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Telemedicine provides remote online services for digital diagnosis and treatment via the Internet. However, there is a risk of data leakage during transmission. Therefore, data protection is an important challenge for telemedicine. Chaos is widely used in image, audio, and EEG encryption because of its unique characteristics of unpredictability, nonlinearity, and sensitivity to an initial state. However, some chaotic maps have various security issues. To solve these problems, this paper proposes a K-sine-transform-based coupled chaotic system (K-STBCCS), combining any two one-dimensional chaotic mappings to generate a new chaos mapping. To demonstrate the reliability of the system, this paper generates three new chaotic mappings using K-STBCCS and analyzes their performance. Using the chaotic mapping generated by K-STBCCS, this paper further proposes an EEG signal encryption scheme based on the confusion-diffusion principle. The purpose of confusion is to separate adjacent EEG signals, while the purpose of diffusion is to change the value of EEG signals. Among them, the diffusion operation uses positive and negative diffusion to reduce the correlation between the ciphertext and the original signals. The experimental results and security analysis show that the proposed EEG signal encryption scheme performs well and passes the rigorous cryptographic security test. [ABSTRACT FROM AUTHOR]

Telemedicine provides remote online services for digital diagnosis and treatment via the Internet. However, there is a risk of data leakage during transmission. Therefore, data protection is an important challenge for telemedicine. Chaos is widely used in image, audio, and EEG encryption because of its unique characteristics of unpredictability, nonlinearity, and sensitivity to an initial state. However, some chaotic maps have various security issues. To solve these problems, this paper proposes a K-sine-transform-based coupled chaotic system (K-STBCCS), combining any two one-dimensional chaotic mappings to generate a new chaos mapping. To demonstrate the reliability of the system, this paper generates three new chaotic mappings using K-STBCCS and analyzes their performance. Using the chaotic mapping generated by K-STBCCS, this paper further proposes an EEG signal encryption scheme based on the confusion-diffusion principle. The purpose of confusion is to separate adjacent EEG signals, while the purpose of diffusion is to change the value of EEG signals. Among them, the diffusion operation uses positive and negative diffusion to reduce the correlation between the ciphertext and the original signals. The experimental results and security analysis show that the proposed EEG signal encryption scheme performs well and passes the rigorous cryptographic security test. [ABSTRACT FROM AUTHOR]

• A many objective evolutionary algorithm based on decomposition and moth search is proposed. • Random and adaptive weights is used to break the limitation of uniform distribution weights. • Mutual evaluation value is used to evaluate the optimal individual in the neighborhood. • Improving scale factor α in MSA is to improve the performance of the proposed algorithm. Decomposition-based multi-objective evolutionary algorithm (MOEA/D) has good performance in solving multi-objective problems (MOPs) but poor performance in solving many-objective optimization problems (MaOPs). The weight vectors in MOEA/D are relatively fixed, which results in poor performance when dealing with complex MaOPs. In this paper, random and adaptive weights are introduced into MOEA/D to break the limitation of fixed weight vectors. And the moth search algorithm (MSA) is used as an operator to improve global search ability. The updating strategies in MSA are more consistent with the neighborhood learning strategy adopted in MOEA/D. In addition, to enable MSA to find the optimal solution in the neighborhood on the MaOPs to update other individuals. This paper introduces mutual evaluation value for evaluating the optimal individual in the neighborhood, and the proposed algorithm is abbreviated as MOEA/DMS. In comparative experiments on the MaF test suite, hypervolume (HV) and inverted generational distance (IGD) are used to measure MOEA/DMS and other many-objective evolutionary algorithms (MaOEAs). The results show that MOEA/DMS has an excellent performance in dealing with MaOPs. Besides, MOEA/DMS is compared with other state-of-the-art MaOEAs on two combinatorial MaOPs. The results show that MOEA/DMS also has significant advantages in dealing with combinatorial MaOPs. [ABSTRACT FROM AUTHOR]

Automatic clustering and dimension reduction are two of the most intriguing topics in the field of clustering. Affinity propagation (AP) is a representative graph-based clustering algorithm in unsupervised learning. However, extracting features from high-dimensional data and providing satisfactory clustering results is a serious challenge for the AP algorithm. Besides, the clustering performance of the AP algorithm is sensitive to preference. In this paper, an improved affinity propagation based on optimization of preference (APBOP) is proposed for automatic clustering on high-dimensional data. This method is optimized to solve the difficult problem of determining the preference of affinity propagation and the poor clustering effect for non-convex data distribution. First, t-distributed stochastic neighbor embedding is introduced to reduce the dimensionality of the original data to solve the redundancy problem caused by excessively high dimensionality. Second, an improved hybrid equilibrium optimizer based on the crisscross strategy (HEOC) is proposed to optimize preference selection. HEOC introduces the crisscross strategy to enhance local search and convergence efficiency. The benchmark function experiments indicate that the HEOC algorithm has better accuracy and convergence rate than other swarm intelligence algorithms. Simulation experiments on high-dimensional and real-world datasets show that APBOP has better effectiveness. [ABSTRACT FROM AUTHOR]

Velocity planning serves as an important issue in motion planning for autonomous vehicles. The presented paper proposes a novel velocity planning method with minimum moving time on the basis of path curvature which is accomplished in three steps. First, the assigned path is divided into some elementary parts based on the path curvature. Second, the velocity planning is transformed into an unconstrained optimization problem by assuming the velocity of vehicle to be a specific cubic polynomial on every elementary part to avoid a sudden acceleration in path switching. Finally, we use a modified projection particle swarm optimization (PPSO) algorithm to obtain the time-optimal velocity profile. The proposed method can generate a smooth time-optimal velocity profile while considering all possible relevant constraints. Three examples are provided on different types of path to demonstrate that the final velocity profile is efficient to avoid the sudden acceleration change. Furthermore, the modified PPSO algorithm in this paper is used to solve the optimization problem with high dimensional variables when its upper bound is known, which can not be achieved by the general PPSO algorithm. [ABSTRACT FROM AUTHOR]

