径问题。     

一种基于复合交叉的实数编码遗传算法

提出了一种基于复合交叉的实数编码遗传算法。通过对父代染色体间的区域进行多次交叉操作,复合交叉操作保留了被搜索区域的信息。算法首先对父代染色体进行复合交叉操作,然后利用包含在新个体集合中的信息进行信息最大化选择,对每一代个体进行基于适应度的选择。集合中信息冗余的个体被从种群中删除,位于欠搜索区域中的个体被保留。由于算法能够始终保持种群的多样性,算法不仅能搜索到全局最优点,同时也能找到尽量多的局部极值点。利用算法对多极值函数进行了寻优,仿真结果表明了算法的有效性。     

一种多染色体交叉的小生境遗传算法

生物进化一般只在两个个体间进行交配,传统的遗传算法也仅在两个染色体之间进行交叉操作。使用在三个以上的染色体进行交叉操作,并设计了多染色体交叉的算子。多染色体交叉算法可显著提高算法局部寻优能力和收敛速度,但由于收敛速度过快容易产生早熟现象。因此,设计了一种带子种群淘汰策略的小生境算法,可避免算法产生早熟现象。通过几种遗传算法的实验结果比较,证明多染色体交叉算法在多峰优化中的效果要优于传统遗传算法。     

A genetic algorithm with the heuristic procedure to solve the multi-line layout problem

The paper presents a genetic algorithm-based meta-heuristic to solve the facility layout problem (FLP) in a manufacturing system, where the material flow pattern of the multi-line layout is considered with the multi-products. The matrix encoding technique has been used for the chromosomes under the objective of minimizing the total material handling cost. The proposed algorithm produces a table with the descending order of the data corresponding to the input values of the flow and cost data. The generated table is used to create a schematic representation of the facilities, which in turn is utilized to heuristically generate the initial population of the chromosomes and to handle the heuristic crossover and mutation operators. The efficiency of the proposed algorithm has been proved through solving the two examples with the total cost less than the other genetic algorithms, CRAFT algorithm, and entropy-based algorithm.     

基于遗传算法的最短路径路由优化算法

论述了用启发式遗传算法解决最短路径路由的优化问题.采用可变长度染色体(路由串)和它的基因(节点)应用于编码问题.交叉操作在交叉点进行部分染色体(部分路由)交换,变异操作维持种群的多样性.该算法采用简单维护操作维护好所有的不可行的染色体.交叉操作和变异操作相结合保证了最优解的搜索能力和解的全局收敛性.计算机仿真实验证明该算法快速有效,可靠性高。     

热轧实施计划中最优倒垛问题的整数规划模型及遗传算法

对钢铁企业板坯库中的最优倒垛问题建立了0和1整数规划模型.这一模型是一个 二次规划模型,且目标函数的系数与变量的取值相关联,属于NP-难问题,获得较大规模的最 优解是不可能或非常困难.为了求解此问题,本文构造了改进遗传算法:(1)提出了适合于最 优倒垛问题的遗传编码,运用此编码,不但能够产生可行的初始染色体,而且能够保证在交叉 和变异操作后的染色体仍然可行;(2)改进了遗传算法结构,在新的结构中,增加了一个培育 操作,改进了交叉操作.通过精选随机产生的问题例子的实验显示出,提出的算法的性能明显 好于原系统的启发式算法,最好的改进率达到7.04％.     

Adaptive infinite impulse response system identification using opposition based hybrid coral reefs optimization algorithm

An efficient global adaptive algorithm is required to determine the parameters of infinite impulse response (IIR) filter owing to the error cost surface of adaptive IIR system identification problem being generally nonlinear and non-differentiable. In this paper, a new bio-inspired algorithm, called opposition based hybrid coral reefs optimization algorithm (OHCRO) is applied for the IIR system identification problem. Coral reefs optimization algorithm (CRO) is a novel global algorithm, which mimics the behaviors of corals’ reproduction and coral reef formation. OHCRO is a modified version of CRO, on the one hand utilizing opposition based learning to accelerate global convergence, on the other hand cooperating with rotational direction method to enhance the local search capability. In addition, the Laplace broadcast spawning and power mutation brooding operator are used to maintain the diversity. The simulation studies have been performed for the performance comparison of genetic algorithm, particle swarm optimization and its variants, differential evolution and its variants and the proposed OHCRO for well-known benchmark examples with same order and reduced order filters. Simulation results and comparative studies justify the efficacy of the OHCRO based system identification approach in terms of convergence speed, identified coefficients and fitness values. In conclusion, OHCRO is a promising method for adaptive IIR system identification.     

