Application of memetic algorithm in modelling discrete-time multivariable dynamics systems

Evolutionary algorithm (EA) such as genetic algorithm (GA) has demonstrated to be an effective method for identification of single-input–single-output (SISO) system. However, for multivariable systems, increasing the orders and the non-linear degrees of the model will result in excessively complex model and the identification procedure for the systems is more often difficult because couplings between inputs and outputs. There are more possible structures to choose from and more parameters are required to obtain a good fit. In this work, a new model structure selection in system identification problems based on a modified GA with an element of local search known as memetic algorithm (MA) is adopted. This paper describes the procedure and investigates the performance and the effectiveness of MA based on a few case studies. The results indicate that the proposed algorithm is able to select the model structure of a system successfully. A comparison of MA with other algorithms such as GAs demonstrates that MA is capable of producing adequate and parsimonious models effectively.     

A hybrid random-key genetic algorithm to minimize weighted number of late deliveries for a single machine

This paper presents a memetic algorithm (MA) to minimize the total weighted number of late jobs (or deliveries) on a single machine. The proposed MA combines a genetic algorithm (GA) with a neighborhood search. The performance of the proposed algorithm is compared with four heuristics from the literature, namely the early due date (EDD), the weighted shortest processing time (WSPT), the forward algorithm (FA), and the weighted forward algorithm (WFA), against 10 benchmark problems and three real-world problems. The results suggest that the MA outperformed the EDD, WSPT, FA, and WFA on the benchmark problems and performed as good as WFA on two of the three real-world problems and outperformed WFA on one real-world problem. The EDD performed the worst among the five solution approaches.     

一种结构损伤评估的两阶段法研究

结构损伤会引起结构动力特性的改变，这种改变可以用模态参数和结构参数表示。文中将柔度投影法和遗传算法相结合，提出一种基于不完整模态数据的结构损伤定位和定量评估的两阶段法。第一阶段，用柔度投影法进行损伤定位，并进行深入分析。第二阶段，用遗传算法确定损伤程度。文中提出一种用于遗传搜索的新的目标函数形式。最后，用一六跨平面桁架桥模型进行数值模拟，验证文中所提方法的有效性。     

基于混合遗传算法的桥梁颤振导数识别

提出一种基于混合遗传算法识别桥梁颤振导数的方法,该混合遗传算法将模拟退火算法与遗传算法相结合,充分利用遗传算法的并行运算机制以及模拟退火算法的强局部搜索机制,具有较强的鲁棒性和全局收敛性,从而保证识别的精度。该方法通过对自由振动时程曲线进行时域拟合,识别出自由振动表达式中的各参数,进而确定系统的等效刚度矩阵和等效阻尼矩阵,并同时得到8个颤振导数。数值仿真算例表明该方法的可靠性,风洞试验表明该方法有效、可行。     

一种基于改进遗传算法的车间调度问题研究

作业车间调度是一类求解较困难的组合优化问题,在考虑遗传算法早熟收敛问题结合模拟退火算法局部最优时能概率性跳出的特性,该特性最终使算法能够趋于全局最优。在此基础上,将遗传算法和模拟退火算法相结合,提出了一种基于遗传和模拟退火的混合算法,该算法将模拟退火算法赋予搜索过程一种时变性融入其中,具有明显的概率跳跃性。同时。通过选取Brandimarte基准问题和经典的Benchmarks基准问题进行分析,并应用实例对该算法进行了仿真研究。该结果表明,通过模拟退火算法与遗产算法相集合,可以使计算的收敛精度明显提高,是行之有效的,与传统的算法相比较,有较明显的优越性。     

A multi-objective genetic algorithm for scheduling in flow shops to minimize the makespan and total flow time of jobs

In this paper the problem of permutation flow shop scheduling with the objectives of minimizing the makespan and total flow time of jobs is considered. A Pareto-ranking based multi-objective genetic algorithm, called a Pareto genetic algorithm (GA) with an archive of non-dominated solutions subjected to a local search (PGA-ALS) is proposed. The proposed algorithm makes use of the principle of non-dominated sorting, coupled with the use of a metric for crowding distance being used as a secondary criterion. This approach is intended to alleviate the problem of genetic drift in GA methodology. In addition, the proposed genetic algorithm maintains an archive of non-dominated solutions that are being updated and improved through the implementation of local search techniques at the end of every generation. A relative evaluation of the proposed genetic algorithm and the existing best multi-objective algorithms for flow shop scheduling is carried by considering the benchmark flow shop scheduling problems. The non-dominated sets obtained from each of the existing algorithms and the proposed PGA-ALS algorithm are compared, and subsequently combined to obtain a net non-dominated front. It is found that most of the solutions in the net non-dominated front are yielded by the proposed PGA-ALS.     

离散变量优化设计的改进斐波那契遗传算法

根据工程实际，充分考虑规范规定的约束条件和各项技术标准要求，建立离散变量结构优化模型。针对遗传算法在迭代过程中经常出现未成熟收敛、振荡、随机性太大和迭代过程缓慢等缺点，提出一种新的遗传算子——转基因算子，用于对遗传算法的改进；提出一种离散变量结构优化设计的斐波那契算法，并与遗传算法结合在一起解决问题。优化设计结果表明，这种改进斐波那契遗传算法的收敛特性得到很好的改善，即发挥了斐波那契算法省时、局部搜索能力强的特点，又发挥了遗传算法全局性好的特点，是有效的工程结构优化设计方法。     

基于模拟退火遗传算法的拆卸路径规划

针对现存拆卸路径规划图论方法中的组合爆炸问题和遗传算法的早熟现象,给出模拟退火和遗传算法相结合的拆卸路径规划方法.在拆卸路径规划模型中,以拆卸效率最优作为优化目标,给出了算法流程.该算法对拆卸路径的全面寻优提供了一种新的思路.通过实例验证该算法的可行性,最后提出了进一步的研究方向.     

基于分等级搜索的可持续进化算法研究

通过分析标准遗传算法在可持续性进化方面的缺陷，提出了HFC模型及其运算流程，井给出了详细的数学描述。HFC算法的分等级搜索方式改进了标准遗传算法的缺陷，是一种能实现可持续性进化的算法模型。HFC模型不仅能确保种群的多样性，而且保证了个体间的公平竞争。实例验证了HFC模型和标准遗传算法在相同问题上的不同效果。     

A hybrid genetic algorithm to minimize makespan for the single batch machine dynamic scheduling problem

This paper considers a single batch machine dynamic scheduling problem, which is readily found in the burn-in operation of semiconductor manufacturing. The batch machine can process several jobs as a batch simultaneously, within the capacity limit of the machine, and the processing time is represented by the longest processing time among all jobs in a batch. For a single batch machine problem with arbitrary job release time, we proposed an improved algorithm (merge-split procedure) to refine the solution obtained by the LPT-BFF heuristic, and two versions of a hybrid genetic algorithm (GA) are introduced in this paper. Each version of the hybrid GA diversifies job sequences using the GA operators in stage 1, forms batches in stage 2, and finally sequence the batches in stage 3. The difference is that merge-split procedures are involved in the second version of the hybrid GA. Computational experiments showed that the hybrid GA would obtain satisfactory average solution quality and the merge-split procedures would be good at reinforcing the solution consistency of the hybrid GA.