基于自适应粒子群优化算法的节点定位研究

针对粒子群优化定位算法易陷入局部极值的缺点,提出了一种基于自适应粒子群优化算法的无线传感器节点定位方法。该方法在迭代前期ω取较大值实现快速收敛到最优解附近,后期取较小值求高精度解。在适应度值越大时全局搜索能力越强,加快向全局最优位置的聚集速度;适应度值越小局部搜索能力越强,可得到高精度的解,并通过对全局最优位置进行自适应变异操作,保证算法能跳出当前的搜索区域。仿真结果表明:与常用的极大似然估计对比,该算法具有收敛快、能耗小、精度高和稳定性好的优点,适合应用在无线传感器网络的定位中。     

一种应用于换热网络综合的阻尼优化方法

针对启发式方法优化换热网络在优化后期易陷入局部极值的问题,提出一种阻尼优化方法,即通过引入延缓概率的概念,以一定的概率不接受费用下降的结构,延缓该结构形成固定匹配,避免因连续变量优化过快导致整型变量优化不充分而陷入局部最优。通过探讨不同阶段的优化特点及优化陷入局部极值的成因,进而提出分阶段延缓策略,合理调控延缓条件以及延缓概率的取值,从而提高算法的全局搜索能力。最后采用四个不同规模的算例进行验证,结果表明该方法可有效地跳出局部最优解,促进结构的进一步优化。     

全局群搜索优化算法及其在汽油调合中的应用

汽油调合配比生产优化是一种非线性约束的多峰优化问题。针对一般群智能优化算法在解决此类优化中易陷于局部最优解,提出了一种改进的群搜索优化算法--全局群搜索优化算法（GGSO）。该算法采用混沌机制初始化粒子在解空间内均匀分布;在算法前期,保留GSO的追随者进化策略,以保证算法的收敛速度。在算法后期,对追随者引入速度更新和个体最优,以保证算法的收敛精度;在粒子陷入局部极值时,对追随者和游荡者引入一种新的交叉、变异机制和自适应混沌扰动机制,以保证粒子跳出局部极值,提高算法全局寻优性能。分别用4个标准测试函数对优化算法进行测试,结果表明：GGSO算法与标准GSO、线性递减惯性权重粒子群算法（LDWPSO）比较,收敛速度和全局寻优性能有明显优势。汽油在线调合优化实例应用表明：该算法有较快的收敛速度,能够较准确地寻得全局最优。     

化学反应优化算法改进研究

针对化学反应优化算法冗余迭代、收敛速度慢的问题，提出了改进化学反应优化算法，该算法通过混合遗传算法，加入选择算子实现个体选择与种群精英保留，减少冗余迭代；通过运用高斯扰动增加邻域搜索活力避免陷入局部最优，此外，该算法通过并行进行不同参数下的多种群迭代进一步提高了算法的运行效率。实验结果表明，改进后的算法相较于基本化学反应优化算法不仅保留了强搜索能力，同时显著提高了收敛速度。     

应用于换热网络优化的并行双层RWCE算法

换热网络规模越大,其解空间内极值点呈指数性增长,优化时不仅要求算法具有强大的全局寻优能力,局部解空间的高精度搜索也不可或缺。鉴于强制进化随机游走算法(RWCE)优化换热网络时难以兼顾局部搜索能力、易导致遗漏最优解的现象,同时为增大算法优化大规模换热网络的种群数量,本文将精细搜索和并行计算相结合提出了并行双层RWCE算法。算法基于多核并行技术,通过并行线程分配建立基础层和精细层,基础层在并行计算技术加持下,算法全局搜索能力大幅提升,精细层将基础层当前最优解实时精细搜索,避免了原算法差解代替优解现象。最后通过两个算例进行验证,结果表明并行双层RWCE算法不仅具有更强的全局搜索能力,且兼具高精度的局部搜索能力,在优化进程中有效保护了最优解。     

A hybrid algorithm based on extremal optimization with adaptive lévy mutation and information fashion algorithm and its applications

提出了一种新的基于自适应lévy变异的极值动力学和信息融合搜索的混合算法。新算法将全局搜索和局部搜索机制有机地结合起来,在全局搜索过程中,信息融合搜索算法(IFA)作为一种群智能进化算法,能够快速地逼近近似最优解;在局部搜索过程中,通过选择近似解的最差组元进行自适应lévy变异,利用极值动力学算法（EO)强大的局部搜索能力,协助IFA跳出局部极值点。将其运用于超临界水氧化去除率神经网络软测量建模,实验结果表明了方法的有效性和实用性。     

基于邻域-克隆选择学习算法的分馏装置负荷优化

在免疫克隆选择和人工免疫网络算法基础上,采用了Agent的思想,提出了一种邻域-克隆选择学习全局优化算法(N-Clonalg)。不同于其他人工免疫算法,N-Clonalg定义了网格化的邻域操作环境,其主要搜索算子有N-克隆选择、N-竞争和自学习算子,能有机结合全局与局部搜索,多峰测试函数表明能较好地克服克隆选择算法(Clonalg)的早熟及人工免疫网络算法(Opt-aiNet)收敛速度慢问题。分馏装置负荷优化实例应用表明,算法具有较好的最优解搜索性能,能较好地实现化工中的寻优问题。     

