一种并行免疫进化策略算法研究

基于克隆选择原理，提出一种自适应并行免疫进化策略．在算法中根据抗体抗原亲和度将初始抗体种群分为两个子群，相应地提出了精英克隆算子和超变异算子．通过精英克隆算子提高算法局部搜索能力，同时利用超变异算子维持种群多样性，通过这两个功能互补算子的并行操作实现种群进化．仿真表明，自适应并行免疫进化策略搜索效率高，能有效抑制早熟收敛现象，可用于解决复杂机器学习问题．     

一种维持种群多样性的多目标差分演化算法

差分演化算法是一种简单而有效的全局优化算法。本文将差分演化算法用于求解多目标优化问题,给出了一种维持种群多样性的多目标差分演化算法。该算法采用正交设计法初始化种群,改进差分演化算子,从而有利于维持种群多样性,提高演化算法的搜索性能。初步实验表明,新算法能有效地求解多目标优化问题。     

求解TSP问题的一种改进的+进制MIMIC算法

十进制MIMIC算法是基于MIMIC二进制编码算法思想的可用来求解TSP的离散分布估计算法。着重考虑该算法在较大规模TSP问题上的算法缺陷,对其编码方式和概率模型进行了改进,提出了新的个体生成策略,在初始化种群阶段使用了贪心算法,在进化过程中引入了杂交算子、变异算子、映射算子、优化算子等演化算子,采用了动态调整方法来确定优势群体的规模。以上改进使得算法在小种群解大规模TSP问题的情况下仍可保持种群的多样性。实验结果表明,改进算法在求解规模、求解质量和寻优速度上都有明显提高。     

基于自适应变异算子的差分进化算法

针对差分演化算法易于早熟、收敛速度慢和收敛精度低等问题,提出一种基于自适应变异算子的差分进化算法。给出个体向量粒子及维度层定义,并提出了基于维度层加权的异维维度选择策略,首次将加权异维学习策略引入差分演化算法中,有效地提高了种群的多样性;根据种群聚集度的思想,提出一种基于种群聚集度自适应的变异算子,该算子能依据种群个体当前的种群聚集度自适应地调整DE/best/1变异算子和加权异维学习变异算子的变异权重,加快算法收敛速度、提高其收敛精度。通过在20个典型的测试函数上进行测试,与7种具有代表性的算法相比,结果表明提出的算法在求解精度和收敛速度上具有很大优势,并显示出了非常好的鲁棒性。     

求解旅行商问题的二阶段演化算法

对Inver-over算子进行了改进,提出了1st-Inver-over算子和2nd-Inver-over算子,实现了求解TSP问题的基于改进Inver-over算子的二阶段演化算法（Two-stage Inver-over EA）。在算法前期,只采用1st-Inver-over算子来保证算法的收敛速度;在算法后期,根据种群的多样性自适应地选取1st-Inver-over算子和2nd-Inver-over算子来协调算法的收敛速度和种群的多样性。在TSPLIB（Traveling Salesman Problem Library）中的典型实例上的实验结果表明,Two-stage Inver-over EA比经典的GT算法具有更好的收敛性和搜索效率。     

改进NSGA算法求解多目标柔性车间作业调度问题

在多目标柔性车间作业调度问题的研究中,求解算法与多目标处理至关重要。因此,基于非支配排序遗传算法提出了改进遗传算法求解该问题,设计了相应的矩阵编码、交叉算子,改进了非劣前沿分级方法,并提出了基于Pareto等级的自适应变异算子以及精英保留策略。实例计算表明,该算法可以利用传统遗传算法全局搜索能力的同时可以防止早熟现象的发生。改进非劣前沿分级方法可以快速得到Pareto最优解集,进一步减小了计算复杂度,而且可以根据种群的多样性改变变异概率,有利于保持种群多样性、发掘潜力个体。     

一种改进的混沌伊藤算法求解车辆配送问题

为了提高基本伊藤算法搜索最优解的效率,在状态转移策略中引入C-W节约法,并根据伊藤算法迭代的特性改进了距离启发因子和路径权重的更新规则,同时在寻优过程中对各个因子的权值系数作线性调整,保证了初期种群的多样性和后期遍历寻优的能力。根据种群中粒子的适应度设计了针对波动算子和漂移算子的自适应扰动策略,以避免算法在迭代过程中出现搜索停滞的现象。构造了4个邻域搜索算子,并在此基础上提出了基于幂函数载波的混沌局部优化方法,该方法提高了局部搜索的充分性和遍历性。仿真结果证明了所提算法的有效性。     

基于演化策略算法的作业车间调度优化

提出一种基于工件操作次序的二维实数编码方法,采用演化策略算法求解作业车间调度问题。设计一种基于三点交叉互换的重组算子用于生成子代个体,并采用个体编码基因随机重新生成的方法设计变异算子。实验结果证明,演化策略算法能有效优化作业车间调度问题,与遗传算法和粒子群优化算法相比,其优化性能更好,并且基于三点交叉互换重组算子的演化策略算法的性能好于基于两点交叉和基于四点交叉互换重组算子的演化策略算法。     