Partial Label Learning (PLL) is a weakly supervised learning framework where each training instance is associated with more than one candidate label. However, label ambiguity in the case of label imbalance has not been well solved. In this paper, a novel label imbalance-aware algorithm named PL-LIMAP is proposed to effectively solve this problem. Firstly, a partial manifold constrained low-rank representation method is proposed to learn the instance affinity matrix, which can identify instances with the same label by fully mining the manifold structure of the data. Moreover, for making full use of the information hidden in candidate labels, the Jaccard distance is introduced to measure the similarity between candidate labels to obtain the label affinity matrix. The combination of these two affinity matrices can achieve the purpose of disambiguation through complementary information. Meanwhile, aiming to determine the imbalance of ambiguous labels, specialized measures are proposed to evaluate the imbalance level in PLL. According to the imbalance level of different labels, an alternative weighting scheme is proposed to mitigate the imbalance of ambiguous labels. As a consequence, the ground-truth label of an instance can be found out even though its label information is not available in advance. Experiments reveal the overwhelming performances of PL-LIMAP. [ABSTRACT FROM AUTHOR]

Solving nonlinear equation systems (NESs) is one of the most important tasks in numerical computing. The NESs usually have multiple roots, and quickly locating their roots in a single algorithm run with a limited number of iterations has always been the algorithm improvement direction. In order to further enhance the efficiency of the existing methods, a density clustering-based differential evolution algorithm (DCDE) for the NESs problem solving is proposed in this paper. Firstly, density-based spatial clustering of applications with noise (DBSCAN) is used to divide the population into clusters and noises, which provides a better direction for population evolution. Secondly, a cluster evaluation factor is proposed, which not only divides the clusters into excellent clusters and non-excellent clusters, but also prevents the migration of excellent clusters and maintain the diversity of populations. Then, a migration strategy and individual generation mechanism are proposed to guide non-excellent clusters to migrate to promising regions. Finally, combined with an archive technique, the individuals which satisfy the solution requirements are archived and randomly initialized to further improve population diversity. To verify the effectiveness of the proposed algorithm, comparative experimental results of the propsoed DCDE with other state-of-the-art algorithms for thirty NESs problems solving show that the proposed DCDE is the most effective algorithm to locate more roots in a single run. [ABSTRACT FROM AUTHOR]

In the realm of practical problem-solving, multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) are becoming increasingly prevalent. MOPRVIF involve determining the optimal decision variables that optimise multiple objectives, leveraging the relational data of a set of variables and multiple objectives. For these problems, this paper focuses on the following two issues: one is the demand for a unified computational model to solve this problem; the other is how to improve the algorithm's deep intelligent search capability. In this regard, this paper designs a dual data-driven multi-objective optimisation method. The method used consisted of four parts: elimination of redundant variables (ERV), objective function construction (OFC), selection evolution operator (SEO), and multi-objective evolutionary algorithm (MOEA). MOEA was the main focus of the method. ERV is data preparation and variable selection according to multiple objectives. OFC involves constructing the relationship model between variables and objectives, and a high-accuracy model is important for guaranteeing reliable results. Furthermore, SEO can adjust the evolution operator during a deep search. This is an important guarantee for deep, intelligent search. MOEA combined OFC and SEO to form the final solution algorithm—Dual Data Driven Multi-Objective Evolutionary Algorithm (DDMOEA). DDMOEA was explored using two different disciplinary problems of drug compound optimisation and wild blueberry cultivation and benchmarks were selected. The first two problem domains are distinct. The first problem is more complex than the second; however, both encompass redundant variables and indefinite objective functions. Benchmarks are utilised independently to gauge the profound intelligent search capability. The experiments affirm that the dual data-driven optimization approach proposed in this paper is effective, practical, and scalable. • Provide a unified solution for optimisation problems with redundant variables and indefinite objective functions. • Propose a data-driven multi-objective optimisation algorithm. • Designing an adaptive optimiser to enhance the algorithm's depth search capability. [ABSTRACT FROM AUTHOR]

K-nearest neighbors search (K-NNS) is a fundamental problem in many areas of machine learning and data mining. In an attempt to solve NNS problems by locality-sensitive hashing (LSH)-based algorithms and avoid the F1-trap, in this paper, we theoretically prove the necessity of maintaining close edges of each dimension, the independence of the partition location and the precision of retrieval. Based on the theories, a new method is proposed for constructing the hash function family. Then, a volume-balancing locality-sensitive hashing (VBLSH) algorithm is implemented. In this algorithm, the problem of constructing the hash function family is transformed into an optimization problem under a fixed number of hypercubes. By solving the optimization problem, different hyperplane intervals are set for each dimension to ensure that the hash functions can evenly segment the data space, where each segmented hypercube has a close volume and close side lengths. Finally, the analysis of the algorithm shows that the VBLSH algorithm has a high index coding efficiency and hash code calculation efficiency. The experimental results show that the VBLSH algorithm can simultaneously take into account the efficiency, precision and recall rate, and avoid the F1-trap; it shows excellent effectiveness on 4 datasets. [ABSTRACT FROM AUTHOR]

• A supervised multi-view model is proposed to realize a block-based feature selection. • The proposed model is composed of all sharing sub-models in each class. • The sparse regularizer can enhance the sparsity of blocks from features and views. • The proposed algorithm can realize the block separation and independent solution. • Numerical experiments show the effectiveness of our method on large-scale datasets. With the increasing scale of obtained multi-view data, how to deal with large-scale multi-view data quickly and efficiently is a significant problem. In this paper, a novel supervised multi-view feature selection method based on locally sparse regularization and block computing is proposed to solve the problem. Specifically, the multi-view dataset is firstly divided into sub-blocks according to classes and views. Then with the aid of the Alternating Direction Method of Multipliers (ADMM), a sharing sub-model is proposed to perform feature selection on each class by integrating each view's locally sparse regularizers and shared loss that makes all views share a common penalty and regresses samples to their labels. Finally, all the sharing sub-models are fused to form the final general additive feature selection model, in which each sub-block adjusts its corresponding variables to perform block-based feature selection. In the optimization process, the proposed model can be decomposed into multiple separate subproblems, and an efficient optimization algorithm is proposed to solve them quickly. The comparison experiments with several state-of-the-art feature selection methods show that the proposed method is superior in classification accuracy and training speed. [ABSTRACT FROM AUTHOR]

• purely distributed ensemble clustering method. • privacy preserving algorithm for diffused data. • collaborative solution with validated conflict resolution. • consensus among agents in the network. In this paper, we consider the problem of distributed unsupervised learning where data to be clustered are partitioned over a set of agents having limited connectivity. In order to solve this problem, we consider a novel and extended ensemble clustering procedure in order to make it suitable to a fully distributed scenario. The proposed algorithm can deal with the case where each agent has a local and different dataset. Additionally, to reduce the total amount of exchanged information, only the local prototypes of clusters are forwarded among the neighbors. Cluster similarity indexes are adopted to solve conflicts among agents and to achieve a common structure at the end of the communication process. The experimental results prove the feasibility of this approach, which is able to reach an optimal performance when compared to a fully centralized implementation, that is where data is collected beforehand on a single clustering agent. [ABSTRACT FROM AUTHOR]

