Ensemble feature selection integrating elitist roles and quantum game model

To accelerate the selection process of feature subsets in the rough set theory （RST）, an ensemble elitist roles based quantum game （EERQG） algorithm is proposed for feature selec- tion. Firstly, the multilevel elitist roles based dynamics equilibrium strategy is established, and both immigration and emigration of elitists are able to be self-adaptive to balance between exploration and exploitation for feature selection. Secondly, the utility matrix of trust margins is introduced to the model of multilevel elitist roles to enhance various elitist roles＇ performance of searching the optimal feature subsets, and the win-win utility solutions for feature selec- tion can be attained. Meanwhile, a novel ensemble quantum game strategy is designed as an intriguing exhibiting structure to perfect the dynamics equilibrium of multilevel elitist roles. Finally, the en- semble manner of multilevel elitist roles is employed to achieve the global minimal feature subset, which will greatly improve the fea- sibility and effectiveness. Experiment results show the proposed EERQG algorithm has superiority compared to the existing feature selection algorithms.

CONVERGENCE RATES FOR A CLASS OF EVOLUTIONARY ALGORITHMS WITH ELITIST STRATEGY

This paper discusses the convergence rates about a class of evolutionary algorithms in general search spaces by means of the ergodic theory in Markov chain and some techniques in Banach algebra. Under certain conditions that transition probability functions of Markov chains corresponding to evolutionary algorithms satisfy, the authors obtain the convergence rates of the exponential order. Furthermore, they also analyze the characteristics of the conditions which can be met by genetic operators and selection strategies.

Elitist Reconstruction Genetic Algorithm Based on Markov Random Field for Magnetic Resonance Image Segmentation

In this paper, elitist reconstruction genetic algorithm （ERGA） based on Markov random field （MRF） is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every generation, and for each solution a fitness value is calculated according to a fitness function, which is constructed based on the MRF potential function according to Metropolis function and Bayesian framework. After the improved selection, crossover and mutation, an elitist individual is restructured based on the strategy of restructuring elitist. This procedure is processed to select the location that denotes the largest MRF potential function value in the same location of all individuals. The algorithm is stopped when the change of fitness functions between two sequent generations is less than a specified value. Experiments show that the performance of the hybrid algorithm is better than that of some traditional algorithms.

具有Elitist选择的遗传算法的收敛速度估计

模拟进化计算是近年来信息科学、人工智能与计算机科学的“热点”研究领域,而由此派生的遗传算法是一族通过模拟自然进化过程搜索最优解的方法。其基本思想源于60年代,Holland在研究机器学习过程中,受达尔文进化论——适者生存的启发,而获得的一种概率搜索算法。该方法在早期作为一种自适应机器学习方法,而近几年在解全局优化问题、人工神经网络的训练与结构优化、程序设计自动化中的查错处理等方面已取得成功的应用,显示了非常广泛的应用前景。

蒙开个地区河库连通工程的泵站运行方式研究

蒙开个地区河库连通工程包含南洞一级泵站、南洞二级泵站和长桥海泵站,研究水泵机组不同运行方式对各泵站运行能耗的影响有助于提升工程运行效益。构建了考虑定速节流运行方式和变频调节运行方式下3座泵站站内机组优化组合模型,运用标准遗传算法、精英保留遗传算法对模型进行求解,考虑变频调节装置成本,分析了改造方案的经济性。结果表明:精英保留遗传算法在求解精度上与标准遗传算法相差较小,但在求解效率上显著优于标准遗传算法,能够更好地求解蒙开个地区河库连通工程的泵站机组优化组合模型;变频调节运行方式的节能效果最为显著,各级泵站在流量范围中段存在高效节能段;当各级泵站大部分时间提水流量低于0.7 m^(3)/s或接近满带流量,无需变频改造;其他情况下改造后各级泵站能快速实现正向收益,经济效益较为明显。研究可为蒙开个地区河库连通工程的泵站节能改造提供参考。

不同驱动系统下纯电动汽车关键性能对比研究

为比较纯电动汽车不同驱动系统的关键性能,基于同一整车参数和某公司提供的可变绕组永磁同步电机试验数据,对纯电动汽车电机驱动系统开展了相关研究。基于精英保留遗传算法和动态规划理论,对单挡、两挡电控机械式自动变速器驱动系统的速比进行了设计优化。采用了精英保留遗传算法和动态规划理论对系统速比进行设计优化,并对可变绕组永磁同步电机绕组切换过程进行了动力性和经济性设计。仿真结果表明,在动力性上,两挡自动变速器驱动系统的加速性能最优;在经济性上,可变绕组永磁同步电机驱动系统的百公里能耗最小,单挡自动变速器驱动系统的动力性和经济性表现最不理想。

重载物流AGV机器人悬架结构优化设计

为提高重载物流AGV机器人的路面适应能力,设计适合物流仓储车间的AGV移动系统。为该系统构建纵臂式独立悬架,具有调节机构以改变减振器角度和纵臂长度。基于简化的悬架系统支架模型,进行静力学特性分析;构建以支架不同尺寸参数为变量,以结构质量为目标函数,以最大变形量和最大等效应力为约束条件的优化数学模型,采用受控精英多目标遗传算法对设计变量进行优化。校核优化后结构强度和刚度满足使用要求,证明了结构优化的合理性,实现了构件精益制造,提高了材料的利用率。

