Recommender system with grey wolf optimizer and FCM

Recommender systems are contributing a significant aspect in information filtering and knowledge management systems. They provide explicit and reliable recommendations to the users so that user can get information about all products in e-commerce domain. In the era of big data and large complex information delivery system, it is impossible to get the right information in the online environment. In this research work, we offered a novel movie-based collaborative recommender system which utilizes the bio-inspired gray wolf optimizer algorithm and fuzzy c-mean (FCM) clustering technique and predicts rating of a movie for a particular user based on his historical data and similarity of users. Gray wolf optimizer algorithm was applied on the Movielens dataset to obtain the initial clusters, and also the initial positions of clusters are obtained. FCM is used to classify the users in the dataset by similarity of user ratings. Our proposed collaborative recommender system performed extremely well with respect to accuracy and precision. We analyzed our proposed recommender system over Movielens dataset which is available publically. Various evaluation metrics were utilized such as mean absolute error, standard deviation, precision and recall. We also compared the performance of projected system with already established systems. The experiment results delivered by proposed recommender system demonstrated that efficiency and performance are enhanced and also offered better recommendations when compared with our previous work [1].

     

双种群灰狼优化算法的物流配送中心选址策略

针对物流配送中心选址模型具有多约束和非线性的特点,导致难以求解的问题.提出一种改进灰狼优化算法的求解策略.文章通过引入交叉变异策略,改进了传统灰狼算法在迭代后期易早熟收敛的问题;通过加入双种群寻优策略,丰富了灰狼算法的种群多样性,提高了算法的收敛速度.将改进后的灰狼算法针对物流配送中心选址模型进行求解,实验结果表明,该改进灰狼优化算法具有较高的全局搜索能力,针对物流配送中心选址模型具有较高的搜索精度,很大程度的提高了物流配送效率.     

基于停滞检测的双向搜索灰狼优化算法

针对灰狼优化算法（GWO）易陷入局部最优、收敛速度低的问题,提出了一种基于停滞检测的双向搜索灰狼优化算法（DBGWO）。为了提升初始种群的质量,引入了Bernouilli shift映射;为了充分利用GWO特有的头狼机制,实现整体提升算法性能的目的,提出一种双向搜索策略;为了提升算法跳出局部最优的能力、增加算法的收敛速度,提出一种停滞检测机制,针对算法是否有陷入局部最优风险的判断,狼群会采取相应的措施改变当前状态。通过对23个基准测试函数进行仿真实验结果表明,所提算法在求解多峰函数问题上效果显著,同时在求解最优解非0点的函数问题上表现也较为优越。将该算法用于求解多阈值图像分割问题,解决了用Kapur熵法计算多阈值时耗时过长的问题。     

Multilevel thresholding using grey wolf optimizer for image segmentation

Multilevel thresholding is one of the most important areas in the field of image segmentation. However, the computational complexity of multilevel thresholding increases exponentially with the increasing number of thresholds. To overcome this drawback, a new approach of multilevel thresholding based on Grey Wolf Optimizer (GWO) is proposed in this paper. GWO is inspired from the social and hunting behaviour of the grey wolves. This metaheuristic algorithm is applied to multilevel thresholding problem using Kapur's entropy and Otsu's between class variance functions. The proposed method is tested on a set of standard test images. The performances of the proposed method are then compared with improved versions of PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based multilevel thresholding methods. The quality of the segmented images is computed using Mean Structural SIMilarity (MSSIM) index. Experimental results suggest that the proposed method is more stable and yields solutions of higher quality than PSO and BFO based methods. Moreover, the proposed method is found to be faster than BFO but slower than the PSO based method.     

混合苍狼优化算法在全局最优中的应用研究

在分析标准苍狼优化算法（GWO）的开发与探索性能基础上,提出了一种混合苍狼优化算法（MAR- GWO）,搜索域得到了全面的扩展,其中针对[α、][β、][δ]领导层苍狼,引入自主搜索行为来加大其优化力度与促进速度的提高,对性能较差搜索狼采取淘汰重组机制以提高搜索效率,又采取概率差分变异行为增加了个体多样性,从而避免局部最优。为了验证MAR-GWO算法有效性,对13个全局优化问题进行实验,分别与GWO、GWO-EPD（改进的苍狼优化算法）、PSO、EA等算法进行了对比测试,从实验结果来看,MAR-GWO算法寻优成功率相对较高、收敛速度快,不易陷入局部最优,在智能算法中具有很强的竞争力。     

基于改进二进制灰狼算法的频谱分配

为解决当前频谱资源紧缺和利用率低的问题,提出一种基于改进二进制灰狼算法(IBGWO)的频谱分配方案.在算法中加入一个非线性收敛因子、柯西扰动策略和自适应权重,提高算法的寻优性能;在连续空间到离散空间的转换中,引入一个新的转换函数实现离散化操作;将改进后的二进制灰狼算法和频谱分配模型结合,以最大化系统效益和认知用户接入公...     

