胖树型片上网络的低能耗映射算法

为解决胖树型片上网络的映射问题,针对该网络不同层路由器间链路长度不同的特点,提出一种低能耗映射优化模型,并设计一种基于捕食搜索策略的差分进化算法(PSDE)进行模型求解.该算法将捕食搜索策略与差分进化算法相结合,弥补了差分进化算法易陷入局部最优的不足,增强了捕食搜索策略的局部搜索能力.仿真实验结果表明,与遗传算法和模拟退火算法相比,PSDE可以缩短运行时间,并获得低能耗、高质量的优化映射结果.     

基于多中心城市策略的分层元胞遗传算法

为提高分层元胞遗传算法在解决复杂函数优化问题时的求解精度、收敛速度和求解效率.在分层元胞遗传算法的基础上借鉴西方经济理论中中心城市思想提出了一种基于多中心城市策略的分层元胞遗传算法.该算法在进化初期选择适应度值高的多个个体作为种群进化过程中的中心城市,中心城市周围元胞空间的个体按照一定的迁移规则往中心城市迁移,全局最优...     

基于改进元胞多目标遗传算法的机床主轴优化

为了进一步提高元胞遗传算法在求解多目标优化问题时的收敛性和分布性。在多目标元胞遗传算法的基础上,引入了三维空间元胞,提出了三维元胞多目标遗传算法。采用多目标基准测试函数对该算法进行了测试,并将其与目前比较流行的几种多目标遗传算法进行对比。结果表明,此种算法在收敛性和分布性上取得了更好的效果。采用以上这几种算法分别对机床主轴多目标优化问题进行了求解,相比其他几种算法,改进的多目标元胞遗传算法得到了更优的结果,说明了改进的算法在求解此问题时行之有效。     

元胞遗传算法的全局优化性能仿真分析

元胞遗传算法将遗传操作限制在邻域内进行,减缓了优势个体在群体中的扩散速度,具有更好的全局探索能力,在求解复杂优化问题中显示出优越性.与传统遗传算法对比,以选择压力作为分析手段,对元胞遗传算法进行定性分析.通过求解具有不同特征的函数,分析进化过程群体多样性变化.从进化过程群体分布图,直观得出元胞遗传算法具有较好的维持群体多样性能力;统计结果表明,元胞遗传算法能极大提高全局收敛率,并且求解稳定性更好.     

基于线性映射的多物种捕食元胞遗传算法

为提高捕食元胞遗传算法的性能及在基因型上对种群进行区分,提出一种基于线性映射的多物种捕食元胞遗传算法。该算法通过引入映射矩阵,改变种群基因型到表现型的映射关系,使不同物种间所携带的遗传信息不同。在进化过程中,不同物种采用不同的遗传方式进行交叉,并根据种群离散程度自适应调整映射矩阵系数控制种群进化方向,有效提高算法跳出局部最优的能力。对若干低维及高维典型函数进行仿真实验,将文中算法与其它同类算法对比,实验结果表明,文中算法在全局收敛率上具有较明显的优势。     

基于捕食搜索策略的遗传算法研究*

针对标准遗传算法易陷入局部最优而出现早熟,提出了一种基于捕食搜索策略的遗传算法。该算法在进化中模拟动物捕食搜索的过程,并根据种群中个体最优适应值来动态改变交叉和变异概率,从而加强算法的全局搜索和局部优化的能力。仿真实验表明该算法是有效的。     

基于种群分级遗传算法的受限广义预测控制

存在约束的控制过程限制了传统GPC方法在工业领域中的广泛应用.本文利用遗传算法来解决受限GPC算法的优化问题.当控制作用突破受限条件时,启用遗传算法来处理带约束的非线性优化问题,并以此作为滚动优化策略,求得最优控制律.遗传算法采用种群分级制度,充分利用所获信息对高级种群进行初始化,大大提高了算法的局部搜索能力.同时低级...     

求解约束优化问题的改进元胞遗传算法

针对约束优化问题,提出一种改进元胞遗传算法.将自适应ε约束处理技术与元胞遗传算法结合,对于自适应ε约束处理技术中的截断进化代数,在其前期提出偏好性指标概念,随机选取满足约束条件较好的个体引导种群快速向可行域逼近,在其后期采用改进柯西变异算子避免陷入局部最优.基于一组标准测试函数进行测试,与其它算法进行对比,结果表明算法具有较好的收敛精度,且在高维函数中取得更优的结果,验证了算法的有效性与先进性.     