Abstract In this paper we study the reachable region queries for objects moving in obstacle space. Loosely speaking, given any one of moving objects and many other information, we are asked to return the whole region in which each location has the non-zero probability to be reachable for the moving object. To solve this problem, this paper presents an efficient algorithm that runs in O (n log n) time and consumes O (n log n) space, where n is the number of vertices of obstacles. Moreover, this paper establishes the lower bound for the proposed problem, showing that our solution essentially is optimal in terms of random access memory (RAM) model. Empirical study based on both real and synthetic datasets demonstrates the effectiveness and efficiency of our solution. [ABSTRACT FROM AUTHOR]

In this paper, optimization of a linear objective function subject to a generalized fuzzy relational inequalities is investigated in which an arbitrary continuous t-norm is considered as fuzzy composition, and fuzzy inequality replaces ordinary inequality in the constraints. Unlike most optimization algorithms, in fuzzy relational inequalities with fuzzy constraints (FRI-FC) we find a near-feasible solution having a better objective value than those resulting from the resolution of the similar problems with crisp (ordinary) inequality constraints. Such solutions are called super-optima in this paper. Subsequently, an algorithm is proposed to find a super-optimum with pre-specified desirable infeasibility. For this purpose, we firstly study some structural properties of the FRI-FC problem and present a new formulization that is independent of t-norms used in the constraints of the problem. This new formulation converts the primary problem into an equivalent problem with simple constraints without considering any penalty parameters. However, it is proved that the transformed equivalent problem enables our algorithm to distinguish unfavorable points and generate a sequence of solutions converging to a super-optimum under a certain sufficient condition. Finally, a modified PSO is presented in which the ability of PSO to solve unconstrained problems with continuous domain, the structure of the transformed problem and some fuzzy structural modifications are combined and form an efficient algorithm to solve the generalized FRI-FC problems. The modified PSO algorithm has been applied to the generalized FRI-FC problem defined with ten well-known continuous t-norms. Additionally, an idea of the FRI-FC problems as outer approximators for FRI problems with ordinary inequalities is also investigated. [ABSTRACT FROM AUTHOR]

Rule learning is a special type of machine learning approaches, and its key advantage is the generation of interpretable models, which provides a transparent process of showing how an input is mapped to an output. Traditional rule learning algorithms are typically based on Boolean logic for inducing rule antecedents, which are very effective for training models on data sets that involve discrete attributes only. When continuous attributes are present in a data set, traditional rule learning approaches need to employ crisp intervals. However, in reality, problems usually show shades of grey, which motivated the development of fuzzy rule learning approaches by employing fuzzy intervals for handling continuous attributes. While a data set contains a large portion of discrete attributes or even no continuous attributes, fuzzy approaches cannot be used to learn rules effectively, leading to a drop in the performance. In this paper, a multi-stage approach of mixed rule learning is proposed, which involves strategic combination of both traditional and fuzzy approaches to handle effectively various types of attributes. We compare our proposed approach with existing algorithms of rule learning. Our experimental results show that our proposed approach leads to significant advances in the performance compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

• Modified compact utility list (MCUL) storage structure is built. • Backward checking is performed to reduce the search space and runtime. • Forward checking is combined with building candidates to reduce the runtime. • Similar transactions or closed transactions are merged to reduce storage space. • Values of LA-prune and C-prune are updated as soon as an MCUL is pruned. Mining closed high utility itemsets (CHUIs) involves finding a representative set of HUIs that is usually smaller than that of HUIs but can generate the full HUIs without loss of information. Researchers have therefore shown interest in this problem, and many methods have been proposed for mining CHUIs effectively, of which CHUI-Miner and EFIM-Closed are the two most efficient algorithms. However, these face performance issues when mining CHUIs from sparse datasets. In this paper, we propose a method for the effective mining of CHUIs in both dense and sparse datasets. We first modify the compact utility list structure in the HMiner algorithm to reduce the mining time, and then develop backward and forward checking methods using the most recently explored CHUIs and combine this with candidate building for the next levels. Finally, we apply pruning strategies to reduce the search space for the generation of CHUIs. Our experimental results show that the proposed algorithm, called HMiner-Closed, is more efficient than the state-of-the-art algorithms for both dense and sparse datasets. [ABSTRACT FROM AUTHOR]

In this paper we introduce a classifier named TEDAClass (Typicality and Eccentricity based Data Analytics Classifier) which is based on the recently proposed AnYa type fuzzy rule based system. Specifically, the rules of the proposed classifier are defined according to the recently proposed TEDA framework. This novel and efficient systematic methodology for data analysis is a promising addition to the traditional probability as well as to the fuzzy logic. It is centred at non-parametric density estimation derived from the data sample. In addition, the proposed framework is computationally cheap and provides fast and exact per-point processing of the data set/stream. The algorithm is demonstrated to be suitable for different classification tasks. Throughout the paper we give evidence of its applicability to a wide range of practical problems. Furthermore, the algorithm can be easily adapted to different classical data analytics problems, such as clustering, regression, prediction, and outlier detection. Finally, it is very important to remark that the proposed algorithm can work “from scratch” and evolve its structure during the learning process. [ABSTRACT FROM AUTHOR]

Highlights • A Quick Inclusion-Exclusion (QIE) technique is proposed. • The proposed QIE is for multi-state flow network reliability problems. • The proposed QIE is easy to code and only needs one comparision in each (intersection) term. • The proposed QIE is simpler than the traditional IE. • The proposed QIE is faster than the RSDP which is the most popular sum-of-disjoint product method. Abstract The reliability of modern information systems modeled as multi-state flow networks (MFNs) is a crucial concern the planning, design and control of these systems. The inclusion-exclusion technique (IET) is a popular tool for assessing MFN reliability because it is simple and easily understood. However, it is less efficient than other methods, like the sum-of-disjoint product method (SDP), for example. This paper proposes a new IET, called the Quick Inclusion-Exclusion technique (QIE) to increase the efficiency of the IET and reduce the amount of memory required in MFNs. The correctness and the time complexity of QIE is analyzed and proven. An MFN reliability example is implemented to illustrate the proposed QIE. In order to demonstrate its performance, the proposed QIE is compared with the most popular SDP, the recursive SDP (RSDP) in 20 benchmark networks taken from the literature. Numerical examples demonstrate that the proposed QIE outperforms RSDP in terms of both efficiency and memory use. This result differs significantly from those obtained by traditional methods. [ABSTRACT FROM AUTHOR]