MRMR-SA-EGA-ELM的叶绿素a浓度预测模型研究

为提高叶绿素a浓度的预测精度,以南太湖区域-湖州市新塘港2020年5月至11月份的水质监测数据为原始样本数据,使用最大相关最小冗余算法(MRMR)从原始样本数据中选取效果更优的特征值,作为预测模型的输入数据,将精英遗传算法(EGA)与模拟退火算法(SA)组合优化极限学习机(ELM)网络的初始参数,最终构建MRMR-SA-EGA-ELM叶绿素a浓度预测模型。实验结果表明,MRMR-SA-EGA-ELM模型预测叶绿素a浓度的平均绝对误差(MAE)、均方误差(MSE)、决定系数(R^(2))分别为1.009、1.607、0.903,而ELM模型预测结果的MAE、MSE、R^(2)分别为2.078、8.249、0.562,MRMR-SA-EGA-ELM模型的效果得到显著提升,可实现对叶绿素a浓度的准确预测。

基于增强精英保留遗传算法的虚拟微网群动态划分及能量局域自治

互联的多微网系统作为可再生能源利用的有效形式之一,随着“双碳”工作的推进越发受到重视。该文旨在研究基于海量可控设备的优化调节方法,以促进可再生能源充分就地消纳,提高多微网系统运行经济性。然而,对海量可控设备的全局控制将面临“维数灾难”的挑战。现有研究中基于地理分布的分区优化能够实现“降维控制”,但可再生能源出力的波动性与负荷在时间上的变化和空间上的迁移,都会导致固定分区的方法难以适用于多微网系统态势变迁下的能量动态管控。针对上述问题,首先建立微电网动态模型;进而提出通过增强精英保留遗传算法(strengthen elitist genetic algorithm,SEGA)将互联多微网场景划分为多个边界可动态调整的虚拟微网群,并进行能量“局域自治”优化;最后基于改进的IEEE-123节点模型进行仿真,结果显示1 h内动态边界虚拟微网群的总运行成本比固定边界虚拟微网群的总运行成本降低了13.6%,且所采用的SEGA的求解时间比传统遗传算法减少了7.09%。

失效情景下考虑拥堵及偏好的多式联运路径选择

为保障集装箱运输的经济性,有效规避新冠疫情所造成的风险和损失,助力交通运输业绿色低碳发展,提出失效情景下以多式联运经营人利润最大和运输碳排放总量最小为目标的多目标0-1规划模型。模型不仅考虑节点及路径失效的不确定性,还考虑失效后的拥堵及托运人偏好等影响路径选择的因素。采用蒙特卡洛方法(Monte Carlo method,MCM)结合带精英策略的非支配排序遗传算法(elitist non-dominated sorting genetic algorithm,NSGA-Ⅱ)的混合算法(MCM-NSGA-Ⅱ)对模型进行求解,并以武汉到柏林的集装箱运输为例验证模型及算法的有效性。研究结果表明:托运人偏好、失效及失效后的拥堵会对运输方案的利润、碳排放量、时间产生影响,从而改变帕累托最优运输方案。研究可为制定并优化多式联运方案提供决策支持。

基于遗传算法的多设施选址问题研究

文章采用数学建模和遗传算法相结合的方式对考虑货物分配的多设施选址问题进行求解,并且在研究中运用正交实验法优化参数设定,在算法中运用轮盘赌选择法保证染色体的多样性,运用精英保留策略防止优良基因的丢失,最终达到使企业物流以更低成本和更高效率运作的目的,所给出的算例验证了算法在求解上述问题的有效性。

Elitism-based immune genetic algorithm and its application to optimization of complex multi-modal functions

A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.

Adaptive template filter method for image processing based on immune genetic algorithm

To preserve the original signal as much as possible and filter random noises as many as possible in image processing,a threshold optimization-based adaptive template filtering algorithm was proposed.Unlike conventional filters whose template shapes and coefficients were fixed,multi-templates were defined and the right template for each pixel could be matched adaptively based on local image characteristics in the proposed method.The superiority of this method was verified by former results concerning the matching experiment of actual image with the comparison of conventional filtering methods.The adaptive search ability of immune genetic algorithm with the elitist selection and elitist crossover(IGAE) was used to optimize threshold t of the transformation function,and then combined with wavelet transformation to estimate noise variance.Multi-experiments were performed to test the validity of IGAE.The results show that the filtered result of t obtained by IGAE is superior to that of t obtained by other methods,IGAE has a faster convergence speed and a higher computational efficiency compared with the canonical genetic algorithm with the elitism and the immune algorithm with the information entropy and elitism by multi-experiments.