改进遗传算法在求解TSP问题上的应用研究

遗传算法是一种比较成熟的智能算法,一般通过改进遗传算法的算子达到提高算法性能的目的。提出一种改进的遗传算法,遗传算子是基于近邻选择策略设计的,另外还对评估函数、种群多样性以及保留精英算子等方面对遗传算法进行了改进,并将其应用到旅行商问题的求解上,实验结果表明提出的算法是有效的。     

精英策略个体优势遗传算法研究

针对遗传算法存在的局部搜索能力差、早熟收敛和进化后期收敛速度慢的问题,提出了一种改进精英策略的个体优势遗传算法（Individual Advantages Genetic Algorithm,IAGA）。IAGA通过在精英子种群更新中不断增加精英个体数量和多样性,在保持算法全局收敛性的同时,增强算法在最优解区域的局部搜索能力。引入半粒子群变异算子,提高了算法前期向全局最优解靠拢的速度;引入个体优势算子,提高种群优势个体的多样性,有效改善了进化后期收敛速度慢的问题;与已有同类算法相比,平衡了收敛速度和全局收敛性之间矛盾的同时,进一步提高了收敛速度和精度。     

基于邻域差分和协方差信息的单目标进化算法

复杂的单目标优化问题是进化计算领域的一个研究热点问题.已有差分进化和协方差进化被认为是处理该问题的较有效的方法,其中差分信息类似于梯度可以有效的指导算法朝着最优解方向搜索,而协方差则是基于统计的方式来生成较优的子代种群.本文引入了协方差信息对差分算子进行改进,提出了一种基于邻域差分和协方差信息的进化算法（DEA/NC）来处理复杂的单目标优化问题.算法对现有差分算子中通常采用的随机选点或结合当前最优解进行差分的方式进行了分析,当随机选择的差分个体间的差异较大时,差分信息不能作为一种局部的梯度信息来指导算法的搜索;而结合最优解的差分信息又会使得种群朝着当前最优解的方向搜索,导致种群快速的陷入局部最优.基于此,本文采用了邻域差分的方式来提高差分算子的有效性,同时避免种群的多样性丢失.另外,引入了协方差来度量个体变量间的相关度,并利用相关度来优化差分算子.最后,算法对cec2014中的单目标优化问题进行了测试,并将实验结果与已有的较好的差分进化算法进行了比较,实验结果表明了本算法的有效性.     

A diversity control mechanism in many objective optimizations

Multi-objective evolutionary optimization algorithms are among the best optimizers for solving problems in control systems, engineering and industrial planning. The performance of these algorithms degrades severely due to the loss of selection pressure exerted by the Pareto dominance relation which will cause the algorithm to act randomly. Various recent methods tried to provide more selection pressure but this would cause the population to converge to a specific region which is not desirable. Diversity reduction in high dimensional problems which decreases the capabilities of these approaches is a decisive factor in the overall performance of these algorithms. The novelty of this paper is to propose a new diversity measure and a diversity control mechanism which can be used in combination to remedy the mentioned problem. This measure is based on shortest Hamiltonian path for capturing an order of the population in any dimension. In order to control the diversity of population, we designed an adaptive framework which adjusts the selection operator according to diversity variation in the population using different diversity measures as well as our proposed one. This study incorporates the proposed framework in MOEA/D, an efficient widely used evolutionary algorithm. The obtained results validate the motivation on the basis of diversity and performance measures in comparison with the state-of-the-art algorithms and demonstrate the applicability of our algorithm/method in handling many-objective problems. Moreover, an extensive comparison with several diversity measure algorithms reveals the competitiveness of our proposed measure.     

Biogeography-based optimization with improved migration operator and self-adaptive clear duplicate operator

Biogeography-based optimization (BBO) is a new emerging population-based algorithm that has been shown to be competitive with other evolutionary algorithms. However, there are some insufficiencies in solving complex problems, such as poor population diversity and slow convergence speed in the later stage. To overcome these shortcomings, we propose an improved BBO (IBBO) algorithm integrating a new improved migration operator, Gaussian mutation operator, and self-adaptive clear duplicate operator. The improved migration operator simultaneously adopts more information from other habitats, maintains population diversity, and preserves exploitation ability. The self-adaptive clear duplicate operator can clear duplicate or almost identical habitats, while also preserving population diversity through a self-adaptation threshold within the evolution process. Simulation results and comparisons from the experimental tests conducted on 23 benchmark functions show that IBBO achieves excellent performance in solving complex problems compared with other variants of the BBO algorithm and other evolutionary algorithms. The performance of the improved migration operator is also discussed.     