基于遗传算法的彩色图像滤波器研究

为实现彩色图像噪声的滤波,基于遗传算法技术构建了一种新型的彩色图像滤波器,在彩色图像的矢量中值滤波过程中,该滤波器利用遗传算法具有寻找全局最优解的能力,获得滤波器窗口的最优权值。从滤波效果看,有一定提高,并进一步提出了一种基于个体相似性的遗传算法：当种群的多样性较好时,采用标准的交叉策略;当种群的多样性较差时,根据个体的相似性选择个体配对,避免相同的个体配对交叉,减少高度相似个体进行配对交叉的概率,从而提高交叉操作的效率。     

Capacitated lot sizing by using multi-chromosome crossover strategy

The capacitated lot sizing problem (CLSP) arises when, under capacity constraints, the decision maker has to determine the production schedule and lot sizes that will minimize the total costs involved. The costs considered in this article are order, inventory carrying, and labor costs. The fitness function for the chromosome is computed using these cost elements. Next, the chromosomes are partitioned into good and poor segments based on the individual product chromosomes. This information is later used during crossover operation and results in crossover among multiple chromosomes. Product chromosomes are grouped into three groups, group 1 (top X%), group 2 (next Y%), and group 3 (last Z%). Product chromosomes from Groups 1, 2 and 3 can only form pairs with chromosomes from group 1. Besides, different crossover and mutation probabilities are applied for each group. The results of the experimentation showed that the different strategies of the proposed approach produced much better results than the classical genetic algorithm. This article has been submitted to the Journal of Intelligent Manufacturing for publication.     

基于遗传算法的QoS多播路由优化算法

研究了带宽、延时、延时抖动和分组丢失率约束以及费用最小的QoS多播路由优化问题,提出了一种启发式遗传算法、该算法采用可变长度染色体(路由串)和它的基因(节点)应用于编码问题。交叉操作在交叉点进行部分染色体(部分路由)交换,变异操作维持种群的多样性。该算法采用简单维护操作维护好所有的不可行的染色体,交叉操作和变异操作相结合保证了最优解的搜索能力和解的全局收敛性。计算机仿真实验证明该算法快速有效,可靠性高。     

一种变形杂交算子——轮转杂交算子

杂交算子是遗传算法的三个基本算子之一,它在改进解群质量方面发挥着重要作用。现有遗传算法的杂交算子通常选择两个个体组成母本进行杂交运算。该文提出了一种选择三个个体组成母本进行轮转杂交的思想,文章称这种新的杂交算子为轮转杂交算子。首先通过理论分析证明,它能够扩大遗传算子搜索子空间,使收敛结果更趋于最优解。然后将其用于分布式系统任务调度问题,模拟结果说明是可行的。     

移动云环境面向多重服务选择的计算卸载算法

移动云计算可以通过计算卸载改善移动设备的能效和应用的执行延时。然而面对云端的多重服务选择时,计算卸载决策是NP问题。为了解决这一问题,提出一种遗传算法寻找计算卸载的最优应用分割决策解。遗传种群初始化中,算法联立预定义和随机染色体方法进行初始种群的生成,减少了无效染色体的发生比例。同时,算法为预定义的预留种群设计一种特定的基于汉明距离函数的适应度函数,更好地衡量了染色体间的差异。种群交叉中分别利用近亲交配与杂交繁育丰富了种群个体。算法通过修正的遗传操作减少了无效解的产生,以更合理的时间代价获得了应用分割的最优可行解。应用现实的移动应用任务图进行仿真实验评估了算法效率。评估结论表明,所设计的遗传算法在应用执行能耗、执行时间以及综合权重代价方面均优于对比算法。     

A genetic algorithm with gene rearrangement for K-means clustering

In this paper, a new clustering algorithm based on genetic algorithm (GA) with gene rearrangement (GAGR) is proposed, which in application may effectively remove the degeneracy for the purpose of a more efficient search. A new crossover operator that exploits a measure of similarity between chromosomes in a population is also presented. Adaptive probabilities of crossover and mutation are employed to prevent the convergence of the GAGR to a local optimum. Using the real-world data sets, we compare the performance of our GAGR clustering algorithm with K-means algorithm and other GA methods. An application of the GAGR clustering algorithm in unsupervised classification of multispectral remote sensing images is also provided. Experiment results demonstrate that the GAGR clustering algorithm has high performance, effectiveness and flexibility.     