多目标灰狼优化算法的改进策略研究

为了解决多目标灰狼优化算法（MOGWO）易陷入局部最优,稳定性差等缺点,基于对算法寻优时灰狼个体运动情况的分析,提出了两条改进策略：一是通过引入“观察”策略赋予灰狼个体自主探索的能力,以提高算法的优化效率和跳出局部最优的能力;二是改进控制参数调整策略,选用幂函数取代线性函数以提高算法的稳定性。然后对两条改进策略进行了可行性分析,提出了带观察策略的多目标灰狼算法并进行了算法复杂度分析。最后通过对6个不同特点测试函数的多次重复实验,结合GD与IGD两种通用评价指标,对原算法、改进后算法和多目标粒子群算法进行比较,从算法效率、寻优能力和稳定性等方面综合验证了算法改进的有效性和优越性。     

A fuzzy hierarchical operator in the grey wolf optimizer algorithm

The main goal of this paper is to study the performance of the Grey Wolf Optimizer (GWO) algorithm when a new hierarchical operator is introduced in the algorithm. This new operator is basically a hierarchical transformation that is inspired in the hierarchical social pyramid of the grey wolf. This proposed operator is applied to the simulation of the hunting process in the algorithm and has 5 variants that are explained in more detail in this paper (centroid, weighted, based on the fitness and two variants using fuzzy logic). Notably the variants having the greatest impact in the GWO performance are based on the use of fuzzy logic. We also present the motivation and results of experiments, as well as the benchmark functions that were used for the tests that are presented. In addition we are presenting a comparison among all methods for 30, 64 and 128 dimensions and we conclude that the performance of the Hierarchical GWO algorithm is better when using a fuzzy variant of the hierarchical operator.     

Multi-strategy ensemble grey wolf optimizer and its application to feature selection

To overcome the limitation of single search strategy of grey wolf optimizer (GWO) in solving various function optimization problems, we propose a multi-strategy ensemble GWO (MEGWO) in this paper. The proposed MEGWO incorporates three different search strategies to update the solutions. Firstly, the enhanced global-best lead strategy can improve the local search ability of GWO by fully exploiting the search space around the current best solution. Secondly, the adaptable cooperative strategy embeds one-dimensional update operation into the framework of GWO to provide a higher population diversity and promote the global search ability. Thirdly, the disperse foraging strategy forces a part of search agents to explore a promising area based on a self-adjusting parameter, which contributes to the balance between the exploitation and exploration. We conducted numerical experiments based on various functions form CEC2014. The obtained results are compared with other three modified GWO and seven state-of-the-art algorithms. Furthermore, feature selection is employed to investigate the effectiveness of MEGWO on real-world applications. The experimental results show that the proposed algorithm which integrate multiple improved search strategies, outperforms other variants of GWO and other algorithms in terms of accuracy and convergence speed. It is validated that MEGWO is an efficient and reliable algorithm not only for optimization of functions with different characteristics but also for real-world optimization problems.     

Sine cosine grey wolf optimizer to solve engineering design problems

Balancing the exploration and exploitation in any nature-inspired optimization algorithm is an essential task, while solving the real-world global optimization problems. Therefore, the search agents of an algorithm always try to explore the unvisited domains of a search space in a balanced manner. The sine cosine algorithm (SCA) is a recent addition to the field of metaheuristics that finds the solution of an optimization problem using the behavior of sine and cosine functions. However, in some cases, the SCA skips the true solutions and trapped at sub-optimal solutions. These problems lead to the premature convergence, which is harmful in determining the global optima. Therefore, in order to alleviate the above-mentioned issues, the present study aims to establish a comparatively better synergy between exploration and exploitation in the SCA. In this direction, firstly, the exploration ability of the SCA is improved by integrating the social and cognitive component, and secondly, the balance between exploration and exploitation is maintained through the grey wolf optimizer (GWO). The proposed algorithm is named as SC-GWO. For the performance evaluation, a well-known set of benchmark problems and engineering test problems are taken. The dimension of benchmark test problems is varied from 30 to 100 to observe the robustness of the SC-GWO on scalability of problems. In the paper, the SC-GWO is also used to determine the optimal setting for overcurrent relays. The analysis of obtained numerical results and its comparison with other metaheuristic algorithms demonstrate the superior ability of the proposed SC-GWO.

