An Improved Multi-Objective Evolutionary Approach for Clustering High-Dimensional Data

Fuzzy partition based soft subspace clustering and its applications in high dimensional data

In this paper, two novel soft subspace clustering algorithms, namely fuzzy weighting subspace clustering with competitive agglomeration (FWSCA) and entropy weighting subspace clustering with competitive agglomeration (EWSCA), are proposed to overcome the problems of the unknown number of clusters and the initialization of prototypes for soft subspace clustering. The main advantage of FWSCA and EWSCA lies in the fact that they effectively integrate the merits of soft subspace clustering and the good properties of fuzzy clustering with competitive agglomeration. This makes it possible to obtain the appropriate number of clusters during the clustering progress. Moreover, FWSCA and EWSCA algorithms can converge regardless of the initial number of clusters and initialization. Substantial experimental results on both synthetic and real data sets demonstrate the effectiveness of FWSCA and EWSCA in addressing the two problems.

While the Euclidean distance metric is commonly utilized in most soft subspace clustering algorithms, some robust distance metrics can also be considered for soft subspace clustering. In this study, a novel soft subspace clustering algorithm called Alternative Soft Subspace Clustering (ASSC) is proposed by incorporating the alternative distance metric into the framework of entropy weighting subspace clustering. The properties of ASSC are investigated and a theoretical analysis on robustness is established accordingly. Experiments on UCI data are conducted and the experimental results show the effectiveness of the proposed algorithm.

Soft subspace clustering (SSC), which analyzes high-dimensional data and applies various weights to each cluster class to assess the membership degree of each cluster to the space, has shown promising results in recent years. This method of clustering assigns distinct weights to each cluster class. By introducing spatial information, enhanced SSC algorithms improve the degree to which intraclass compactness and interclass separation are achieved. However, these algorithms are sensitive to noisy data and have a tendency to fall into local optima. In addition, the segmentation accuracy is poor because of the influence of noisy data. In this study, an SSC approach that is based on particle swarm optimization is suggested with the intention of reducing the interference caused by noisy data. The particle swarm optimization method is used to locate the best possible clustering center. Second, increasing the amount of geographical membership makes it possible to utilize the spatial information to quantify the link between different clusters in a more precise manner. In conclusion, the extended noise clustering method is implemented in order to maximize the weight. Additionally, the constraint condition of the weight is changed from the equality constraint to the boundary constraint in order to reduce the impact of noise. The methodology presented in this research works to reduce the amount of sensitivity the SSC algorithm has to noisy data. It is possible to demonstrate the efficacy of this algorithm by using photos with noise already present or by introducing noise to existing photographs. The revised SSC approach based on particle swarm optimization (PSO) is demonstrated to have superior segmentation accuracy through a number of trials; as a result, this work gives a novel method for the segmentation of noisy images.

Transfer learning-assisted multi-objective evolutionary clustering framework with decomposition for high-dimensional data

High dimensional data is a phenomenon in real-world data mining applications. Text data is a typical example. In text mining, a text document is viewed as a vector of terms whose dimension is equal to the total number of unique terms in a data set, which is usually in thousands. High dimensional data occurs in business as well. In retails, for example, to effectively manage supplier relationship, suppliers are often categorized according to their business behaviors (Zhang, Huang, Qian, Xu, & Jing, 2006). The supplier’s behavior data is high dimensional, which contains thousands of attributes to describe the supplier’s behaviors, including product items, ordered amounts, order frequencies, product quality and so forth. One more example is DNA microarray data. Clustering high-dimensional data requires special treatment (Swanson, 1990; Jain, Murty, & Flynn, 1999; Cai, He, & Han, 2005; Kontaki, Papadopoulos & Manolopoulos., 2007), although various methods for clustering are available (Jain & Dubes, 1988). One type of clustering methods for high dimensional data is referred to as subspace clustering, aiming at finding clusters from subspaces instead of the entire data space. In a subspace clustering, each cluster is a set of objects identified by a subset of dimensions and different clusters are represented in different subsets of dimensions. Soft subspace clustering considers that different dimensions make different contributions to the identification of objects in a cluster. It represents the importance of a dimension as a weight that can be treated as the degree of the dimension in contribution to the cluster. Soft subspace clustering can find the cluster memberships of objects and identify the subspace of each cluster in the same clustering process.

Broad learning approach to Surrogate-Assisted Multi-Objective evolutionary fuzzy clustering algorithm based on reference points for color image segmentation

The efficient operation of the maintenance service system is key to achieving sustainable operations, with its core lying in the coordinated scheduling of spare parts production and maintenance personnel, as well as the holistic management of in-warranty and out-of-warranty demands. This approach optimizes resource allocation and enhances long-term service value. This paper investigates the integrated scheduling of distributed spare parts production and maintenance personnel with differentiated in-warranty and out-of-warranty demands (ISSPD). To solve the ISSPD, an improved non-dominated sorting genetic algorithm-II that uses Q-learning to adaptively select local search strategies (QLNSGA) is proposed, which incorporates a decoding strategy for differentiated order types, eight knowledge-driven local search strategies, and a Q-learning mechanism for the adaptive selection of key local search operators. Compared to random local search operators, the Q-learning mechanism achieves a 55% decrease in IGD metric and a 65% increase in HV metric. Through comparative experiments with four mainstream algorithms, QLNSGA outperforms RIPG by 58% in terms of the IGD index, and its HV index is generally superior to that of comparative algorithms such as MOEA/D. This indicates that QLNSGA exhibits superior performance in both computational efficiency and solution quality, effectively enhancing service levels and significantly reducing operational costs, thereby providing scientific decision support for service-oriented manufacturing enterprises.

