Optimal Scheduling Method of Cogeneration System with Heat Storage Device Based on Memetic Algorithm

Electric-heat coupling characteristics of a cogeneration system and the operating mode of fixing electricity with heat are the main reasons for wind abandonment during the heating season in the Three North area.To improve the wind-power absorption capacity and operating economy of the system,the structure of the system is improved by adding a heat storage device and an electric boiler.First,aiming at the minimum operating cost of the system,the optimal scheduling model of the cogeneration system,including a heat storage device and electric boiler,is constructed.Second,according to the characteristics of the problem,a cultural gene algorithm program is compiled to simulate the calculation example.Finally,through the system improvement,the comparison between the conditions before and after and the simulation solutions of similar algorithms prove the effectiveness of the proposed scheme.The simulation results show that adding the heat storage device and electric boiler to the scheduling optimization process not only improves the wind power consumption capacity of the cogeneration system but also reduces the operating cost of the system by significantly reducing the coal consumption of the unit and improving the economy of the system operation.The cultural gene algorithm framework has both the global evolution process of the population and the local search for the characteristics of the problem,which has a better optimization effect on the solution.

Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks

This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input image is the most important and difficult step in the detection of VPLN,a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects(CCO)and hence enriches the solution space with more solution candidates.Due to the combination of the outputs of the three binarization techniques,many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions.The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype.Using any of the previous versions will give moderate results but with very low speed.Hence,a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols.The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques.Also,a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns.Various image samples with a wide range of scale,orientation,and illumination conditions have been experimented with to verify the effect of the new improvements.Encouraging results with 97.55%detection precision have been reported using the recent challenging public Chinese City Parking Dataset(CCPD)outperforming the author of the dataset by 3.05%and the state-of-the-art technique by 1.45%.

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network.This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms.The efficiency of the sensor node is energy bounded,acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors.Network management plays a significant role in wireless sensor networks,which was obsessed with the factors like the reliability of the network,resource management,energy-efficient routing,and scalability of services.The topology of the wireless sensor networks acts dri-ven factor for network efficiency which can be effectively maintained by perform-ing the clustering process effectively.More solutions and clustering algorithms have been offered by various researchers,but the concern of reduced efficiency in the routing process and network management still exists.This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks.The memetic algorithm employs a local searching methodology to mitigate the premature convergence,while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain(i.e.,)best cluster head in the wireless sensor networks.The proposed hybrid algorithm is compared with the state of art clus-tering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time,energy consumption,throughput,etc.

Memetic algorithm for multi-mode resource-constrained project scheduling problems

A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called ＂strategic oscillation＂ to make the search process have a higher probability to visit solutions around a ＂feasible boundary＂. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.

文化基因算法(Memetic Algorithm)研究进展

文化基因算法(memetic algorithm)是Pablo Moscato提出的建立在模拟文化进化基础上的优化算法,它实质上是一种基于种群的全局搜索和基于个体的局部启发式搜索的结合体。文化基因算法的概念被提出后,已被越来越多的研究人员接受和采纳。本文主要介绍了文化基因算法的起源、实现过程,以及在各类优化问题中的应用情况。

A Memetic Algorithm With Competition for the Capacitated Green Vehicle Routing Problem

In this paper, a memetic algorithm with competition(MAC) is proposed to solve the capacitated green vehicle routing problem(CGVRP). Firstly, the permutation array called traveling salesman problem(TSP) route is used to encode the solution, and an effective decoding method to construct the CGVRP route is presented accordingly. Secondly, the k-nearest neighbor(k NN) based initialization is presented to take use of the location information of the customers. Thirdly, according to the characteristics of the CGVRP, the search operators in the variable neighborhood search(VNS) framework and the simulated annealing(SA) strategy are executed on the TSP route for all solutions. Moreover, the customer adjustment operator and the alternative fuel station(AFS) adjustment operator on the CGVRP route are executed for the elite solutions after competition. In addition, the crossover operator is employed to share information among different solutions. The effect of parameter setting is investigated using the Taguchi method of design-ofexperiment to suggest suitable values. Via numerical tests, it demonstrates the effectiveness of both the competitive search and the decoding method. Moreover, extensive comparative results show that the proposed algorithm is more effective and efficient than the existing methods in solving the CGVRP.

Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due time.It is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production systems.Two objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the problem.To obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy algorithm.The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers.Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

Data-Driven Heuristic Assisted Memetic Algorithm for Efficient Inter-Satellite Link Scheduling in the BeiDou Navigation Satellite System

Inter-satellite link(ISL)scheduling is required by the BeiDou Navigation Satellite System(BDS)to guarantee the system ranging and communication performance.In the BDS,a great number of ISL scheduling instances must be addressed every day,which will certainly spend a lot of time via normal metaheuristics and hardly meet the quick-response requirements that often occur in real-world applications.To address the dual requirements of normal and quick-response ISL schedulings,a data-driven heuristic assisted memetic algorithm(DHMA)is proposed in this paper,which includes a high-performance memetic algorithm(MA)and a data-driven heuristic.In normal situations,the high-performance MA that hybridizes parallelism,competition,and evolution strategies is performed for high-quality ISL scheduling solutions over time.When in quick-response situations,the data-driven heuristic is performed to quickly schedule high-probability ISLs according to a prediction model,which is trained from the high-quality MA solutions.The main idea of the DHMA is to address normal and quick-response schedulings separately,while high-quality normal scheduling data are trained for quick-response use.In addition,this paper also presents an easy-to-understand ISL scheduling model and its NP-completeness.A seven-day experimental study with 10080 one-minute ISL scheduling instances shows the efficient performance of the DHMA in addressing the ISL scheduling in normal(in 84 hours)and quick-response(in 0.62 hour)situations,which can well meet the dual scheduling requirements in real-world BDS applications.

Applying memetic algorithm-based clustering to recommender system with high sparsity problem

A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.

A genetic Gaussian process regression model based on memetic algorithm

Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.

Control Strategy for a Quadrotor Based on a Memetic Shuffled Frog Leaping Algorithm

This work presents a memetic Shuffled Frog Leaping Algorithm(SFLA)based tuning approach of an Integral Sliding Mode Controller(ISMC)for a quadrotor type of Unmanned Aerial Vehicles(UAV).Based on the Newton–Euler formalism,a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes.Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law,which are usually selected by repetitive and time-consuming trials-errors based procedures,a constrained optimization problem is formulated for the systematically tuning of these unknown variables.Under time-domain operating constraints,such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation-and swarm intelligence-based algorithms such as the Crow Search Algorithm(CSA),Fractional Particle Swarm Optimization Memetic Algorithm(FPSOMA),Ant Bee Colony(ABC)and Harmony Search Algorithm(HSA).Numerical experiments are carried out for various sets of algorithms’parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization.Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence.Demonstrative results verified that the proposed metaheuristicsbased approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.

Nonlinear Analysis of Structures by Total Potential Optimization Using Metaheuristic Algorithms (TPO/MA)

Structural analysis problems can be formulized as either root finding problems,or optimization problems.The general practice is to choose the first option directly or to convert the second option again to a root finding problem by taking relevant derivatives and equating them to zero.The second alternative is used very randomly as it is and only for some simple demonstrative problems,most probably due to difficulty in solving optimization problems by classical methods.The method called TPO/MA(Total Potential Optimization using Metaheuristic Algorithms)described in this study successfully enables to handle structural problems with optimization formulation.Using metaheuristic algorithms provides additional advantages in dealing with all kinds of constraints.

Understanding Factors of Bias in the Way Information Is Processed Through a Metamemetic and Multilogical Approach

Training in complex thinking is required in fields like computer science and discussing sensitive topics that can easily polarize internet users’ propensities. Multilogicality and Metamemetic reasoning are strongly suggested as an approach to identifying and analyzing factors related to AI Bias and human biases. This approach entails identifying problems and deducting invalid premises, distinguishing them from valid premises or those we are uncertain about. The theme of this paper focuses on four groups of people: curators, developers, businesses, and users (the fourth group being the main focus). This approach offers a new way to apply critical thinking strategies in the context of living in a digital age.

求解多目标问题的Memetic免疫优化算法

将基于Pareto支配关系的局部下山算子和差分算子引入免疫多目标优化算法之中,提出了一种求解多目标问题的Memetic免疫优化算法(Memetic immune algorithm for multiobjective optimization,简称MIAMO).该算法利用种群中抗体在决策空间上的位置关系设计了两种有效的启发式局部搜索策略,提高了免疫多目标优化算法的求解效率.仿真实验结果表明,MIAMO与其他4种有效的多目标优化算法相比,不仅在求得Pareto最优解集的逼近性、均匀性和宽广性上有明显优势,而且算法的收敛速度与免疫多目标优化算法相比明显加快.