     

A binary grey wolf optimizer for the multidimensional knapsack problem

Grey Wolf Optimizer (GWO) is a new meta-heuristic that mimics the leadership hierarchy and group hunting mechanism of grey wolves in nature. A binary version is developed to tackle the multidimensional knapsack problem which has an extensive engineering background. The proposed binary grey wolf optimizer integrates some important features including an initial elite population generator, a pseudo-utility-based quick repair operator, a new evolutionary mechanism with a differentiated position updating strategy. The proposed algorithm takes full advantage of the knowledge of the problem to be solved and highlights the distinctive feature of the optimizer in the family of evolutionary algorithm. Experimental results statistically show the effectiveness of the new optimizer and the superiority of the proposed algorithm in solving the multidimensional knapsack problem, especially the large-scale problem.     

基于优化的灰狼算法的大规模Web服务组合

针对大规模Web服务环境中难以获得整体性能高的组合服务的问题,提出了一种大规模Web服务组合方法。首先,采用文档对象模型（DOM）对XML格式的用户需求描述文档进行解析,以生成抽象Web服务组合序列;然后,采用服务主题模型进行服务筛选,并为每个抽象Web服务选取Top-k个具体Web服务从而缩减组合空间;接着,为提高服务组合质量和组合效率,提出了一种基于Logistic混沌映射和非线性收敛因子的优化的灰狼算法（OGWO/LN）来进行最优服务组合方案选择;该算法采用混沌映射来生成初始种群以增加服务组合方案的多样性,并避免了多次局部寻优;同时,提出一种非线性收敛因子来调节算法的搜索能力以提高算法的寻优性能;最后,采用MapReduce框架对OGWO/LN进行了并行实现。在真实数据集上的实验结果表明,所提算法与IFOA4WSC、MR-IDPSO、MR-GA等算法相比,平均适应度值分别提高了8.69%、7.94%和12.25%,在解决大规模Web服务组合问题时具有更好的寻优性能和稳定性。     

An integer encoding grey wolf optimizer for virtual network function placement

This paper studies the virtual network function placement (VNF-P) problem in the context of network function virtualization (NFV), where the end-to-end delay of a requested service function chain (SFC) is minimized and the compute, storage, I/O and bandwidth resources are considered. To address this problem, an integer encoding grey wolf optimizer (IEGWO) is proposed. IEGWO has two significant features, namely an integer encoding scheme and a new wolf position update mechanism. The integer encoding scheme is problem-specific and offers a natural way to represent VNF-P solutions. The proposed wolf position update mechanism divides the wolf pack into two groups in each iteration, where one group performs exploitation while the other focuses on global exploration. It provides the search with a balanced local exploitation and global exploration during evolution. Performance evaluation has been conducted based on 20 test instances and IEGWO is compared with five state-of-the-art meta-heuristics, including the black hole algorithm (BH), the genetic algorithm (GA), the group counseling optimization (GCO), the particle swarm optimization (PSO) and the teaching–learning-based optimization (TLBO). Simulation results demonstrate that compared with BH, GA, GCO, PSO and TLBO, IEGWO achieves significantly better solution quality regarding the mean (standard deviation), boxplot and t-test results of the best fitness values obtained.     

求解作业车间调度问题的改进混合灰狼优化算法*

灰狼优化算法(GWO)是目前一种比较新颖的群智能优化算法,具有收敛速度快,寻优能力强等优点。本文将灰狼优化算法用于求解复杂的作业车间调度问题,与布谷鸟搜索算法进行比较研究,验证了标准GWO算法求解经典作业车间调度问题的可行性和有效性。在此基础上,针对复杂作业车间调度问题难以求解的特点,对标准GWO算法进行改进,通过进化种群动态、反向学习初始化种群,以及最优个体变异等三个方面的改进操作,测试结果表明改进后的混合灰狼优化算法能够有效跳出局部最优值,找到更好的解,并且结果鲁棒性更强。     