Genetic algorithms for video segmentation

The current paper presents a new genetic algorithm (GA)-based method for video segmentation. The proposed method is specifically designed to enhance the computational efficiency and quality of the segmentation results compared to standard GAs. The segmentation is performed by chromosomes that independently evolve using distributed genetic algorithms (DGAs). However, unlike conventional DGAs, the chromosomes are initiated using the segmentation results of the previous frame, instead of random values. Thereafter, only unstable chromosomes corresponding to moving object parts are evolved by crossover and mutation. As such, these mechanisms allow for effective solution space exploration and exploitation, thereby improving the performance of the proposed method in terms of speed and segmentation quality. These advantages were confirmed based on experiments where the proposed method was successfully applied to both synthetic and natural video sequences.     

基于父个体相似度的自适应遗传算法

标准遗传算法在产生后代个体时采用先交叉后变异的策略,一方面当父个体非常相似时,交叉操作很难产生新的个体,影响算法对新的解空间进行搜索,从而导致种群多样性的丧失;另一方面交叉产生的优秀个体再历经变异,极有可能遭破坏而影响算法的收敛性。该文根据染色体的相似性,给出了个体相似度的概念,并在此基础上提出了依据父个体相似度的大小自适应地选择遗传算子(交叉或变异)的遗传算法。仿真实验表明,与采用常规遗传策略的遗传算法相比,新算法能显著提高解的质量和收敛速度。     

基于跳跃基因算子的改进实数遗传算法

为了避免遗传算法在求解数值优化问题时出现搜索能力差、多样性缺失等弊端,提出一种基于实数编码的改进遗传算法(IRCGA).算法集成两个特别设计的算子:模拟二进制跳跃基因算子(SBJG)和多方向交叉算子(MX).SBJG算子以染色体为操作对象,本质上模拟了二进制跳跃基因操作中的插入运动,即利用一种随机的方式将选定的染色体块插入到染色体位点,实现种群内部染色体间的转位,为种群提供额外的遗传多样性;MX算子通过增加交叉方向的方式扩大算子的搜索区域,从而提升后代个体质量与算法的搜索能力.在11个实例的基础上进行对比实验,结果表明,采用改进算子能够明显提升算法在求解数值优化问题时的性能,同时,相比于其他先进有效的算法,IRCGA具有较强的搜索能力且能够维持一定的种群多样性,从而验证了改进算法的有效性和可行性.     

基于遗传算法的WSNs多路径路由优化

对WSNs的拓扑结构进行分析,建立其路由网络模型,结合遗传算法基本原理,提出了一种求解WSNs最优多路径路由算法。该算法采用可变长度染色体编码,采取选择、交叉和变异操作,充分利用基站的信息资源和强大功能,全局优化了WSNs多路径路由。仿真结果表明,该优化机制有效延长了WSNs的生命周期,改善了网络性能。     

混合粗粒度遗传算法在约束最优化问题中的应用

选取粗粒度遗传算法，并针对其过早收敛、收敛速度慢的缺陷进行改进，提出混合粗粒度遗传算法。混合粗粒度遗传算法按照适应度函数值对染色体群体进行分组，各分组采用不同的惩罚系数、交叉、变异算子；同时采用同种互斥和最优解保留策略。实验结果表明该算法在约束最优化问题中应用良好。     

A genetic algorithm for shortest path routing problem and the sizing of populations

This paper presents a genetic algorithmic approach to the shortest path (SP) routing problem. Variable-length chromosomes (strings) and their genes (parameters) have been used for encoding the problem. The crossover operation exchanges partial chromosomes (partial routes) at positionally independent crossing sites and the mutation operation maintains the genetic diversity of the population. The proposed algorithm can cure all the infeasible chromosomes with a simple repair function. Crossover and mutation together provide a search capability that results in improved quality of solution and enhanced rate of convergence. This paper also develops a population-sizing equation that facilitates a solution with desired quality. It is based on the gambler ruin model; the equation has been further enhanced and generalized. The equation relates the size of the population, quality of solution, cardinality of the alphabet, and other parameters of the proposed algorithm. Computer simulations show that the proposed algorithm exhibits a much better quality of solution (route optimality) and a much higher rate of convergence than other algorithms. The results are relatively independent of problem types for almost all source-destination pairs. Furthermore, simulation studies emphasize the usefulness of the population-sizing equation. The equation scales to larger networks. It is felt that it can be used for determining an adequate population size in the SP routing problem.