This paper presents a multiobjective memetic algorithm based on adaptive local search chains (MMA-ALSC) for vehicle routing problem with time window (VRPTW) which is an important research area in logistics. As shown in most previous studies, VRPTW is essentially a multiobjective optimization problem and can be solved effectively by the multiobjective algorithms with various local search operators. We have observed, however, that the promising solutions obtained during the process of evolution are not fully utilized to guide the search together with different local search operators. This will lead to the discontinuous and insufficient search in the regions showing promise. To alleviate this drawback, MMA-ALSC is proposed and characterized by combining a multi-directional local search strategy (MD-LS) with an enhanced local search chain technique (eLS-Chain). In MMA-ALSC, on the one hand, with MD-LS, different local search operators are designed to perform the search towards multiple directions with distinct problem-specific knowledge of multiobjective VRPTW (MOVRPTW). On the other hand, with eLS-Chain, the promising solutions obtained during the process of evolution are adaptively selected for the subsequent local search operators. In this way, MMA-ALSC can not only effectively explore the search space in multiple directions, but also fully exploit the promising solutions in a chain-based way. Experimental results on two suites of benchmark instances have demonstrated the competitive performance of MMA-ALSC when compared with other representative algorithms.

This paper presents a memetic algorithm for the task of sketch geolocation. Small sets of 2D objects having real-world origin are depicted as sketches, which capture their spatial configuration. These are matched to a much larger geospatial reference scene using a memetic algorithm, which combines both global and local search strategies. Sets are represented as attributed relational graphs in which objects are graph nodes and the spatial relationships between objects, defined by the histograms of forces, are graph edges. We define a similarity measure between two such graphs and describe two local search operators. The first is a greedy operator based on our previous work and the second is a new approach based on the VF2 subgraph isomorphism algorithm. Our experiments show that both methods can be successfully applied to this domain.

An evolutionary programming algorithm for continuous global optimization

This paper addresses a bi-objective urban traffic light scheduling problem (UTLSP), which requires minimizing both the total network-wise delay time of all vehicles and total delay time of all pedestrians within a given finite-time window. First, a centralized model is employed to describe the UTLSP, where the cost functions and constraints of the two objectives are presented. A non-domination strategy-based metric is used to compare and rank solutions based on the two objectives. Second, metaheuristics, such as harmony search (HS) and artificial bee colony (ABC), are implemented to solve the UTLSP. Based on the characteristics of the UTLSP, a local search operator is utilized to improve the search performance of the developed optimization algorithms. Finally, experiments are carried out based on the real traffic data in Jurong area of Singapore. The HS, ABC, and their variants with the local search operator are evaluated in 19 case studies with different scales and time windows. To the best of our knowledge, this paper is the first of its kind to solve bi-objective traffic light scheduling problems in the literature. To demonstrate the effectiveness of the proposed algorithms in dealing with bi-objective optimization in traffic light scheduling, they are compared to the classical non-dominated sorting genetic algorithm II (NSGAII) with and without the local search operation. The comparisons indicate that our algorithms outperform the NSGAII algorithm with and without the local search operator for solving the UTLSP.

Multi-user agile earth observation satellite scheduling problem (MU-AEOSSP) is an important combinatorial optimization problem for satellite daily management. In this study, a MU-AEOSSP is addressed to tackle the failure rate and the fairness of different users simultaneously. A hybrid multi-objective coevolutionary approach (HMOCA) is then proposed to handle the complicate constraints and to optimize the objectives. HMOCA evolves two populations to solve an original MU-AEOSSP considering all constraints and a helper problem without the transition time constraint. By the cooperation of the two population, both the convergence and the diversity performance can be significantly improved. To further enhance the performance of HMOCA, several specific variation operators and a local search operator considering the time-dependent transition time of the MU-AEOSSP are equipped. The HMOCA is extensively tested and compared with three classical multi-objective evolutionary algorithms (NSGAII, MOEA/D, IBEA) and two methods of the time-dependent multi-objective AEOSSP (D-MOMA-TD and I-MOMA-TD) on several instances which are generated based on real-word situation. Experiment results show that the proposed approach outperforms all the comparison methods on most of the instances in terms of convergence, solution quality and diversity.KeywordsCoevolutionary algorithmAgile satelliteMulti-objective

Self-organizing subspace clustering for high-dimensional and multi-view data

When solving multi-objective optimization problems (MOPs) with big data, traditional multi-objective evolutionary algorithms (MOEAs) meet challenges because they demand high computational costs that cannot satisfy the demands of online data processing involving optimization. The gradient heuristic optimization methods show great potential in solving large scale numerical optimization problems with acceptable computational costs. However, some intrinsic limitations make them unsuitable for searching for the Pareto fronts. It is believed that the combination of these two types of methods can deal with big MOPs with less computational cost. The main contribution of this paper is that a multi-objective memetic algorithm based on decomposition for big optimization problems (MOMA/D-BigOpt) is proposed and a gradient-based local search operator is embedded in MOMA/D-BigOpt. In the experiments, MOMA/D-BigOpt is tested on the multi-objective big optimization problems with thousands of variables. We also combine the local search operator with other widely used MOEAs to verify its effectiveness. The experimental results show that the proposed algorithm outperforms MOEAs without the gradient heuristic local search operator.