改进的教学优化算法及其应用

针对教学优化算法（TLBO）收敛速度慢,容易陷入局部最优的问题,本文提出了一种改进的方法。算法的改进主要在两方面：一是对教学因子（TF）进行自适应调整,使TF随算法迭代减小,这样算法在搜索前期采用全局搜索,搜索空间快速收敛于最优解附近,提高搜索速度,搜索后期采用局部精细搜索以获得高精度的解。二是引入信任权重,对学生已获得的知识采取部分信任的策略,避免对已获取知识的过分信任,增加学生个体与教师及学生之间的信息共享,利于算法跳出局部最优。算法在8个标准测试函数上应用,仿真结果表明改进的算法有更快的收敛速度并且能够跳出局部最优。最后将改进的算法应用到乙烯裂解炉裂解运行效益优化中,显著提高了裂解炉的效益。     

一种新的多种群竞争粒子群优化算法及高密度聚乙烯装置操作优化

利用模糊C均值聚类对种群自适应划分,提出一种基于模糊C均值聚类的多群竞争粒子群优化算法。根据种群规模选择不同的寻优策略,规模大者采用标准粒子群算法寻优,规模小者在最优解邻域随机搜索,增大跳出局部最优概率。在每个聚类内部,个体相互通信,通过竞争学习分别找到各聚类种群的适应值,按照不同聚类的适应值排序,再把适应值小者向其邻近的适应值大者融合,通过种群间的竞争保证种群向最优解搜索。该算法避免陷入局部最优,提高了算法的全局搜索能力,通过标准函数验证了算法的有效性。最后,把提出的优化算法应用到高密度聚乙烯装置（HDPE）乙烯单体总消耗的优化操作,实际应用效果良好。     

融合交叉变异和混沌的新型混合粒子群算法

针对粒子群算法在多峰函数优化中极易陷入局部最优的问题,提出一种融合交叉、变异以及混沌的新型混合粒子群算法。该算法采用混沌初始化所有粒子位置和速度,保证初始粒子在解空间均匀分布;在每代进化过程中引入交叉操作增加种群的多样性;并且在算法后期,粒子陷入局部极值时,采用一种新的自适应混沌扰动机制和变异机制,以确保粒子跳出局部最优位置。选用4个标准测试函数对所提出的算法进行对比仿真研究,结果表明,该算法具有较快的收敛速度、有效的全局寻优能力。     

一种改进的混合遗传算法及在化工过程动态优化中的应用(英文)

The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. Genetic algorithm (GA) has been proved to be a feasible method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm (HIGA) for solving dynamic optimization problems is proposed to overcome these defects. Simplex method (SM) is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Gaussian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.     

基于新策略粒子群算法优化换热网络

换热网络综合优化是过程系统中最广泛研究的方向。尽管如此,MINLP的复杂性给粒子群算法的应用提供了广泛的空间。首先,提出两种不同机理的局部搜索策略来完善粒子群算法作为启发式算法局部搜索能力不强和精度不高的问题,使算法能更有利地接近全局最优的局部极值。其次,对含固定投资费用的算例,采用费用计算替换公式的策略,来避免迭代计算初期面积较小时因为固定投资费用权重较大而使算法陷入局部最优问题。最后用4个四股流算例分别从不同侧面说明以上两种策略的有效性,并都得到了该算例目前为止最好的局部极值。     

Illumination correction model with sine-cosine algorithm to optimize gray wolf population for least-squares support vector regression

The color of an object appears different from its true color when illuminated with light sources of different hues. To solve this problem, this article proposes a combination algorithm (SCA-GWO-LSSVR) based on the sine-cosine algorithm (SCA) and the gray wolf optimization (GWO) algorithm to optimize the regression prediction model of the least-squares support vector regression (LSSVR) algorithm. The performance of the traditional LSSVR is significantly affected by the penalty parameter (gamma) and the sig2 kernel function parameter. The proposed method uses the improved GWO algorithm to search the population to find the best LSSVR parameter solution. The proposed algorithm uses the SCA to create multiple random candidate solutions in population initialization to avoid blind initialization of the GWO algorithm. In the process of iterative optimization, the SCA is infiltrated, and its sine-cosine wave mathematical model is used to quickly identify the best outward or inward position of the gray wolf. Finally, the LSSVR combines the optimal sig2 kernel function parameters and penalty parameters (gamma) to obtain a highly versatile illumination correction model. The experimental results show that the fitting accuracy of the proposed method reaches 86.8%, which is 5% higher than that of the LSSVR algorithm alone.     

