基于模拟退火遗传算法的认知无线电决策引擎

如何根据环境变化和用户需求智能调整无线电参数是认知无线电的基本功能.提出了遗传算法和模拟退火相结合的认知无线电参数调整方法,给出了该方法流程,运用多载波系统对算法性能进行了仿真分析.实验结果表明该方法改进了遗传算法中后期的爬山能力,优化得到的参数比单独使用遗传算法优化所得参数具有更高的归一化目标函数值,而且该方法能够在多个目标函数间进行权衡,参数调整结果与当前对目标函数的偏好一致.     

基于粒子群算法的认知无线电参数优化及敏感度分析

认知无线电能根据环境变化和用户需求自适应调整工作参数。现有认知引擎大多采用遗传算法优化参数。但随着认知用户数的增加,遗传算法染色体增多,导致算法收敛时间过长,无法满足实时通信需求。将改进惯性因子的粒子群算法用于认知无线电工作参数的优化,并在不同通信模式下对传输参数进行敏感度分析,以便有选择性地从目标函数中剔除敏感度较低的参数,降低处理复杂度。仿真结果表明,采用粒子群算法的参数优化在收敛速度、搜索效率和算法稳定性等方面均优于遗传算法,仅需较小的进化代数就能找到最优参数解,从而减小了优化时间,满足了认知无线电实时处理的要求。     

基于粒子群算法的认知无线电参数优化及敏感度分析

认知无线电能根据环境变化和用户需求自适应调整工作参数.现有认知引擎大多采用遗传算法优化参数.但随着认知用户数的增加,遗传算法染色体增多,导致算法收敛时间过长,无法满足实时通信需求.将改进惯性因子的粒子群算法用于认知无线电工作参数的优化,并在不同通信模式下对传输参数进行敏感度分析,以便有选择性地从目标函数中剔除敏感度较低...     

多目标遗传算法求解认知无线电SUL维持问题

当某个一级用户重新出现时,二级用户必须马上让出属于此一级用户的频谱,此时就存在着如何维持二级用户链接（Secondary Users’Link,SUL）的问题。基于CORVUS系统的“冗余子信道”模型在SUL原有的N个子信道基础上再使用X个冗余子信道进行二级用户之间的通信。只要传输过程中受干扰的子信道个数少于X,接收端就可以从N+X个子信道中恢复出正确信息来,从而解决了SUL维持的问题。但尚未有一种有效的办法以求解最优的N和X。将SUL维持问题建模为一个多目标优化问题,提出了一种基于多目标遗传算法的SUL维持算法SULEA。SULEA能够根据不同的用户服务需求动态地选择适应度函数来求解最优的N和X,Matlab实验证明了SULEA的正确性和有效性。     

认知无线电网络中认知引擎及其安全问题研究

认知引擎可以根据外在无线环境的变化自适应调整传输参数，优化系统性能，是认知无线电网络的关键智能技术之一。然而．攻击者可以改变要优化的目标函数，阻止认知无线电的自适应操作。分析了认知引擎及其安全性问题，给出了一种防止目标函数攻击的解决思路。     

基于二进制蚁群模拟退火算法的认知引擎

在认知无线电系统中,认知引擎依据通信环境的变化和用户需求动态配置无线电工作参数。针对认知引擎中的智能优化问题,提出一种二进制蚁群模拟退火（BAC&SA）算法用于认知无线电参数优化。该算法在二进制蚁群优化（BACO）算法中引入模拟退火（SA）算法,融合了BACO的快速寻优能力和SA的概率突跳特性,能有效避免BACO容易陷入局部最优解的缺陷。仿真实验结果表明,与遗传算法（GA）和BACO算法相比,基于BAC&SA算法的认知引擎在全局搜索能力和平均适应度等方面具有明显的优势。     

一种有效的面向多目标软硬件划分的遗传算法

软硬件划分是软硬件协同设计的关键技术之一,划分结果对最终的设计方案有非常重要的影响。软硬件划分根据优化目标的数量,可分为单目标划分和多目标划分。多目标划分问题是一个NP-hard问题,一般不存在传统意义上的“最优解”,而是存在一组互不支配的Pareto最优解。遗传算法因其具有并行、群体搜索的特点而非常适于求解多目标优化问题。通过抽象描述将一个实际SOC设计问题转化为多目标软硬件划分问题,采用遗传算法便可获得最优设计方案。为克服过早收敛及加快搜索速度,改进了适应度函数的定义,通过自适应参数调整,加入惩罚函数的适应度定义,提高了进化速度,从而有效地获得了Pareto最优解集。在实际问题的应用中,多目标软硬件划分遗传算法是能有效求取平衡系统成本、硬件面积、功耗和时间特性的最优化方案。     

多目标优化设计在杀螺剂配方研究中的应用

针对某生物杀螺剂制作中多目标约束问题,提出了一种应用Pareto遗传算法来解决问题的优化方法。建立了用于多目标优化的适应度函数,使用排列选择方法将带约束的多目标问题转换为无约束优化问题;并根据计算中的收敛情况引入了适当的移民算子,改善了遗传算法的进化性能,得到了Pareto最优解集,成功地解决了该生物杀螺剂的最优配方问题。     