Abstract Dynamic multiobjective optimization problems (DMOPs) are characterized by a time-variant Pareto optimal front (PF) and/or Pareto optimal set (PS). To handle DMOPs, an algorithm should be able to track the movement of the PF/PS over time efficiently. In this paper, a novel dynamic multiobjective evolutionary algorithm (DMOEA) is proposed for solving DMOPs, which includes a hybrid of memory and prediction strategies (HMPS) and the multiobjective evolutionary algorithm based on decomposition (MOEA/D). In particular, the resultant algorithm (MOEA/D-HMPS) detects environmental changes and identifies the similarity of a change to the historical changes, based on which two different response strategies are applied. If a detected change is dissimilar to any historical changes, a differential prediction based on the previous two consecutive population centers is utilized to relocate the population individuals in the new environment; otherwise, a memory-based technique devised to predict the new locations of the population members is applied. Both response mechanisms mix a portion of existing solutions with randomly generated solutions to alleviate the effect of prediction errors caused by sharp or irregular changes. MOEA/D-HMPS was tested on 14 benchmark problems and compared with state-of-the-art DMOEAs. The experimental results demonstrate the efficiency of MOEA/D-HMPS in solving various DMOPs. [ABSTRACT FROM AUTHOR]

Selecting the most appropriate algorithm to use when attempting to solve a black-box continuous optimization problem is a challenging task. Such problems typically lack algebraic expressions, it is not possible to calculate derivative information, and the problem may exhibit uncertainty or noise. In many cases, the input and output variables are analyzed without considering the internal details of the problem. Algorithm selection requires expert knowledge of search algorithm efficacy and skills in algorithm engineering and statistics. Even with the necessary knowledge and skills, success is not guaranteed. In this paper, we present a survey of methods for algorithm selection in the black-box continuous optimization domain. We start the review by presenting Rice’s (1976) selection framework. We describe each of the four component spaces – problem, algorithm, performance and characteristic – in terms of requirements for black-box continuous optimization problems. This is followed by an examination of exploratory landscape analysis methods that can be used to effectively extract the problem characteristics. Subsequently, we propose a classification of the landscape analysis methods based on their order, neighborhood structure and computational complexity. We then discuss applications of the algorithm selection framework and the relationship between it and algorithm portfolios, hybrid meta-heuristics, and hyper-heuristics. The paper concludes with the identification of key challenges and proposes future research directions. [ABSTRACT FROM AUTHOR]

Recently, event-based social networks (EBSNs) have become popular, hence how to organize a social event has received significant attention. Most of prior studies about social events organization usually consider the willingness of attendees and their relationships. However, they ignore the roles of attendees. In fact, many social events have requirement of attendees roles in the real world. In this paper, we propose to study the problem of Multi-Role Social Event Organization (MRSEO). Our goal is to maximize the overall harmony of the social event while considering multiple factors, such as attendees’ roles, willingness and their relationships. To solve the problem, we propose two algorithms. Firstly, we propose a continuous relaxation technique based algorithm, called MRSEO-CRA. It converts the problem of MRSEO to an equivalent unconstrained continuous problem, and then employs RatioDCA algorithm to solve the converted one. Secondly, to better trade off between performance and running time, we further propose the other algorithm based on improved PageRank, called MRSEO-IPR. We conduct extensive experiments on real-world datasets to evaluate these two proposed algorithms and experimental results show that our algorithms outperform the state-of-the-art algorithm in terms of performance and running time. [ABSTRACT FROM AUTHOR]

Mining high utility patterns is an important data mining problem that is formulated as finding patterns whose utilities are no less than a threshold. As the mining results are very sensitive to such a threshold, it is difficult for users to specify an appropriate one. An alternative formulation of the problem is to find the top- n high utility patterns. However, the second formulation is more challenging because the corresponding threshold is unknown in advance and the solution search space becomes even larger. When there are very long patterns prior algorithms simply cannot work to mine top- n high utility patterns even for very small n . This paper proposes a novel algorithm for mining top- n high utility patterns that are long. The proposed algorithm adopts an opportunistic pattern growth approach and proposes five opportunistic strategies for scalably maintaining shortlisted patterns, for efficiently computing utilities, and for estimating tight upper bounds to prune search space. Extensive experiments show that the proposed algorithm is 1 to 3 orders of magnitude more efficient than the state-of-the-art top- n high utility pattern mining algorithms, and it is even up to 2 orders of magnitude faster than high utility pattern mining algorithms that are tuned with an optimal threshold. [ABSTRACT FROM AUTHOR]

Attribute reduction is a key aspect of Rough Set Theory. Finding the complete set of reducts is important for solving problems such as the assessment of attribute relevance, multi–objective cost–sensitive attribute reduction and dynamic reduct computation. The main limitation in the application of Rough Set methods is that finding all reducts of a decision system has exponential complexity regarding the number of attributes. Several algorithms have been reported to reduce the cost of reduct computation. Unfortunately, most of these algorithms relay on high cost operations for candidate evaluation. Therefore, in this paper, we propose a new algorithm for computing all reducts of a decision system, based on the pruning properties of gap elimination and attribute contribution , that uses simpler operations for candidate evaluation in order to reduce the runtime. Finally, the proposed algorithm is evaluated and compared with other state of the art algorithms, over synthetic and real decision systems. [ABSTRACT FROM AUTHOR]

k -means is a preeminent partitional based clustering method that finds k clusters from the given dataset by computing distances from each point to k cluster centers iteratively. The filtering algorithm improves the performance of k -means by imposing an index structure on the dataset and reduces the number of cluster centers searched while finding the nearest center of a point. The performance of filtering algorithm is influenced by the degree of separation between initial cluster centers. In this paper, we propose an efficient initial seed selection method, RDBI, to improve the performance of k -means filtering method by locating the seed points at dense areas of the dataset and well separated. The dense areas are identified by representing the data points in a kd -tree. A comprehensive experimental analysis is performed to evaluate the performance efficiency of proposed method against state-of-the-art initialization methods and shown that the proposed method is efficient in terms of both running time and clustering accuracy. [ABSTRACT FROM AUTHOR]

