Abstract:Most of the existing clustering algorithms are often based on Euclidean distance measure. However, only using Euclidean distance measure may not be sufficient enough to partition a dataset with different structures. Thus, it is necessary to combine multiple distance measures into clustering. However, the weights for different distance measures are hard to set. Accordingly, it appears natural to keep multiple distance measures separately and to optimize them simultaneously by applying a multiobjective optimizat… Show more
“…Recent research has reported some first steps towards exploiting the intrinsic multi-criterion nature of MvC [14,11,15,16,17,8]. In MvC, data views are available either in the form of multiple feature sets or as multiple dissimilarity matrices [5,6,18].…”
“…Recent research has reported some first steps towards exploiting the intrinsic multi-criterion nature of MvC [14,11,15,16,17,8]. In MvC, data views are available either in the form of multiple feature sets or as multiple dissimilarity matrices [5,6,18].…”
“…Clustering is used to divide a set of objects, where objects in the same group are more similar to each other than to objects in different groups. Fuzzy C-means (FCM) [29] is one of the most popular clustering algorithms and is based on the fuzzy set principle. FCM evolves a partition matrix U (X ) during computation and minimizes equation (1).…”
Section: Problem Statement a Cluster Analysismentioning
confidence: 99%
“…We compared the proposed method with three classic singleobjective clustering algorithms (i.e., the differential evolution algorithm (DE) [40], particle swarm optimization (PSO) [41], and FCM [29]) on 8 UCI data sets. As seen in Table 4, the F − measure values of DE, PSO and FCM are all lower than that of HCHPS-MOEC.…”
Section: E Ablation Study 1) Evaluation On the Impact Of Multiobjective Optimizationmentioning
Clustering is a classic combined optimization problem that is widely used in pattern recognition, image processing, market analysis and so on. However, the efficiency of clustering algorithms decreases as the amount of data increases. In addition, most of the existing methods optimize only one objective and therefore may be suitable only for datasets with certain features. To address these limitations, in this paper, we develop a new hybrid chain-hypergraph P system (named HCHPS), which makes full use of the parallelism of P systems as well as the advantages of chain and hypergraph topology structures for accurate and efficient clustering. Our new P system comprises three types of subsystems, i.e., reaction chain membrane subsystems, local communication membrane subsystems and global ensemble membrane subsystems. Each type of subsystems is implemented end-to-end in HCHPS with new rules and membrane structures in parallel. In particular, to obtain efficient clustering center objects and make the algorithm robust to data with various features, the reaction chain membrane subsystems perform three different multiobjective strategies simultaneously by new chain evolution rules. To increase the population diversity of cluster centers, the local communication membrane subsystems utilize transport rules between membranes for coevolution of nondominated objects. The global ensemble membrane subsystems conduct a new dense representation multisize ensemble strategy to further improve the accuracy of the final results. Evaluations on two artificial data sets and 17 real-life data sets demonstrate the robustness of the proposed method in correctly clustering data sets with different dimensions and shapes. Our experimental results outperform those of both baseline and state-of-the-art methods. Moreover, benefiting from the parallelism, HCHPS is less time consuming than other methods, featuring an average completion time of 28.07 seconds on the 17 real-life data sets. Moreover, an ablation study shows that our proposed components are critical for effective cluster analysis. INDEX TERMS Chain-hypergraph P system, multiobjective optimization, cluster analysis.
“…However, modelling the actual traffic behavior accurately is difficult, and optimization algorithms based on the aforementioned model may be ineffective. Some dynamic programming algorithms with a large amount of calculation are unsuitable for real-time control [9]. Fuzzy control does not need accurate modelling, and the design of a fuzzy controller is relatively simple.…”
This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In addition to arrival rate and queue vehicles, a specific phase order rule is considered to avoid disordered phase selection in fuzzy control. At the second stage, the green time detection module decides whether to extend the current green time or switch phases every few seconds and the vehicle arrival rate is not required as the input to controller in real-time detection. Differential evolution algorithm with low space complexity and fast convergence is applied to optimize the fuzzy rules for avoiding artificial uncertainty. Simulation experiments are designed to compare traditional fuzzy controller, fixed-time controller, and fuzzy controller without flow prediction. Results show that the current proposed method in this paper can reduce vehicle delay significantly.
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