基于活跃约束条件辅助目标的进化算法求解油品调合问题

根据油品调合问题的特点,提出了一种基于活跃约束条件辅助目标的求解约束优化问题的新方法。该方法根据进化算法种群中的可行解和不可行解共同辨识约束优化问题的活跃约束条件。然后,通过增加活跃约束条件辅助目标的方法将单目标约束优化问题转换为多目标约束优化问题进行求解。通过该方法,相应的进化算法可以利用油品调合问题的活跃约束条件信息,从而达到提高进化算法求解油品调合问题的搜索效率和避免局部最优解的目的。最后,通过仿真研究证实了该方法的有效性。     

A novel search framework for multi‐stage process scheduling with tight due dates

This article improves the original genetic algorithm developed by He and Hui (Chem Eng Sci. 2007; 62:1504–1527) and proposes a novel global search framework (GSF) for the large‐size multi‐stage process scheduling problems. This work first constructs a comprehensive set of position selection rules according to the impact factors analysis presented by He and Hui (in this publication in 2007), and then selects suitable rules for schedule synthesis. In coping with infeasibility emerging during the search, a penalty function is adopted to force the algorithm to approach the feasible solutions. The large‐size problems with tight due dates are challenging to the current solution techniques. Inspired by the gradient used in numerical analysis, we treat the deviation existing among the computational tests of the algorithm as evolutionary gradient. Based on this concept, a GSF is laid out to fully utilize the search ability of the current algorithm. Numerical experiments indicate that the proposed search framework solves such problems with satisfactory solutions. © 2010 American Institute of Chemical Engineers AIChE J, 2010     

RWCE优化换热网络的不可行解影响分析及强化策略

换热网络优化问题常以外罚函数法处理约束,赋予违反约束的不可行解较大的罚值。强制进化随机游走算法(RWCE)优化换热网络时,其非贪婪搜索机制使不可行解以一定概率被保留,从而改变全局寻优过程。本文首先分析不可行解对优化进程的影响,揭示偏移量较小的不可行解对结构进化的促进作用;然后提出差解概率动态调整策略,合理利用不可行解的正面作用,强化结构进化能力;最后,鉴于上述优化结果中偏移量较小的不可行解居多,提出一种可行化策略,通过分段罚指数和双种群优化技术促使过程中有潜力的不可行解快速返回可行域,并提升优化质量。将结合两条强化策略的改进算法应用于16股流与15股流算例,优化结果较文献最优解分别节省了0.35%、0.48%,表明改进后的算法较原算法全局搜索能力得到了显著提升。     

一种大步长激励的结构进化策略应用于换热网络优化

强制进化随机游走算法（random walk algorithm with compulsive evolution,RWCE）具有进化策略简单、控制参数少、全局搜索能力强等特点,是换热网络优化的一种有效方法。鉴于步长对RWCE算法跳出局部最优解有重要的影响,针对RWCE算法的步长分布进行研究,通过分析大步长在局部极值跳出中的作用机理,建立了一种全新的步长组合生成方法,并据此提出了一种大步长激励的结构进化策略。最后,通过3个算例验证了结构进化策略的有效性,与文献结果进行对比,结果表明大步长激励的结构进化策略提高了算法的全局搜索能力。     

An improved genetic algorithm based on a novel selection strategy for nonlinear programming problems

Genetic algorithm is a heuristic population-based search method that incorporates three primary operators: crossover, mutation and selection. Selection operator plays a crucial role in finding optimal solution for constrained optimization problems. In this paper, an improved genetic algorithm (IGA) based on a novel selection strategy is presented to handle nonlinear programming problems. Each individual in selection process is represented as a three-dimensional feature vector composed of objective function value, the degree of constraints violations and the number of constraints violations. We can distinguish excellent individuals through two indices according to Pareto partial order. Additionally, IGA incorporates a local search (LS) process into selection operation so as to find feasible solutions located in neighboring areas of some infeasible solutions. Experimental results over a set of benchmark problems demonstrate that proposed IGA has better robustness, effectiveness and stableness than other algorithm reported in literature.     

Design of memetic algorithms for the efficient optimization of chemical process synthesis problems with structural restrictions

In Urselmann et al., 2011a, Urselmann et al., 2011b we presented a memetic algorithm (MA) for the design optimization of reactive distillation columns. The MA is a combination of a problem-specific evolutionary algorithm (EA) that optimizes the design variables and a mathematical programming (MP) method that solves the continuous sub-problems with fixed discrete decisions which are proposed by the EA to local optimality. In comparison to the usual superstructure formulation, the search space of the MA is significantly reduced without excluding feasible solutions. The algorithm computes many different local optima and can handle structural restrictions and discontinuous cost functions. In this contribution, a systematic procedure to modify the MA to solve more complex design problems is described and demonstrated using the example of a reactive distillation column with an optional side- or pre-reactor with structural restrictions on the number of streams. New concepts to handle connected and optional unit operations are proposed.     

TS法的改进及其在求解化工优化问题中的应用

TS算法属于现代优化算法,是局部领域搜索法的推广,常用于求解组合优化问题.利用TS法搜索过程的有向性和能够跳离局部最优解的特点,对其进行了改造,以适应求解连续变量化工优化问题.首先,根据化工优化问题变量的特性,提出了一种简便的邻域映射方案,并改进了迭代过程中自适应因子的下降函数;进一步分析对比了禁忌步数、自适应因子和初始解等参数对于优化结果的影响.然后通过算例和换热网络优化问题的求解,表明改造后的TS法在求解连续变量化工优化问题中的有效性,及其在化工优化领域的发展价值.