基于元胞遗传算法的智能组卷的研究

智能组卷是一个包含多重约束条件的目标优化问题,遗传算法的群体搜索策略可以为多目标优化提供较好的解决方案。但传统的遗传算法在组卷过程中存在收敛速度慢、收敛性较差等缺点,组出的试卷质量不高。提出一种新的元胞遗传组卷算法,将群体中的所有元胞按照一定的演化规则演化之后,再进行遗传操作,并把该算法应用到智能组卷中。实验结果表明,新的元胞遗传组卷算法与传统的遗传组卷算法相比,可以有效地提高收敛速度,并能进一步改善收敛性,组出的试卷更加符合人们的要求。     

一种动态邻居元胞遗传算法

研究一种改进的元胞遗传算法。将遗传算法中的个体适应度和元胞自动机中的邻居定义做了结合,提出基于元胞间距离以及元胞个体适应度的"影响力算子",并作为算子中心元胞判断邻居的依据,从而形成改进算法,并对改进算法的基本性能的进行了两组定量分析,一是影响力算子对选择压和多样化损失的控制,另一部分是将算法与改良后传统元胞遗传算法做了对比测试。结果显示,即便使用最朴素的影响力算子而且不采用其它优化手段的情况下,算法依然能对选择压和多样化损失进行有效地控制,并且相较于使用了最优个体保持和小范围竞争择优的传统元胞遗传算法收敛率提高了约10%。     

基于Predator-Prey行为的双种群粒子群优化算法

根据生物的捕食-食饵(predator-prey)行为的规律,提出了一种双种群粒子群优化(DPPSO)算法.将粒子分成predator和prey两个种群,其中predator种群每间隔一定的迭代次数后排斥prey种群.在排斥的过程中,predator种群采用“擒贼先擒王”的策略,逐步向prey种群的群体最优位置靠近,同...     

Synergic predator-prey optimization for economic thermal power dispatch problem

This paper introduces a synergic predator-prey optimization (SPPO) algorithm to solve economic load dispatch (ELD) problem for thermal units with practical aspects. The basic PPO model comprises prey and predator as essential components. SPPO uses collaborative decision for movement and direction of prey and maintains diversity in the swarm due to fear factor of predator, which acts as the baffled state of preys’ mind. In the SPPO, the decision making of prey is bifurcated into corroborative and impeded parts. It comprises four behaviors namely inertial, cognitive, collective swarm intelligence, and prey's individual and neighborhood concern of predator. The prey particle memorizes its best and not-best positions as experiences. In this research work, to improve the quality of prey swarm, which influence convergence rate, opposition based initialization is used. To verify robustness of proposed algorithm general benchmark problems and small, medium, and large power generation test power system are simulated. These test systems have non-linear behavior due to multi-fuel options and practical constraints. The constraints of prohibited operating zone and ramp rate limits of power generators’ are handled using heuristics. Newton–Raphson procedure is exploited to attain the transmission losses using load flow analysis. The outcomes of SPPO are compared with the results described in literature and are found satisfactory.     

Intelligent computing for numerical treatment of nonlinear prey–predator models

In this study, a new computing paradigm is presented for evaluation of dynamics of nonlinear prey–predator mathematical model by exploiting the strengths of integrated intelligent mechanism through artificial neural networks, genetic algorithms and interior-point algorithm. In the scheme, artificial neural network based differential equation models of the system are constructed and optimization of the networks is performed with effective global search ability of genetic algorithm and its hybridization with interior-point algorithm for rapid local search. The proposed technique is applied to variants of nonlinear prey–predator models by taking different rating factors and comparison with Adams numerical solver certify the correctness for each scenario. The statistical studies have been conducted to authenticate the accuracy and convergence of the design methodology in terms of mean absolute error, root mean squared error and Nash-Sutcliffe efficiency performance indices.     