Over the last two decades, many different differential evolution algorithms for solving optimization problems have been introduced. Although most of these algorithms used a single mutation strategy, several with multiple mutation strategies have recently been proposed. Multiple-operator-based algorithms have been proven to be more effective and efficient than single-operator-based algorithms for solving a wide range of benchmark and practical problems. In these algorithms, adaptive operator selection mechanisms are generally applied to place greater emphasis on the best-performing evolutionary operators based on their performance histories for generating new offspring. In this paper, we investigate using problem landscape information in an adaptive operator selection mechanism. For this purpose, a new algorithm, which considers both this problem landscape information and the performance histories of the operators, for dynamically selecting the most suitable differential evolution operator during the evolutionary process, is proposed. The contributions of each component of the selection mechanism are analyzed and the performance of the proposed algorithm is evaluated by solving 45 unconstrained optimization problems. The results demonstrate the effectiveness and superiority of the proposed algorithm to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Differential Evolution (DE) is one of the most popular optimization methods for real-valued problems and a large number of its variants have been proposed so far. However, bringing together different ideas that already led to successful DE versions is rare in the literature. In the present paper a novel DE algorithm is proposed, in which three among the most efficient concepts already applied separately within DE framework are gathered together, namely: (1) the adaptation of algorithm control parameters and probabilities of using different mutation strategies; (2) the use of Nelder–Mead algorithm as a local search method hybridized with DE; and (3) the splitting mutation into Global and Local models, when Local mutation model is based on the concept of neighborhood of individuals organized on a ring topology. The performance of the novel algorithm, called Adaptive Memetic DE with Global and Local neighborhood-based mutation operators is compared with 13 different DE variants on a set of 25 popular problems which include rotated, shifted and hybrid composition functions. It is found that, although none DE algorithm outperforms all the others for the majority of problems, on average the proposed approach perform better than all 13 DE algorithms selected for comparison. The proposed algorithm is another heuristic approach developed to solve optimization problems. The question may arise, whether proposing novel methods is useful as No Free Lunch theorems for optimization state that the expected performance of all possible heuristics on all possible problems is equal. In the last section of the paper the limitations and implications of No Free Lunch theorems are discussed based on rich, but unfortunately frequently neglected literature. A very simple continuous and differentiable minimization problem is proposed, for which it is empirically verified that each among considered 14 DE algorithms perform poorer than random sampling. It is also empirically shown that when all considered DE algorithms search for the maximum of the proposed problem, they found lower minimum than DE algorithms searching for the minimum. Such result is not unexpected according to No Free Lunch theorems and should be considered as a precaution from generalization of good performance of heuristic optimization methods. [Copyright &y& Elsevier]

Abstract: Researchers in the past always laid more emphasis on constructing models, assessing model risks and developing algorithms to solve various models, paying little attention to the research on the value ranges of risks for portfolio models. This paper attempts to fill this gap by presenting some practical approaches to obtain new and accurate results on value ranges of risks for the traditional mean–variance portfolio models. The accurate upper and lower bounds are identified for the minimizing risk portfolio models with or without short selling. A rigorous mathematical proof is utilized to derive necessary and sufficient conditions of the equal weight portfolio model that is equivalent to the minimizing risk portfolio model. Two numerical examples are given to verify the effectiveness and correctness of the theorems and corollaries discussed in this paper. [Copyright &y& Elsevier]

With the increasing popularity of online social networks, people today typically own several such accounts (e.g., Facebook, Twitter, and Flickr). In this scenario, it arises an interesting and challenging problem: how to identify the same person in different social networks, which is known as the network reconciliation problem. Prior work on this problem first assumes that the relationships between users are homogeneous, and then makes use of the local features (degree, common mapped neighbors) to achieve high precision. However, this assumption does not hold in reality since users usually have different tie strengths between each other. In this paper, we remodel the reconciliation problem by considering the users’ heterogeneous relationships and propose a unified framework called UniRank for incorporating the local features and global features together. Based on UniRank, we design an efficient two-stage network reconciliation algorithm. First, we design a global matching algorithm to explore more seeds with fast speed. Second, for each explored seed, we design a breadth-first strategy based local matching algorithm to match more seeds. Extensive simulations on real-world and synthetic social network datasets show that our algorithm significantly improves the state-of-art algorithm by up to 9 X in terms of F 1 score even under very rough conditions. [ABSTRACT FROM AUTHOR]

Proliferation of multi-national corporations and extremely competitive business environments have led to an unprecedented demand for third-party logistics services. However, recent studies on the vehicle routing problem (VRP) have considered only simple constraints. They also do not scale well to real-world problems that are encountered in the logistics industry. In this paper, we introduce a novel vehicle routing problem with time window and pallet loading constraints; this problem accounts for the actual needs of businesses in the logistics industry such as the delivery of consumer goods and agricultural products. To solve this new VRP, we propose a hybrid approach by combining Tabu search and the artificial bee colony algorithm. A new benchmark data set is generated to verify the performance of the proposed algorithm because the proposed VRP has never been reported in the literature. Experiments are performed for a data set of Solomon's 56 vehicle routing problem with time windows. Our approach is superior to a number of other heuristic algorithms in a comparison on Solomon's VRPTW instances. [ABSTRACT FROM AUTHOR]

Multiple-Instance Learning (MIL) refers to the problem wherein each object is a bag consisting of multiple instances and only the bags' labels are provided. MIL data can contain irrelevant, redundant, and noisy components, which makes feature-extraction preprocessing essential for performance improvement. In this paper, we propose a Multiple-Instance Feature Extraction (MIFE) framework to design algorithms at both the bag and instance levels based on the Maximum Trace-Difference criterion, which simultaneously maximizes between-class scattering and minimizes within-class scattering. MIFE not only treats the existing Multiple-Instance Discriminant Analysis algorithm as an instance-level realization but also enables us to adopt different bag-level distances to design corresponding bag-level algorithms. In particular, we introduce the Class-to-Bag (C2B) and Bag-to-Bag (B2B) distances into the MIFE framework and obtain the MIFE-C2B and MIFE-B2B algorithms, respectively. The experimental results show that both MIFE-C2B and MIFE-B2B obtain competitive classification performance, and MIFE-B2B obtains the best performance on most tested datasets. The dimensionality reduction results show that both MIFE-C2B and MIFE-B2B obtain their best performance with no more than approximately 30% of the original dimensions on most tested datasets. [ABSTRACT FROM AUTHOR]