改进的进化算法解最短路问题

最短路问题是组合优化中的经典问题之一,对其设计有效的算法具有广泛的应用价值和重要的理论意义．为了减少对初始种群选取的限制,扩大种群的多样性,本文提出了一种新的杂交方式．根据一对染色体中不同位相同基因对的数目,设计了分类杂交．这种杂交不仅增加了种群的多样性,还避免了不可行解的出现．与杂交算子相对应设计了具有局部搜索功能的收缩—扩张式变异算子,使得本算法效率有了极大提高,并在理论上证明该算法以概率1收敛到全局最优解．最后的数值试验也表明此算法是十分有效的．     

多点交叉学习组织进化算法

为了提高组织进化算法(organizational evolutionary algorithm,简称OEA)在高维多模函数全局优化中陷入局部极值,分析了OEA 算法早熟收敛的原因,提出了多点交叉学习组织进化算法(mOEA).设计了一个多个组织的领导交叉学习策略来提高组织领导种群多样性,避免早熟收敛;结合社会群体认知和学习的习性,改进OEA 中的吞并算子,使得同一组织内的个体成员有的在其领导周围爬山运动,有的在搜索范围内随机变异,既提高成员群体的适应度值,又增强成员群体的多样性,不易陷入局部极值.与OEA     

一种基于最大最小策略和非均匀变异的萤火虫算法

针对多目标萤火虫算法勘探能力弱、求解精度差的问题,本文提出了一种基于最大最小策略和非均匀变异的萤火虫算法(HVFA-M).该算法首先引入Maximin策略,实现对外部档案的动态调整和对精英解的随机选择;其次,精英解结合当前最好解共同引导萤火虫进行全局搜索以扩大算法的搜索范围,提高算法的勘探能力,从而增加找寻全局最优解的...     

一种新的免疫进化算法在函数优化中的应用

针对克隆选择算法在求解高维函数优化问题时易陷入局部最优以及收敛速度较慢的弱点,本文基于生物免疫系统内部学习优化机制以及进化算法,提出了一种新的免疫进化算法,它包括正交交叉、单形交叉、克隆、多极变异和选择。新算法将进化计算的思想融入到克隆选择中,提出了一种新的变异算子,在保证种群多样性的同时提高了算法的全全局寻优能力。理论分析证明了算法的收敛性,并将算法应用于不同的测试函数进行仿真实验。结果表明,该算法是有效的。     

基于种群相似度的自适应改进蚁群算法及应用

针对蚁群算法收敛速度较慢、易陷入局部最优的问题,提出了一种基于种群相似度的自适应改进蚁群算法。利用种群相似度对种群内个体的多样性进行度量并根据优化过程中种群相似度的变化情况自适应地调整蚁群算法的参数和信息素更新策略,提升算法的优化性能。该算法用于求解旅行商问题（Traveling Salesman Problem,TSP）问题,并与经典的蚁群系统（Ant Colony System,ACS）及最大最小蚂蚁系统（MAX-MIN Ant System,MMAS）算法进行比较,实验结果表明改进算法在解的质量与算法稳定性方面得到显著提升,有效地平衡了种群多样性与收敛速度之间的矛盾。     

Chaos-based multi-objective immune algorithm with a fine-grained selection mechanism

In this paper, we propose a chaos-based multi-objective immune algorithm (CMIA) with a fine-grained selection mechanism based on the clonal selection principle. Taking advantage of the ergodic and stochastic properties of chaotic sequence, a novel mutation operator, named as chaos-based mutation (CM) operator, is proposed. Moreover, the information of diversity estimation is also adopted in the CM operator for nondominated solutions to adjust mutation steps adaptively, which encourages searching less-crowded regions with relative large step sizes. When comparing with polynomial mutation operator that is used in many state-of-the-art multi-objective optimization evolutionary algorithms, simulations show that it is effective to enhance the search performance. On the other hand, in order to increase the population diversity, a fine-grained selection mechanism is proposed in this paper, which seems to be remarkably effective in two-objective benchmark functions. When comparing with two state-of-the-art multi-objective evolutionary algorithms (NSGA-II and SPEA-2) and a new multi-objective immune algorithm (NNIA), simulation results of CMIA indicate the effectiveness of the fine-grained selection mechanism and the remarkable performance in finding the true Pareto-optimal front, especially on some benchmark functions with many local Pareto-optimal fronts.     

Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems.

This paper presents a novel evolutionary algorithm (EA) for constrained optimization problems, i.e., the hybrid constrained optimization EA (HCOEA). This algorithm effectively combines multiobjective optimization with global and local search models. In performing the global search, a niching genetic algorithm based on tournament selection is proposed. Also, HCOEA has adopted a parallel local search operator that implements a clustering partition of the population and multiparent crossover to generate the offspring population. Then, nondominated individuals in the offspring population are used to replace the dominated individuals in the parent population. Meanwhile, the best infeasible individual replacement scheme is devised for the purpose of rapidly guiding the population toward the feasible region of the search space. During the evolutionary process, the global search model effectively promotes high population diversity, and the local search model remarkably accelerates the convergence speed. HCOEA is tested on 13 well-known benchmark functions, and the experimental results suggest that it is more robust and efficient than other state-of-the-art algorithms from the literature in terms of the selected performance metrics, such as the best, median, mean, and worst objective function values and the standard deviations.