Unsupervised hyperspectral feature selection based on fuzzy c-means and grey wolf optimizer

Hyperspectral image (HSI) with hundreds of narrow and consecutive spectral bands provides substantial information to discriminate various land-covers. However, the existence of redundant features/bands not only gives rise to increasing of computation time but also interferes the classification result of hyperspectral images. Obviously, it is a very challenging problem how to select an effective feature subset from original bands to reduce the dimensionality of the hyperspectral dataset. In this study, a novel unsupervised feature selection method is suggested to remove the redundant features of HSI by feature subspace decomposition and optimization of feature combination. Feature subset decomposition is achieved by the fuzzy c-means (FCM) algorithm. The optimal feature selection is based on the optimization process of grey wolf optimizer (GWO) algorithm and maximum entropy (ME) principle. To evaluate the effectiveness of the proposed method, experiments are conducted on three well-known hyperspectral datasets, Indian Pines, Pavia University, and Salinas. Six state-of-the-art feature selection methods are used to compare with the proposed method. Experimental results successfully confirm the superior performance of our proposal with respect to three classification accuracy indices overall accuracy (OA), average accuracy (AA) and kappa coefficient (κ).     

Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization

Due to the novelty of the Grey Wolf Optimizer (GWO), there is no study in the literature to design a multi-objective version of this algorithm. This paper proposes a Multi-Objective Grey Wolf Optimizer (MOGWO) in order to optimize problems with multiple objectives for the first time. A fixed-sized external archive is integrated to the GWO for saving and retrieving the Pareto optimal solutions. This archive is then employed to define the social hierarchy and simulate the hunting behavior of grey wolves in multi-objective search spaces. The proposed method is tested on 10 multi-objective benchmark problems and compared with two well-known meta-heuristics: Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Multi-Objective Particle Swarm Optimization (MOPSO). The qualitative and quantitative results show that the proposed algorithm is able to provide very competitive results and outperforms other algorithms. Note that the source codes of MOGWO are publicly available at http://www.alimirjalili.com/GWO.html.     

Detection of spam reviews using hybrid grey wolf optimizer clustering method

Multimedia Tools and Applications - Currently, online reviews play an essential role in the decision-making of customers. Various online websites such as Amazon, Yelp, Google Plus, BookMyShow,...     

采用动态权重和概率扰动策略改进的灰狼优化算法

针对基本灰狼优化（GWO）算法存在易陷入局部最优,进而导致搜索精度偏低的问题,提出了一种改进的GWO （IGWO）算法。一方面,通过引入由GWO算法系数向量构成的权值因子,动态调整算法的位置向量更新方程;另一方面,通过采用概率扰动策略,增强算法迭代后期的种群多样性,从而提升算法跳出局部最优的能力。对多个基准测试函数进行仿真实验,实验结果表明,相对于GWO算法、混合GWO （HGWO）算法、引力搜索算法（GSA）和差分进化（DE）算法,所提IGWO算法有效摆脱了局部收敛,在搜索精度、算法稳定性以及收敛速度上具有明显优势。     

Hybrid multi-verse optimizer with grey wolf optimizer for power scheduling problem in smart home using IoT

The Journal of Supercomputing - The power scheduling problem in smart home (PSPSH) is one of the complex NP-hard scheduling problems, where it has a deep and rugged search space due to the high...     

改进的灰狼优化算法及其高维函数和FCM优化

灰狼优化算法(GWO)具有较强的局部搜索能力和较快的收敛速度,但在解决高维和复杂的优化问题时存在全局搜索能力不足的问题.对此,提出一种改进的GWO,即新型反向学习和差分变异的GWO(ODGWO).首先,提出一种最优最差反向学习策略和一种动态随机差分变异算子,并将它们融入GWO中,以便增强全局搜索能力;然后,为了很好地平衡探索与开采能力以提升整体的优化性能,对算法前、后半搜索阶段分别采用单维操作和全维操作形成ODGWO;最后,将ODGWO用于高维函数和模糊C均值(FCM)聚类优化.实验结果表明,在许多高维Benchmark函数(30维、50维和1000维)优化上,ODGWO的搜索能力大幅度领先于GWO,与state-of-the-art优化算法相比,ODGWO具有更好的优化性能.在7个标准数据集的FCM聚类优化上, 与GWO、GWOepd和LGWO相比,ODGWO表现出了更好的聚类优化性能,可应用在更多的实际优化问题上.