Abstract: Teaching–Learning-Based Optimization (TLBO) seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances. It is reported that it outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. Such a breakthrough has steered us towards investigating the secrets of TLBO’s dominance. This paper reports our findings on TLBO qualitatively and quantitatively through code-reviews and experiments, respectively. Our findings have revealed three important mistakes regarding TLBO: (1) at least one unreported but important step; (2) incorrect formulae on a number of fitness function evaluations; and (3) misconceptions about parameter-less control. Additionally, unfair experimental settings/conditions were used to conduct experimental comparisons (e.g., different stopping criteria). The experimental results for constrained and unconstrained benchmark functions under fairly equal conditions failed to validate its performance supremacy. The ultimate goal of this paper is to provide reminders for metaheuristics’ researchers and practitioners in order to avoid similar mistakes regarding both the qualitative and quantitative aspects, and to allow fair comparisons of the TLBO algorithm to be made with other metaheuristic algorithms. [Copyright &y& Elsevier]

Ordered data widely exists in practical problems. Dominance-based Rough Set Approach (DRSA) is an effective mathematical tool to obtain approximations of concepts and discovery knowledge from ordered data. In this paper, we focus on the dynamic DRSA for the multi-dimensional variation of an ordered information system, and propose a novel incremental simplified algorithm which can efficiently update approximations of DRSA when objects and attributes increase simultaneously. Most of existing algorithms can efficiently deal with the single-dimensional variation of an information system. However, multi-dimensional variations often occur in real dynamic data. That is, the object set, the attribute set or attribute values of an information system often vary simultaneously. Although we can directly use the definitions of approximations, or integrate some single-dimensional incremental algorithms to cope with multi-dimensional variations, this always results in complex algorithm architectures and a large amount of inefficient computation. In our works, by simplifying traditional definitions of DRSA and employing the incremental learning strategy, we develop an algorithm to neglect unnecessary parameters and avoid much redundant computation. Then, we present two different storage schemes for our proposed algorithm to solve the problem of memory consumption. Finally, a series of experiments are conducted to evaluate its efficiency. Experimental results clearly show that for the two-dimensional variation of objects and attributes in an ordered information system, our proposed algorithm is much faster not only than the non-incremental algorithm based on traditional definitions, but also than the integration of two single-dimensional incremental algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the group decision making (GDM) problems with interval-valued Atanassov intuitionistic fuzzy preference relations (IV-AIFPRs) and develops a novel method for solving such problems. A new consistency index of an Atanassov intuitionistic fuzzy preference relation (AIFPR) is introduced to judge the consistency of an AIFPR and then a convergent iterative Algorithm I is designed to repair the consistency of an AIFPR with unacceptable consistency. Subsequently, the consistency and acceptable consistency of an IV-AIFPR are defined through separating an IV-AIFPR into two AIFPRs. Based on Algorithm I, a new iterative Algorithm II is devised to repair the consistency of an IV-AIFPR with unacceptable consistency. Afterwards, to determine decision makers’ (DMs’) weights objectively, an optimization model is established by minimizing the deviations between each individual IV-AIFPR and the collective one. This model is skillfully transformed into a linear goal program to resolve sufficiently considering different principles of decision making. A linear program is built to derive interval-valued Atanassov intuitionistic fuzzy (IVAIF) priority weights of alternatives. Then, a TOPSIS (technique for order performance by similarity to an ideal solution) based approach is proposed to rank such IVAIF priority weights. Thereby, a method for GDM with IV-AIFPRs is put forward. At length, a practical example of a virtual enterprise partner selection is provided to illustrate the feasibility and validity of the proposed method. [ABSTRACT FROM AUTHOR]

Scheduling problems with resource constraints have been a new research trend in recent years. This paper addresses a multi-resource-constrained flexible job shop scheduling problem that is very common in semiconductor manufacturing, precision engineering, and many other modern industries. To address this important problem, a novel algorithm called the shuffled multi-swarm micro-migrating birds optimization (SM 2 -MBO) algorithm is presented with a two-vector representation. The SM 2 -MBO forms a number of micro-swarms, each of which performs its own MBO independently. A random shuffle process applied to the entire population is invoked periodically to propagate the good information that is found in some of the micro-swarms. A diverse controlling strategy based on the aging phenomenon of life is proposed to diversify the population. An adaptive search operator based on a problem-specific crossover and a two-vector crossover helps to balance exploitation and exploration. Numerical experiments and comparisons are conducted against the best performing algorithms reported in the literature for the considered problem. The results demonstrate that the proposed SM 2 -MBO performs significantly better than the existing algorithms in solving the multi-resource-constrained flexible job shop scheduling problem with the makespan criterion. Furthermore, the proposed SM 2 -MBO can improve 9 out of 10 best known solutions for the benchmark instances in the literature. [ABSTRACT FROM AUTHOR]

The minimum weighted vertex cover (MWVC) problem, an extension of the classical minimum vertex cover (MVC) problem, is an important NP-complete combinatorial optimization problem with a wide range of applications. The objective of this paper is to design an efficient local search algorithm to solve the MWVC problem. First, the weighted edge strategy is proposed to define the dynamic scoring strategy so that our algorithm can find different possible optimal solutions. Second, the weighted configuration checking (WCC) strategy is proposed to overcome the cycling problem in local search. By combining the WCC strategy with the scoring strategy, we design the vertex selection strategy to determine the vertex to be selected as a candidate solution component. Based on these strategies, a novel local search framework, namely diversion local search based on weighted configuration checking (DLSWCC), is presented. DLSWCC is evaluated against several state-of-the-art algorithms on various benchmark instances. Experimental results show that DLSWCC outperforms its competitors in terms of both solution quality and computational efficiency in most classical instances. Specifically, DLSWCC can obtain 22 new upper bounds of 71 moderate-scale problem instances, 5 new upper bounds of 15 large-scale problem instances, and 56 new upper bounds of 56 massive graph instances. [ABSTRACT FROM AUTHOR]

According to the literature, a linear optimization problem subjected to a system of bipolar fuzzy relational equations with max-Łukasiewicz composition can be translated into a 0–1 integer linear programming problem and solved using integer optimization techniques. However, the technique of integer optimization may involve hight computation complexity. To improve computational efficiency for solving such an optimization problem, this paper proves that each component of an optimal solution obtained from such an optimization problem can either be the corresponding component’s lower bound or upper bound value. Because of this characteristic, a simple value matrix with some simplified rules can be proposed to reduce the problem size first. A simple solution procedure is then presented for determining optimal solutions without translating such an optimization problem into a 0–1 integer linear programming problem. Two examples are provided to illustrate the simplicity and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: The k-covers problem (kCP) asks us to compute a minimum cardinality set of strings of given length k >1 that covers a given string. It was shown in a recent paper, by reduction to 3-SAT, that the k-covers problem is NP-complete. In this paper we introduce a new problem, that we call the k-bounded relaxed vertex cover problem (RVCP k ), which we show is equivalent to k-bounded set cover (SCP k ). We show further that kCP is a special case of RVCP k restricted to certain classes G x ,k of graphs that represent all strings x . Thus a minimum k-cover can be approximated to within a factor k in polynomial time. We discuss approximate solutions of kCP, and we state a number of conjectures and open problems related to kCP and G x ,k . [Copyright &y& Elsevier]

