Cuckoo and krill herd‐based k‐means++ hybrid algorithms for clustering

Clustering algorithms can be optimized using nature‐inspired techniques. Many algorithms inspired by nature, namely, firefly algorithm, ant colony optimization algorithm, and so forth, have improved clustering results. k‐means is a popular clustering technique but has limitations of local optima, which have been overcome using its various hybrids. k‐means++ is a hybrid k‐means clustering algorithm that gives the procedure to initialize centre of the clusters. In the proposed work, hybrids of nature‐inspired techniques using cuckoo and krill herd algorithm are implemented on k‐means++ algorithm to enhance cluster quality and generate optimized clusters. The designed algorithms are implemented, and the results are compared with their counterparts. Performance parameters such as accuracy, f‐measure, error rate, standard deviation, CPU time, cluster quality check, and so forth are used to measure the clustering capabilities of these algorithms. The results indicate the high performance of newly designed algorithms.     

Automatic clustering algorithm based on multi-objective Immunized PSO to classify actions of 3D human models

Multi-objective clustering algorithms are preferred over its conventional single objective counterparts as they incorporate additional knowledge on properties of data in the from of objectives to extract the underlying clusters present in many datasets. Researchers have recently proposed some standardized multi-objective evolutionary clustering algorithms based on genetic operations, particle swarm optimization, clonal selection principles, differential evolution and simulated annealing, etc. In many cases it is observed that hybrid evolutionary algorithms provide improved performance compared to that of individual algorithm. In this paper an automatic clustering algorithm MOIMPSO (Multi-objective Immunized Particle Swarm Optimization) is proposed, which is based on a recently developed hybrid evolutionary algorithm Immunized PSO. The proposed algorithm provides suitable Pareto optimal archive for unsupervised problems by automatically evolving the cluster centers and simultaneously optimizing two objective functions. In addition the algorithm provides a single best solution from the Pareto optimal archive which mostly satisfy the users' requirement. Rigorous simulation studies on 11 benchmark datasets demonstrate the superior performance of the proposed algorithm compared to that of the standardized automatic clustering algorithms such as MOCK, MOPSO and MOCLONAL. An interesting application of the proposed algorithm has also been demonstrated to classify the normal and aggressive actions of 3D human models.     

Evolutionary Algorithms for Minimax Problems in Robust Design

Many robust design problems can be described by minimax optimization problems. Classical techniques for solving these problems have typically been limited to a discrete form of the problem. More recently, evolutionary algorithms, particularly coevolutionary optimization techniques, have been applied to minimax problems. A new method of solving minimax optimization problems using evolutionary algorithms is proposed. The performance of this algorithm is shown to compare favorably with the existing methods on test problems. The performance of the algorithm is demonstrated on a robust pole placement problem and a ship engineering plant design problem.     

Soft and evolutionary computation based data association approaches for tracking multiple targets in the presence of ECM

This paper proposes two novel soft and evolutionary computing based hybrid data association techniques to track multiple targets in the presence of electronic countermeasures (ECM), clutter and false alarms. Joint probabilistic data association (JPDA) approach is generally used for tracking multiple targets. Fuzzy clustering means (FCM) technique was proposed earlier as an efficient method for data association, but its cluster centers may fall to local minima. Hence, new hybrid data association approaches based on fuzzy particle swarm optimization (Fuzzy-PSO) and fuzzy genetic algorithm (Fuzzy-GA) clustering techniques have been presented as robust methods to overcome local minima problem. The data association matrix is evaluated for all tracks using validated measurements obtained by phased array radar for four different cases applying four data association methods (JPDA, FCM, Fuzzy-PSO, and Fuzzy-GA). Therefore, two hybrid data association approaches are designed and tested for multi-target tracking using intelligent techniques. Experimental results indicate that Fuzzy-GA data association technique provides improved performance compared to all other methods in terms of position and velocity RMSE values (38.69% and 33.19% average improvement for target-1;31.17% and 9.68% average improvement for target-2) respectively for crossing linear targets case. However, FCM technique gives better performance in terms of execution time (94.88% less average execution time) in comparison with other three techniques(JPDA, Fuzzy-GA, and Fuzzy-PSO) for the case of linear crossing targets. Thus accomplishing efficient and alternative multiple target tracking algorithms based on expert systems. The results have been validated with 100 Monte Carlo runs.     