基于降维扫描方法的自适应多目标遗传算法

为了有效地应用遗传算法解决H2/H∞鲁棒控制系统设计问题,将遗传算法与局部优化方法相结合,提出了基于降维扫描方法的自适应多目标遗传算法（DRSA-MOGA）。通过引入适应度函数标准化方法、基于最优Pareto解集搜索的降维扫描方法和适应度函数自适应调整方法,提高了算法的全局优化性能和局部搜索能力。仿真结果表明,DRSA-MOGA算法在不损失解的均匀度的情况下可以达到很高的逼近度。     

RVM核参数的遗传算法优化方法

核函数的参数严惩影响RVM的综合性能.为求得稀疏解、避免过拟合,提出使用遗传算法针对问题背景自动优化核函数的参数.在适应度函数评判下,种群经过选择、交叉和变异迭代进化,高效率地得到最优解,在定义RVM回归性能综合评判批准Fitness作为适应度函数的基础上,使用Matlab遗传算法工具箱和改进的Tipping程序获取sinc数据最优核函数参数,实验证明遗传算法可以高效准备地优化RVM核参数,特别对于具有较多参数的核函数更具实用性.     

多宇宙并行量子多目标进化算法

提出了一种新的基于量子计算的多目标进化算法,即多宇宙并行量子多目标进化算法。算法中将所有的量子个体按给定的拓扑结构分成多个独立子种群,划分为多个宇宙;采用目标个体均匀分配原则和动态调整旋转角机制对各宇宙量子个体进行演化;宇宙之间采用最佳移民操作来交换信息,设计最优个体保留方案以便各宇宙共享全局信息,提高算法的执行效率。该算法用于多目标0/1背包问题的仿真结果表明：新方法能够找到接近Pareto最优前端的更好的解,同时维持解分布的均匀性。     

Fuzzy preference-based multi-objective optimization method

Multiobjective evolutionary computation is still quite young and there are many open research problems. This paper is an attempt to design a hybridized Multiobjective Evolutionary Optimization Algorithm with fuzzy logic called Fuzzy Preference-Based Multi–Objective Optimization Method (FPMOM). FPMOM as an integrated components of Multiobjective Optimization Technique, Evolutionary Algorithm and Fuzzy Inference System able to search and filter the pareto-optimal and provide a good trade-off solution for the multiobjective problem using fuzzy inference method to choose the user intuitive based specific trade-off requirement. This paper will provide a new insight into the behaviourism of interactive Multiobjective Evolutionary Algorithm optimization problems using fuzzy inference method.     

Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator

Most controllers optimization and design problems are multiobjective in nature, since they normally have several (possibly conflicting) objectives that must be satisfied at the same time. Instead of aiming at finding a single solution, the multiobjective optimization methods try to produce a set of good trade-off solutions from which the decision maker may select one. Several methods have been devised for solving multiobjective optimization problems in control systems field. Traditionally, classical optimization algorithms based on nonlinear programming or optimal control theories are applied to obtain the solution of such problems. The presence of multiple objectives in a problem usually gives rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Recently, Multiobjective Evolutionary Algorithms (MOEAs) have been applied to control systems problems. Compared with mathematical programming, MOEAs are very suitable to solve multiobjective optimization problems, because they deal simultaneously with a set of solutions and find a number of Pareto optimal solutions in a single run of algorithm. Starting from a set of initial solutions, MOEAs use iteratively improving optimization techniques to find the optimal solutions. In every iterative progress, MOEAs favor population-based Pareto dominance as a measure of fitness. In the MOEAs context, the Non-dominated Sorting Genetic Algorithm (NSGA-II) has been successfully applied to solving many multiobjective problems. This paper presents the design and the tuning of two PID (Proportional–Integral–Derivative) controllers through the NSGA-II approach. Simulation numerical results of multivariable PID control and convergence of the NSGA-II is presented and discussed with application in a robotic manipulator of two-degree-of-freedom. The proposed optimization method based on NSGA-II offers an effective way to implement simple but robust solutions providing a good reference tracking performance in closed loop.     

改进的多宇宙并行量子进化算法

通过设计一种新的量子个体更新策略,提出了改进的多宇宙并行量子进化算法,并对算法的收敛性进行了分析探讨,从理论上证明了该算法的有效性,最后将该算法用于多目标0/1背包问题。仿真结果表明：改进方法能够找到接近Pareto最优前端的更好的解,同时维持解分布的均匀性。     

The effects of asymmetric neighborhood assignment in the MOEA/D algorithm

The Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) is a very efficient multiobjective evolutionary algorithm introduced in recent years. This algorithm works by decomposing a multiobjective optimization problem to many scalar optimization problems and by assigning each specimen in the population to a specific subproblem. The MOEA/D algorithm transfers information between specimens assigned to the subproblems using a neighborhood relation.In this paper it is shown that parameter settings commonly used in the literature cause an asymmetric neighbor assignment which in turn affects the selective pressure and consequently causes the population to converge asymmetrically. The paper contains theoretical explanation of how this bias is caused as well as an experimental verification. The described effect is undesirable, because a multiobjective optimizer should not introduce asymmetries not present in the optimization problem. The paper gives some guidelines on how to avoid such artificial asymmetries.     