Software systems tend to become larger and more complicated as the functional requirements increase gradually. Well-modularized software systems are widely believed to be understood, managed and evolved easily. Software module clustering problems (SMCPs) are significant and challenging problems in software engineering whose primary aim is to obtain perfect structures of software systems according to predefined rules. To solve SMCPs, it is important to design a measure which can appropriately evaluate the quality of the structures obtained. So far, the modularization quality (the basic MQ ) is widely used as the conventional measure. However, this measure does not consider global modules and edge directions between two modules. Therefore, in this paper, we design a new modularization quality measure based on the similarity, labeled as MS , to automatically guide optimization algorithms to find a good partition of software systems which considers both global modules and edge directions. To systematically validate the performance of MS , three optimization algorithms, namely hill-climbing algorithm (HC), genetic algorithm (GA), and multi-agent evolutionary algorithm (MAEA), are adopted to optimize MS on 17 real-world software systems with varying sizes. A thorough comparison shows that MS outperforms the basic MQ in guiding the optimization algorithms to find more meaningful module clusters for software systems. [ABSTRACT FROM AUTHOR]

Compared with the Pawlak rough set model, decision-theoretic rough set (DTRS) models have relatively powerful data processing capability for complex and low quality data. However, the non-monotonicity of the criteria used in DTRS models causes many problems, particularly the inefficiency of attribute reduction algorithms, which greatly narrows their applications. Few systematic studies have been reported on how to construct efficient reduction methods for DTRS models. This paper discusses this problem and constructs an efficient reduction algorithm for DTRS models incorporating three aspects: taking advantage of common characteristics of data sets and employing division techniques, a fast division approach to computing equivalence classes is proposed, which is the most frequently used and the most time-consuming basic operation in the reduction algorithm; extracting a “monotonic ingredient” from decision systems, an effective heuristic function is constructed for the reduction algorithm, which can guide searches for the algorithm and has better ability to find shorter super-reducts; a novel algorithm is proposed to search the power set space of a given super-reduct, which has a shorter average search length than the existing backtracking methods. Finally, the reduction algorithm is proposed that combines these improvements. Experimental results demonstrate that these improvements are effective and the proposed reduction algorithm is relatively efficient. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of recovering a tensor with missing data. We propose a new model combining the total variation regularization and low-rank matrix factorization. A block coordinate decent (BCD) algorithm is developed to efficiently solve the proposed optimization model. We theoretically show that under some mild conditions, the algorithm converges to the coordinate wise minimizers. Experimental results are reported to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic mentoring scheme along with a self-regulation scheme have been incorporated in the standard Particle Swarm Optimization (PSO) algorithm to empower the searching particles with human-like characteristics. The algorithm is referred to as a Dynamic Mentoring and Self-Regulation based Particle Swarm Optimization (DMeSR-PSO) algorithm. Based on their experiences, the particles are divided into three groups, viz., the mentor group, the mentee group and the independent learner group where the number of particles in each group is dynamically changing in every iteration. In human learning psychology, mentoring is regarded as a powerful and effective learning process and independent learners are the ones who do not need mentoring and are capable of performing self-regulation of their own knowledge. Therefore, the particles in each of the above three groups have different learning strategies for their velocity updates where the mentors are equipped with a strong self-belief based search, the mentees are taking guidance from the mentors and the independent learners employ self-perception strategy. The DMeSR-PSO algorithm has been extensively evaluated using the simple unimodal and multimodal benchmark functions from CEC2005, more complex shifted and rotated benchmark functions fromCEC2013 and also based on eight real- world problems from CEC2011. The results have been compared with six state-of-the-art PSO variants and five meta-heuristic algorithms for the CEC2005 problems. Further, a comparative analysis on CEC2013 benchmark functions with different PSO variants has also been presented. Finally, DMeSR-PSO's performance on the real-world problems is compared with the top two algorithms from the CEC2011 competition. The results indicate that the proposed learning strategies help DMeSR-PSO to achieve faster convergence and provide better solutions in most of the problems with a 95% confidence level, yielding an effective optimization algorithm for real-world applications. [ABSTRACT FROM AUTHOR]

The problem of conditional distributivity for different two aggregation operators plays an important role in many different fields such as utility theory and integration theory. This paper deals with the conditional distributivity of a continuous nullnorm with respect to a uninorm with the continuous underlying t-norm and t-conorm. [ABSTRACT FROM AUTHOR]

This paper introduces a hierarchical clustering algorithm in networks based upon a first divisive stage to break the graph and a second linking stage which is used to join nodes. As a particular case, this algorithm is applied to the specific problem of community detection in social networks, where a betweenness measure is considered for the divisive criterion and a similarity measure associated to data is used for the linking criterion. We show that this algorithm is very flexible as well as quite competitive (from both a performance and a computational complexity point of view) in relation with a set of state-of-the-art algorithms. Furthermore, the output given by the proposed algorithm allows to show in a dynamic and interpretable way the evolution of how the groups are split in the network. [ABSTRACT FROM AUTHOR]

Ideally, document clustering methods should produce clusters that are semantically relevant and readily understandable as collections of documents belonging to particular contexts or topics. However, existing popular document clustering methods often ignore term-document corpus-based semantics while relying upon generic measures of similarity. In this paper, we present CDIM, an algorithmic framework for partitional clustering of documents that maximizes the sum of the discrimination information provided by documents. CDIM exploits the semantic that term discrimination information provides better understanding of contextual topics than term-to-term relatedness to yield clusters that are describable by their highly discriminating terms. We evaluate the proposed clustering algorithm using well-known discrimination/semantic measures including Relative Risk (RR), Measurement of Discrimination Information (MDI), Domain Relevance (DR), and Domain Consensus (DC) on twelve data sets to prove that CDIM produces high-quality clusters comparable to the best methods. We also illustrate the understandability and efficiency of CDIM, suggesting its suitability for practical document clustering. [ABSTRACT FROM AUTHOR]