A new multiobjective clustering technique based on the concepts of stability and symmetry

Most clustering algorithms operate by optimizing (either implicitly or explicitly) a single measure of cluster solution quality. Such methods may perform well on some data sets but lack robustness with respect to variations in cluster shape, proximity, evenness and so forth. In this paper, we have proposed a multiobjective clustering technique which optimizes simultaneously two objectives, one reflecting the total cluster symmetry and the other reflecting the stability of the obtained partitions over different bootstrap samples of the data set. The proposed algorithm uses a recently developed simulated annealing-based multiobjective optimization technique, named AMOSA, as the underlying optimization strategy. Here, points are assigned to different clusters based on a newly defined point symmetry-based distance rather than the Euclidean distance. Results on several artificial and real-life data sets in comparison with another multiobjective clustering technique, MOCK, three single objective genetic algorithm-based automatic clustering techniques, VGAPS clustering, GCUK clustering and HNGA clustering, and several hybrid methods of determining the appropriate number of clusters from data sets show that the proposed technique is well suited to detect automatically the appropriate number of clusters as well as the appropriate partitioning from data sets having point symmetric clusters. The performance of AMOSA as the underlying optimization technique in the proposed clustering algorithm is also compared with PESA-II, another evolutionary multiobjective optimization technique.     

Hybridization of evolutionary algorithms and local search by means of a clustering method.

This paper presents a hybrid evolutionary algorithm (EA) to solve nonlinear-regression problems. Although EAs have proven their ability to explore large search spaces, they are comparatively inefficient in fine tuning the solution. This drawback is usually avoided by means of local optimization algorithms that are applied to the individuals of the population. The algorithms that use local optimization procedures are usually called hybrid algorithms. On the other hand, it is well known that the clustering process enables the creation of groups (clusters) with mutually close points that hopefully correspond to relevant regions of attraction. Local-search procedures can then be started once in every such region. This paper proposes the combination of an EA, a clustering process, and a local-search procedure to the evolutionary design of product-units neural networks. In the methodology presented, only a few individuals are subject to local optimization. Moreover, the local optimization algorithm is only applied at specific stages of the evolutionary process. Our results show a favorable performance when the regression method proposed is compared to other standard methods.     

一种带混合进化机制的膜聚类算法

膜计算(也称为P系统或膜系统)是一种新颖的分布式、并行计算模型.为了处理数据聚类问题,提出了一种采用混合进化机制的膜聚类算法.它使用了一个由3个细胞组成的组织P系统,为一个待聚类的数据集发现最优的簇中心.其对象表示候选的簇中心,并且这3个细胞分别使用了3种不同的进化机制:遗传算子、速度-位移模型和差分进化机制.然而,所使用的速度-位移模型和差分进化机制是结合了这个特殊膜结构和转运机制所提出的改进版本.这种混合进化机制能够增强系统中对象的多样性和改善收敛性能.在混合进化机制和转运机制控制下,这种膜聚类算法能够确定一个数据集的良好划分.所提出的膜聚类算法在3个人工数据集和5个真实数据集上被评估,并与k-means和几种进化聚类算法进行比较.统计显著性测试建立了所提出的膜聚类算法的优势.     

An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis

Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO–K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO–SA), combination of ACO and SA (ACO–SA), combination of PSO and ACO (PSO–ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem.     

Efficient bottom-up hybrid hierarchical clustering techniques for protein sequence classification

Hybrid hierarchical clustering techniques which combine the characteristics of different partitional clustering techniques or partitional and hierarchical clustering techniques are interesting. In this paper, efficient bottom-up hybrid hierarchical clustering (BHHC) techniques have been proposed for the purpose of prototype selection for protein sequence classification. In the first stage, an incremental partitional clustering technique such as leader algorithm (ordered leader no update (OLNU) method) which requires only one database (db) scan is used to find a set of subcluster representatives. In the second stage, either a hierarchical agglomerative clustering (HAC) scheme or a partitional clustering algorithm—‘K-medians’ is used on these subcluster representatives to obtain a required number of clusters. Thus, this hybrid scheme is scalable and hence would be suitable for clustering large data sets and we also get a hierarchical structure consisting of clusters and subclusters and the representatives of which are used for pattern classification. Even if more number of prototypes are generated, classification time does not increase much as only a part of the hierarchical structure is searched. The experimental results (classification accuracy (CA) using the prototypes obtained and the computation time) of the proposed algorithms are compared with that of the hierarchical agglomerative schemes, K-medians and nearest neighbour classifier (NNC) methods. The proposed methods are found to be computationally efficient with reasonably good CA.     