Co-evolutionary cloud-based attribute ensemble multi-agent reduction algorithm

In order to improve the performance of the attribute reduction algorithm to deal with the noisy and uncertain large data, a novel co-evolutionary cloud-based attribute ensemble multi-agent reduction（CCAEMR） algorithm is proposed.First, a co-evolutionary cloud framework is designed under the M apReduce mechanism to divide the entire population into different co-evolutionary subpopulations with a self-adaptive scale. Meanwhile, these subpopulations will share their rewards to accelerate attribute reduction implementation.Secondly, a multi-agent ensemble strategy of co-evolutionary elitist optimization is constructed to ensure that subpopulations can exploit any correlation and interdependency between interacting attribute subsets with reinforcing noise tolerance.Hence, these agents are kept within the stable elitist region to achieve the optimal profit. The experimental results show that the proposed CCAEMR algorithm has better efficiency and feasibility to solve large-scale and uncertain dataset problems with complex noise.

Adaptive multicascade attribute reduction based on quantum-inspired mixed co-evolution

Due to the fact that conventional heuristic attribute reduction algorithms are poor in running efficiency and difficult in accomplishing the co-evolutionary reduction mechanism in the decision table, an adaptive multicascade attribute reduction algorithm based on quantum-inspired mixed co-evolution is proposed. First, a novel and efficient self- adaptive quantum rotation angle strategy is designed to direct the participating populations to mutual adaptive evolution and to accelerate convergence speed. Then, a multicascade model of cooperative and competitive mixed co-evolution is adopted to decompose the evolutionary attribute species into subpopulations according to their historical performance records, which can increase the diversity of subpopulations and select some elitist individuals so as to strengthen the sharing ability of their searching experience. So the global optimization reduction set can be obtained quickly. The experimental results show that, compared with the existing algorithms, the proposed algorithm can achieve a higher performance for attribute reduction, and it can be considered as a more competitive heuristic algorithm on the efficiency and accuracy of minimum attribute reduction.

双精英协同进化遗传算法

针对传统遗传算法早熟收敛和收敛速度慢的问题,提出一种双精英协同进化遗传算法(double elitecoevolutionary genetic algorithm,简称DECGA).该算法借鉴了精英策略和协同进化的思想,选择两个相异的、高适应度的个体(精英个体)作为进化操作的核心,两个精英个体分别按照不同的评价函数来选择个体,组成各自的进化子种群.两个子种群分别采用不同的进化策略,以平衡算法的勘探和搜索能力.理论分析证明,该算法具有全局收敛性.通过对测试函数的实验,其结果表明,该算法能搜索到几乎所有测试函数的最优解,同时能够有效地保持种群的多样性.与已有算法相比,该算法在收敛速度和搜索全局最优解上都有了较大的改进和提高.

一种基于改进遗传算法的神经网络优化算法研究

遗传算法是目前优化搜索算法中应用比较广泛的一种,但基本遗传算法存在收敛速度慢、易于陷入局部最优等缺点。针对上述问题对遗传算法(GA)的选择算子进行改进,在最优保存策略的基础上将每代种群按照适应度由小到大排序,平均分成前中后3段,按照0.6、0.8、1的比例进行选择;从尾段中随机抽取个体来补足种群由于选择操作而损失的个体;既利用了最优保存策略的全局收敛特性同时也保持了种群的多样性;用改进的遗传算法调整神经网络的权值形成了新的改进遗传算法优化BP神经网络(IGA-BP);通过与选择算子为适应度比例选择算子的GA-BP网络进行比较,结果表明算法改进后缩短了收敛时间同时减少了运行误差;最后将该改进算法应用于水泥回转窑的故障诊断中,验证了算法的可行性。

免疫进化算法

研究现有进化算法的优越性与存在不足的基础上,受生物免疫原理的启发,提出了一种新的算法———免疫进化算法.该新算法作为一种全局优化算法,以父代最优个体为基础来产生子代群体,并以最优个体的收敛来代替群体的收敛.在寻优过程中,该新算法还把确定性的变化和随机性的搜索有效地结合在一起,提高了收敛速度.通过马尔可夫链的分析,证明它是全局收敛的.测试表明,免疫进化算法不仅参数设置简单,而且可以提高收敛速度.

复合材料层合板铺层顺序优化遗传算法

采用遗传算法进行复合材料层合板的铺层顺序优化设计.即采用整数编码方式,并对解码进行特殊处理以满足平衡铺层要求;通过父代与子代竞争延长了父代个体寿命,使优良基因有更多机会遗传给下一代;遍历所有分析过的个体信息以减少计算量.算例表明,改进的遗传算法是有效的.

蜜蜂进化型遗传算法

本文提出了一种蜜蜂进化型遗传算法.在该算法中,种群的最优个体作为蜂王与被选的每个个体(雄蜂)以概率进行交叉操作,增强了对种群最优个体所包含信息的开采能力.为了避免算法过早收敛,在代进化过程中引入了一个随机种群,提高了算法的勘探能力.通过将该算法建模为齐次有限M arkov链,证明了它的全局收敛性.实验结果表明,蜜蜂进化型遗传算法是一种提高遗传算法性能的有效改进算法.