基于变异算子和邻域值自适应的MOEA/D算法

基于分解的多目标进化算法（MOEA/D）在解决多目标问题时，具有简单有效的特点。但多数MOEA/D采用固定的控制参数，导致全局搜索能力差，难以平衡收敛性和多样性。针对以上问题提出一种基于变异算子和邻域值自适应的多目标优化算法。该算法根据种群中个体适应度值的分散或集中程度进行判断，并据此对变异算子进行自适应的调节，从而增强算法的全局搜索能力；根据进化所处的阶段以及个体适应度值的集中程度，自适应地调节邻域值大小，保证每个个体在不同的进化代数都有一个邻域值大小；在子问题邻域中，统计子问题对应个体的被支配数，通过判断被支配数是否超过设定的上限，来决定是否将Pareto支配关系也作为邻域内判断个体好坏的准则之一。将提出的算法与传统的MOEA/D在标准测试问题上进行对比。实验结果表明，提出的算法求得的解集具有更好的收敛性和多样性，在求解性能上具有一定的优势。     

一种求解鲁棒优化问题的多目标进化方法

鲁棒优化问题（Robust Optimization Problem,ROP）是进化算法（Evolutionary Algorithms,EAs）研究的重要方面之一,对于许多实际工程优化问题,通常需要得到鲁棒最优解。利用多目标优化中的Pareto思想优化ROP的鲁棒性和最优性,将ROP转化为一个两目标的优化问题,一个目标为解的鲁棒性,一个目标为解的最优性。针对ROP与多目标优化的特点,利用动态加权思想,设计一种求解ROP的多目标进化算法。通过测试函数的实验仿真,验证了该方法的有效性。     

Analysis of a Multiobjective Evolutionary Algorithm on the 0–1 knapsack problem

Multiobjective Evolutionary Algorithms (MOEAs) are increasingly being used for effectively solving many real-world problems, and many empirical results are available. However, theoretical analysis is limited to a few simple toy functions. In this work, we select the well-known knapsack problem for the analysis. The multiobjective knapsack problem in its general form is NP-complete. Moreover, the size of the set of Pareto-optimal solutions can grow exponentially with the number of items in the knapsack. Thus, we formalize a (1+ε)$(1+\epsilon )$-approximate set of the knapsack problem and attempt to present a rigorous running time analysis of a MOEA to obtain the formalized set. The algorithm used in the paper is based on a restricted mating pool with a separate archive to store the remaining population; we call the algorithm a Restricted Evolutionary Multiobjective Optimizer (REMO). We also analyze the running time of REMO on a special bi-objective linear function, known as LOTZ (Leading Ones : Trailing Zeros), whose Pareto set is shown to be a subset of the knapsack. An extension of the analysis to the Simple Evolutionary Multiobjective Optimizer (SEMO) is also presented. A strategy based on partitioning of the decision space into fitness layers is used for the analysis.     

Optimization of sonic crystal attenuation properties by <Emphasis Type="Italic">ev-MOGA</Emphasis> multiobjective evolutionary algorithm

This paper shows a promising method for acoustic barrier design using a new acoustic material called Sonic Crystals (SCs). The configuration of these SCs is set as a multiobjective optimization problem which is very difficult to solve with conventional optimization techniques. The paper presents a new parallel implementation of a Multiobjective Evolutionary Algorithm called ev-MOGA (also known as ) and its application in a complex design problem. ev-MOGA algorithm has been designed to converge towards a reduced, but well distributed, representation of the Pareto Front (solution of the multiobjective optimization problem). The algorithm is presented in detail and its most important properties are discussed. To reduce the ev-MOGA computational cost when objective functions are substantial, a basic parallelization has been implemented on a distributed platform. Partially supported by MEC (Spanish Government) and FEDER funds: projects DPI2005-07835, MAT2006-03097 and Generalitat Valenciana (Spain) projects GV06/026, GV/2007/191.     

A problem space genetic algorithm in multiobjective optimization

In this study, a problem space genetic algorithm (PSGA) is used to solve bicriteria tool management and scheduling problems simultaneously in flexible manufacturing systems. The PSGA is used to generate approximately efficient solutions minimizing both the manufacturing cost and total weighted tardiness. This is the first implementation of PSGA to solve a multiobjective optimization problem (MOP). In multiobjective search, the key issues are guiding the search towards the global Pareto-optimal set and maintaining diversity. A new fitness assignment method, which is used in PSGA, is proposed to find a well-diversified, uniformly distributed set of solutions that are close to the global Pareto set. The proposed fitness assignment method is a combination of a nondominated sorting based method which is most commonly used in multiobjective optimization literature and aggregation of objectives method which is popular in the operations research literature. The quality of the Pareto-optimal set is evaluated by using the performance measures developed for multiobjective optimization problems.