Finite element based hybrid evolutionary optimization approach to solving rigid pavement inversion problem

This paper focuses on the development of a new backcalculation method for concrete road structures based on a hybrid evolutionary global optimization algorithm, namely shuffled complex evolution (SCE). Evolutionary optimization algorithms are ideally suited for intrinsically multi-modal, non-convex, and discontinuous real-world problems such as pavement backcalculation because of their ability to explore very large and complex search spaces and locate the globally optimal solution using a parallel search mechanism as opposed to a point-by-point search mechanism employed by traditional optimization algorithms. SCE, a type of evolutionary optimization algorithms based on the tradeoff of exploration and exploitation, has proved to be an efficient method for many global optimization problems and in some cases it does not suffer the difficulties encountered by other evolutionary computation techniques. The SCE optimization approach is hybridized with a neural networks surrogate finite-element based forward pavement response model to enable rapid computation of global or near-global pavement layer moduli solutions. The proposed rigid pavement backcalculation model is evaluated using field non-destructive test data acquired from a full-scale airport pavement test facility.     

A two-leveled symbiotic evolutionary algorithm for clustering problems

Because of its unsupervised nature, clustering is one of the most challenging problems, considered as a NP-hard grouping problem. Recently, several evolutionary algorithms (EAs) for clustering problems have been presented because of their efficiency for solving the NP-hard problems with high degree of complexity. Most previous EA-based algorithms, however, have dealt with the clustering problems given the number of clusters (K) in advance. Although some researchers have suggested the EA-based algorithms for unknown K clustering, they still have some drawbacks to search efficiently due to their huge search space. This paper proposes the two-leveled symbiotic evolutionary clustering algorithm (TSECA), which is a variant of coevolutionary algorithm for unknown K clustering problems. The clustering problems considered in this paper can be divided into two sub-problems: finding the number of clusters and grouping the data into these clusters. The two-leveled framework of TSECA and genetic elements suitable for each sub-problem are proposed. In addition, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. The performance of the proposed algorithm is compared with some popular evolutionary algorithms using the real-life and simulated synthetic data sets. Experimental results show that TSECA produces more compact clusters as well as the accurate number of clusters.     

An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering

Clustering techniques have received attention in many fields of study such as engineering, medicine, biology and data mining. The aim of clustering is to collect data points. The K-means algorithm is one of the most common techniques used for clustering. However, the results of K-means depend on the initial state and converge to local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Modify Imperialist Competitive Algorithm (MICA) and K-means (K), which is called K-MICA, for optimum clustering N objects into K clusters. The new Hybrid K-ICA algorithm is tested on several data sets and its performance is compared with those of MICA, ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handling data clustering.     

GA-fuzzy modeling and classification: complexity and performance

The use of genetic algorithms (GAs) and other evolutionary optimization methods to design fuzzy rules for systems modeling and data classification have received much attention in recent literature. Authors have focused on various aspects of these randomized techniques, and a whole scale of algorithms have been proposed. We comment on some recent work and describe a new and efficient two-step approach that leads to good results for function approximation, dynamic systems modeling and data classification problems. First, fuzzy clustering is applied to obtain a compact initial rule-based model. Then this model is optimized by a real-coded GA subjected to constraints that maintain the semantic properties of the rules. We consider four examples from the literature: a synthetic nonlinear dynamic systems model, the iris data classification problem, the wine data classification problem, and the dynamic modeling of a diesel engine turbocharger. The obtained results are compared to other recently proposed methods     

Hybrid chemical reaction based metaheuristic with fuzzy c-means algorithm for optimal cluster analysis

Hybridization of two or more algorithms has always been a keen interest of research due to the quality of improvement in searching capability. Taking the positive insights of both the algorithms, the developed hybrid algorithm tries to minimize the substantial limitations. Clustering is an unsupervised learning method, which groups the data according to their similar or dissimilar properties. Fuzzy c-means (FCM) is one of the popularly used clustering algorithms and performs better as compared to other clustering techniques such as k-means. However, FCM possesses certain limitations such as premature trapping at local minima and high sensitivity to the cluster center initialization. Taking these issues into consideration, this research proposes a novel hybrid approach of FCM with a recently developed chemical based metaheuristic for obtaining optimal cluster centers. The performance of the proposed approach is compared in terms of cluster fitness values, inter-cluster distance and intra-cluster distance with other evolutionary and swarm optimization based approaches. A rigorous experimentation is simulated and experimental result reveals that the proposed hybrid approach is performing better as compared to other approaches.     