Recent years have witnessed unprecedented volumes of structured data published in RDF format. Taking full advantage of such data has attracted a growing amount of research interest from both academia and industry. However, efficient processing of top- k queries in RDF data is still a new topic. Most existing approaches ignore top- k queries, or only provide a limited number of ranking functions. In this paper, we provide an effective and efficient processing algorithm for top- k queries that consists of a novel tree-style index MS-tree and MS-tree-based filtering and pattern-matching functions. Firstly, candidate entities in RDF data were efficiently ranked and filtered through an MS-tree-based top-down method. Then, query structure patterns are matched in RDF data through graphic exploration. In order to handle more complex scoring functions, a dynamic variable selecting optimization is employed to accelerate the threshold decrease. We evaluate our solutions with both synthetic and real-world datasets. The experimental results show that our model significantly outperforms state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Data clustering aims to segment a database into groups of objects based on the similarity among these objects. Due to its unsupervised nature, the search for a good-quality solution can become a complex process. There is currently a wide range of clustering algorithms, and selecting the best one for a given problem can be a slow and costly process. In 1976, Rice formulated the Algorithm Selection Problem (ASP), which postulates that the algorithm performance can be predicted based on the structural characteristics of the problem. Meta-learning brings the concept of learning about learning; that is, the meta-knowledge obtained from the algorithm learning process allows the improvement of the algorithm performance. Meta-learning has a major intersection with data mining in classification problems, in which it is normally used to recommend algorithms. The present paper proposes new ways to obtain meta-knowledge for clustering tasks. Specifically, two contributions are explored here: (1) a new approach to characterize clustering problems based on the similarity among objects; and (2) new methods to combine internal indices for ranking algorithms based on their performance on the problems. Experiments were conducted to evaluate the recommendation quality. The results show that the new meta-knowledge provides high-quality algorithm selection for clustering tasks. [ABSTRACT FROM AUTHOR]

An essential assumption in most algorithms in computational geometry is that the input data is precise which is not always true in real world problems. In this paper we introduce a model – called λ-geometry model – for handling a dynamic form of imprecision which allows the precision changes in the input data of geometric problems. Also, we study two families of basic computational geometry problems under this model: axis-aligned bounding box (AABB) and proximity problems. For a set of n imprecise points where each point is modeled by a convex polygonal region with O( k ) representation complexity, we propose an O( n (log n + log k + m )) time algorithm to solve the largest AABB problem, where m is the maximum number of corner defining functions when the largest AABB is defined by points on its corners. Also, we propose an O( n log n + nk + k 2 log k α ( k )) time algorithm for finding the smallest AABB under the assumption that the regions are disjoint. We show that the algorithms are optimal when k is O(log n ). Further, we propose O( n 2 k log( nk ) α ( nk )) time algorithms for finding the closest and furthest pair problems, and present a lower bound of Ω( n 2 ) for the closest pair problem when k is constant. [ABSTRACT FROM AUTHOR]

Abstract: Entity resolution is the problem of identifying the tuples that represent the same real world entity. In this paper, we propose a complete solution to the problem of entity resolution over probabilistic data (ERPD), which arises in many applications that have to deal with probabilistic data. To deal with the ERPD problem, we distinguish between two classes of similarity functions, i.e. context-free and context-sensitive. We propose a PTIME algorithm for context-free similarity functions, and an approximation algorithm for context-sensitive similarity functions. We validated our algorithms through experiments over both synthetic and real datasets. Our extensive performance evaluation shows the effectiveness of our algorithms. [Copyright &y& Elsevier]

Abstract: Capacitated Arc Routing Problem (CARP) has drawn much attention during the last few years. In addition to the goal of minimizing the total cost of all the routes, many real-world applications of CARP also need to balance these routes. The Multi-objective CARP (MO-CARP) commonly exists in practical applications. In order to solve MO-CARP efficiently and accurately, this paper presents a Multi-population Cooperative Coevolutionary Algorithm (MPCCA) for MO-CARP. Firstly, MPCCA applies the divide-and-conquer method to decompose the whole population into multiple subpopulations according to their different direction vectors. These subpopulations evolve separately in each generation and the adjacent subpopulations can share their individuals in the form of cooperative subpopulations. Secondly, multiple subpopulations are used to search different objective subregions simultaneously, so individuals in each subpopulation have a different fitness function, which can be modeled as a Single Objective CARP (SO-CARP). The advanced MAENS approach for single-objective CARP can be used to search each objective subregion. Thirdly, the internal elitism archive is used to construct evolutionary pool for each subregion, which greatly speeds up the convergence. Lastly, the fast nondominated ranking and crowding distance of NSGA-II are used for selecting the offspring and keeping the diversity. MPCCA is tested on 91 CARP benchmarks. The experimental results show that MPCCA obtains better generalization performance over the compared algorithms. [Copyright &y& Elsevier]

Abstract: In recent years, many different variants of the particle swarm optimizer (PSO) for solving optimization problems have been proposed. However, PSO has an inherent drawback in handling constrained problems, mainly because of its complexity and dependency on parameters. Furthermore, one PSO variant may perform well for some test problems but not obtain good results for others. In this paper, our purpose is to develop a new PSO algorithm that can efficiently solve a variety of constrained optimization problems. It considers a mix of different PSO variants each of which evolves with a different number of individuals from the current population. In each generation, the algorithm assigns more individuals to the better-performing variants and fewer to the worse-performing ones. Also, a new PSO variant is developed for use in the proposed algorithm to maintain a better balance between its local and global PSO versions. A new methodology for adapting PSO parameters is presented and the proposed self-adaptive PSO algorithm tested and analyzed on two sets of test problems, namely the CEC2006 and CEC2010 constrained optimization problems. Based on the results, the proposed algorithm shows significantly better performance than the same global and local PSO variants as well as other-state-of-the-art algorithms. Although, based on our analysis, it cannot guarantee an optimal solution for any unknown problem, it is expected to be able to solve a wide variety of practical problems. [Copyright &y& Elsevier]

Abstract: Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH). This paper introduces the chaos theory into the KH optimization process with the aim of accelerating its global convergence speed. Various chaotic maps are considered in the proposed chaotic KH (CKH) method to adjust the three main movements of the krill in the optimization process. Several test problems are utilized to evaluate the performance of CKH. The results show that the performance of CKH, with an appropriate chaotic map, is better than or comparable with the KH and other robust optimization approaches. [Copyright &y& Elsevier]