基于Gabor小波与Memetic算法的人脸识别方法

提出一种基于Gabor小波与Memetic算法的人脸识别方法MA-Gabor(Memetic Algorithm-Gabor).算法使用一组特定的Gabor小波滤波器对人脸图像重要区域进行针对性的特征提取运算,可在较短处理时间内获得更具区分能力的识别数据.为提升识别性能,MA-Gabor引入Memetic算法用于Gabor小波滤波器组的优化设计.实验结果表明,Memetic算法可获得比传统优化方法更佳的设计效果.通过将优化设计的Gabor小波滤波器组用于人脸图像的特征提取,MA-Gabor算法可取得比现有人脸识别方法更高的识别率.

基于Memetic算法的要地防空优化部署方法

火力单元优化部署问题是网络化防空火控系统的一个重要研究内容.本文将要地防空优化部署作为组合优化问题,优化目标为最大化部署方案对保护要地的防御贡献程度,约束主要考虑了地理条件和火力资源.利用网格离散化思想对防区进行划分,对部署方案、火力覆盖能力、约束条件以及火力覆盖要求等条件进行了表征,建立了问题的数学模型.构造了一种基于Memetic算法的优化求解方法,运用遗传算法和邻域搜索作为全局和局部搜索方法,用解的构造方式和选择策略处理了约束条件,比较了局部搜索使用不同邻域时算法的运行效率.最后通过实验验证了本方法的合理性和有效性.

基于Memetic算法的有限缓冲区流水车间调度问题

针对有限缓冲区流水线调度问题,提出一种基于变邻域搜索策略的Memetic算法。在基本遗传算法中,采用NEH算法和改进IG算法产生部分初始种群,使用部分交叉算子和插入变异算子,在交叉和变异之后设计一种改进变邻域搜索方法来进行局部搜索。在该改进变邻域搜索中,将模拟退火算法作为变邻域搜索中的局部搜索方法,大大增强了算法的寻优能力,使得Memetic算法在集中搜索和分散搜索之间达到更合理的平衡。运用提出算法求解经典基准算例,并与当前先进算法比较,验证了所提算法的有效性。

基于Memetic算法的带时间窗车辆路径问题研究

提出一种模拟文化进化的Memetic算法求解带时间窗的车辆路径问题。设计了一种实数编码方案,将离散的问题转为连续优化问题。采用邻域搜索帮助具备一定学习能力的个体提高寻优速度;采用禁忌搜索帮助部分个体跳出局部最优点,增强全局寻优性能。实验结果表明,该算法可以更有效地求出优化解,是带时间窗车辆路径问题的一种有效求解算法。

基于Memetic算法的车间动态调度策略研究

针对车间动态调度问题的求解,提出了一种基于Memetic算法的车间动态调度策略。该策略结合滚动窗口机制,并采用基于周期和事件的混合驱动策略,运用Memetic算法对每个滚动窗口工件集进行重调度。该算法采用顺序交叉算子和基于邻域搜索的新型变异算子,在交叉和变异后均采用改进的模拟退火策略进行局部搜索。通过对改进后的基准实例进行实验,验证了该策略的有效性。

基于模拟退火的混合萤火虫Memetic算法

针对标准萤火虫算法(FA),首先,从数学理论上分析并揭示了其存在的种群过早收敛、容易陷入局部最优等不足,然后提出一种基于模拟退火的混合萤火虫Memetic算法。该算法利用标准萤火虫算法对上一代种群进行全局搜索以保持种群的多样性和算法的全局探索能力;使用模拟退火算子对当前种群中的部分个体进行局部搜索,以一定概率接受适应度较差的个体以避免算法陷入局部最优,该算法同步进行萤火虫吸引过程和模拟退火过程以降低算法复杂度。最后,对该算法在10个标准测试函数上进行对比仿真实验。实验结果表明,该算法在6个测试函数中均能找到最优解,最优值、平均值、方差等指标比对比算法高出一定数量级,在4个复合函数中效果均优于萤火虫算法。