Enhancing principal direction divisive clustering

While data clustering has a long history and a large amount of research has been devoted to the development of numerous clustering techniques, significant challenges still remain. One of the most important of them is associated with high data dimensionality. A particular class of clustering algorithms has been very successful in dealing with such datasets, utilising information driven by the principal component analysis. In this work, we try to deepen our understanding on what can be achieved by this kind of approaches. We attempt to theoretically discover the relationship between true clusters in the data and the distribution of their projection onto the principal components. Based on such findings, we propose appropriate criteria for the various steps involved in hierarchical divisive clustering and develop compilations of them into new algorithms. The proposed algorithms require minimal user-defined parameters and have the desirable feature of being able to provide approximations for the number of clusters present in the data. The experimental results indicate that the proposed techniques are effective in simulated as well as real data scenarios.     

Solving equations by hybrid evolutionary computation techniques

Evolutionary computation techniques have mostly been used to solve various optimization and learning problems. This paper describes a novel application of evolutionary computation techniques to equation solving. Several combinations of evolutionary computation techniques and classical numerical methods are proposed to solve linear and partial differential equations. The hybrid algorithms have been compared with the well-known classical numerical methods. The experimental results show that the proposed hybrid algorithms outperform the classical numerical methods significantly in terms of effectiveness and efficiency     

Evolutionary algorithms for optimization problems with uncertainties and hybrid indices

Many optimization problems in real-world applications contain both explicit (quantitative) and implicit (qualitative) indices that usually contain uncertain information. How to effectively incorporate uncertain information in evolutionary algorithms is one of the most important topics in information science. In this paper, we study optimization problems with both interval parameters in explicit indices and interval uncertainties in implicit indices. To incorporate uncertainty in evolutionary algorithms, we construct a mathematical uncertain model of the optimization problem considering the uncertainties of interval objectives; and then we transform the model into a precise one by employing the method of interval analysis; finally, we develop an effective and novel evolutionary optimization algorithm to solve the converted problem by combining traditional genetic algorithms and interactive genetic algorithms. The proposed algorithm consists of clustering of a large population according to the distribution of the individuals and estimation of the implicit indices of an individual based on the similarity among individuals. In our experiments, we apply the proposed algorithm to an interior layout problem, a typical optimization problem with both interval parameters in the explicit index and interval uncertainty in the implicit index. Our experimental results confirm the feasibility and efficiency of the proposed algorithm.     

Memetic evolutionary training for recurrent neural networks: an application to time-series prediction

Abstract: Artificial neural networks are bio-inspired mathematical models that have been widely used to solve complex problems. The training of a neural network is an important issue to deal with, since traditional gradient-based algorithms become easily trapped in local optimal solutions, therefore increasing the time taken in the experimental step. This problem is greater in recurrent neural networks, where the gradient propagation across the recurrence makes the training difficult for long-term dependences. On the other hand, evolutionary algorithms are search and optimization techniques which have been proved to solve many problems effectively. In the case of recurrent neural networks, the training using evolutionary algorithms has provided promising results. In this work, we propose two hybrid evolutionary algorithms as an alternative to improve the training of dynamic recurrent neural networks. The experimental section makes a comparative study of the algorithms proposed, to train Elman recurrent neural networks in time-series prediction problems.     

A survey of repair methods used as constraint handling techniques in evolutionary algorithms

This paper provides a survey of the most important repair heuristics used in evolutionary algorithms to solve constrained optimization problems. Popular techniques are reviewed, such as some crossover operators in permutation encoding, algorithms for fixing the number of 1s in binary encoded genetic algorithms, and more specialized techniques such as Hopfield neural networks, heuristics for graphs and trees, and repair heuristics in grouping genetic algorithms. The survey also gives some indications about the design and implementation of hybrid evolutionary algorithms, and provides a revision of the most important applications in which hybrid evolutionary techniques have been used.     

Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks

In recent years, time series forecasting studies in which fuzzy time series approach is utilized have got more attentions. Various soft computing techniques such as fuzzy clustering, artificial neural networks and genetic algorithms have been used in fuzzy time series method to improve the method. While fuzzy clustering and genetic algorithms are being used for fuzzification, artificial neural networks method is being preferred for using in defining fuzzy relationships. In this study, a hybrid fuzzy time series approach is proposed to reach more accurate forecasts. In the proposed hybrid approach, fuzzy c-means clustering method and artificial neural networks are employed for fuzzification and defining fuzzy relationships, respectively. The enrollment data of University of Alabama is forecasted by using both the proposed method and the other fuzzy time series approaches. As a result of comparison, it is seen that the most accurate forecasts are obtained when the proposed hybrid fuzzy time series approach is used.