Group technology based adaptive cell formation using predator-prey genetic algorithm

In recent years, there has been a considerable growth of application of group technology in cellular manufacturing. This has led to investigation of the primary cell formation problem (CFP), both in classical and soft-computing domain. Compared to more well-known and analytical techniques like mathematical programming which have been used rigorously to solve CFPs, heuristic approaches have yet gained the same level of acceptance. In the last decade we have seen some fruitful attempts to use evolutionary techniques like genetic algorithm (GA) and Ant Colony Optimization to find solutions of the CFP. The primary aim of this study is to investigate the applicability of a fine grain variant of the predator-prey GA (PPGA) in CFPs. The algorithm has been adapted to emphasize local selection strategy and to maintain a reasonable balance between prey and predator population, while avoiding premature convergence. The results show that the algorithm is competitive in identifying machine-part clusters from the initial CFP matrix with significantly less number of iterations. The algorithm scaled efficiently for large size problems with competitive performance. Optimal cluster identification is then followed by removal of the bottleneck elements to give a final solution with minimum inter-cluster transition cost. The results give considerable impetus to study similar NP-complete combinatorial problems using fine-grain GAs in future.     

Towards a dynamic modeling of the predator prey problem

This article addresses a new dynamic optimization problem (DOP) based on the Predator-Prey (PP) relationship in nature. Indeed, a PP system involves two adversary species where the predator’s objective is to hunt the prey while the prey’s objective is to escape from its predator. By analogy to dynamic optimization, a DOP can be seen as a predation between potential solution(s) in the search space, which represents the predator, and the moving optimum, as the prey. Therefore we define the dynamic predator-prey problem (DPP) whose objective is to keep track of the moving prey, so as to minimize the distance to the optimum. To solve this problem, a dynamic approach that continuously adapts to the changing environment is required. Accordingly, we propose a new evolutionary approach based on three main techniques for DOPs, namely: multi-population scheme, random immigrants, and memory of past solutions. This hybridization of methods aims at improving the evolutionary approaches ability to deal with DOPs and to restrain as much as possible their drawbacks. Our computational experiments show that the proposed approach achieves high performance for DPP and and competes with state of the art approaches.     

Prey predator hyperheuristic

Prey predator algorithm is a population based metaheuristic algorithm inspired by the interaction between a predator and its prey. In the algorithm, a solution with a better performance is called best prey and focuses totally on exploitation whereas the solution with least performance is called predator and focuses totally on exploration. The remaining solutions are called ordinary prey and either exploit promising regions by following better performing solutions or explore the solution space by randomly running away from the predator. Recently, it has been shown that by increasing the number of best prey or predator, it is possible to adjust the degree of exploitation and exploration. Even though, this tuning has the advantage of easily controlling these search behaviors, it is not an easy task. As any other metaheuristic algorithm, the performance of prey predator algorithm depends on the proper degree of exploration and exploitation of the decision space. In this paper, the concept of hyperheuristic is employed to balance the degree of exploration and exploitation of the algorithm. So that it learns and decides the best search behavior for the problem at hand in iterations. The ratio of the number of the best prey and the predators are used as low level heuristics. From the simulation results the balancing of the degree of exploration and exploitation by using hyperheuristic mechanism indeed improves the performance of the algorithm. Comparison with other algorithms shows the effectiveness of the proposed approach.     

一种基于多样性度量的多目标化进化算法

针对如何通过附加的方法对多目标化问题进行理论分析,提出并证明了选择附加函数的3个前提条件.提出一种多目标化进化算法,根据种群中个体的多样性度量进行多目标化,并采用改进的非劣分类遗传算法对构造所得的多目标优化问题进行多目标优化.在静态和动态两种环境下进行算法性能验证,结果表明,在种群多样性保持、处理欺骗问题、动态环境下的适应能力等方面,所提算法明显优于其他同类算法.     

一种基于生态捕食模型的网络负载平衡算法

针对分布式系统的负载分配问题,通过对生态捕食模型的研究,提出一种基于生态差分方程数学模型、分布式控制的网络负载平衡算法。该算法将两节点对应到生态系统的捕食者和被捕食者,将各节点的负载信息对应到种群规模,利用两种群生态差分方程数学模型动态调整节点负载信息,达到网络负载平衡。实验证明了该算法的有效性。     

基于改进的AIA优化ANN的多移动机器人追捕研究

建立了一个包含多个捕猎机器人和单个猎物机器人的动态空间模型,并构建了捕猎机器人的AIAE-ANN行为决策系统。人工神经网络(ANN)所有的连接权值采用改进型人工免疫算法(AIAE)进行优化,使神经网络的性能不断得到进化,最终可生成一个性能优良的行为决策系统,从而完成捕猎机器人的围捕。仿真实验表明:用AIAE训练,能有效地应用于追捕系统的多移动机器人研究。