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Recently a number of evolutionary multiobjective optimization algorithms have been proposed in the framework of MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition). A multiobjective problem is decomposed into multiple single-objective problems using a set of weight vectors in MOEA/D. The number of single-objective problems is the same as the number of weight vectors, which is also...
This article explores the order batching problem (OBP), in warehouse of e-commerce companies. Based on the real E-commence warehouse case, we present a valid tabu search(TS) algorithm to determine how to group the orders in batches, with a greed-based seed heuristic method generating its initial solution. In tabu search, a modified combined picker routing algorithm for the multiple-cross-aisle picker...
Due to the increasing of the size of the datasets, techniques for instance selection have been applied for reducing the data to a manageable volume, leading to a reduction of the computational resources that are necessary for performing the learning process. Besides that, algorithms of instance selection can also be applied for removing useless, erroneous or noisy instances, before applying learning...
Anomaly detection technique play an extraordinary role in the Intrusion Detection System (IDS) for its ability to detect novel attacks. To overcome the high-dimensionality problem the anomaly detection cursed of, we propose a novel Meta-Heuristic-based Sequential Forward Selection (MH_SFS) feature selection algorithm, which can be generally implemented in anomaly detection system. It is an improvement...
Traditional classification algorithms addressing imbalanced-class dataset mostly concentrate on the majority classes' accuracy, such that the minority class's accuracy is usually ignored. Focusing on this issue, we propose a novel classification algorithm using Ensemble Feature Selections (EFS) for imbalanced-class dataset. This algorithm utilizes the superiority of EFS in accuracy, then considers...
Clustering is grouping similar data items, features, observations etc. In to cluster. Clustering Problem has been addressed many times as it is one of the important step in data analysis in various application areas. This paper presents an overview of message passing data clustering technique with a goal of providing useful concepts which can be accessible to the community of clustering practitioners...
This paper develops a new fuzzy harmony search algorithm (FHS) for solving optimization problems. FHS employs a novel method using fuzzy logic for adaptation of the harmony memory accepting parameter that enhances the accuracy and convergence rate of the harmony search (HS) algorithm. In this paper the impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning...
In recent years researchers have tried to apply Stochastic Algorithms for solving Optimization problems. Some of these algorithms like Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Immune Systems (AIS) are more known because of their significant abilities in finding optimal solutions of the problems comparing to others. Although these algorithms...
In literature, there are many supervised learning algorithms presented and applied in various problem domains. However, none of them could consistently perform well over all the datasets. This paper presents a novel approach for simultaneous selection of optimal feature subset and classifier for a given dataset. For large scale problems, this would require to search a huge solution space. Therefore,...
This paper proposes to use multi objective nonlinear functions with Tabu Search Algorithm (TSA) to design a fractional order proportional integral derivative (FOPID) controller. Classical TSA modified to satisfy the maximum number of multi nonlinear functions that define important criteria for FOPID design. Simulation result exhibits the performance of the algorithm.
Finding an appropriate set of features from data of high dimensionality for building an accurate classification model is a well-known NP-hard computational problem. Unfortunately in data mining, some big data are not only big in volume but they are described by a large number of features. Many feature subset selection algorithms have been proposed in the past, they are nevertheless far from perfect...
The maximum clique question is a paradigmatic combinatorial optimization. In order to get better solution, cuckoo search, a new meta-heuristic method was introduced. The cuckoo search was used to get better candidate solutions in this hybrid algorithm. Then, hybrid algorithm is compared with VNS and SA by standard DIMACS data sets, result shows that hybrid algorithm can get better solution in most...
Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. In feature selection algorithms search strategies are key aspects. Since feature selection is an NP-Hard problem; therefore heuristic algorithms have been studied to solve this problem. In this paper, we have proposed a method based on memetic algorithm...
In this paper, membership function shapes and types and the fuzzy rules of fuzzy systems are adjusted by using a Intelligent Gravitational Search Algorithm (IGSA) in order to obtain an optimal fuzzy system. The advantages of this method in classifying various data sets are illustrated. Possible extensions of this technique are briefly discussed.
In the paper a novel cuckoo search (CS) algorithm based on the idea of opposition (OCS) algorithm is proposed and attempts to increase the exploration efficiency of the solution space in order to solve optimization problems. the modifications focus on the solution construction phase of the CS algorithm, which organically merges the opposition-based learning into CS algorithm and the OCS algorithm...
Bayesian network (BN) structure learning is an NP hard problem. Search and score algorithms are one of the main approaches proposed for learning BN structure from data. Previous research has shown that the relative performances of such algorithms are problem dependent and that fitness landscape analysis can be used to characterize the difficulty of the search for different scoring functions. In this...
Technical impossibility to solve exactly NP-hard combinatorial optimization problems for large instances requires the use of heuristics. Nevertheless, the exact methods can be useful, when sub-problems can be extracted from the whole problem. Indeed, their resolution contributes in the global solution search, by combining exact resolution of sub-problems and heuristic resolution of the global problem...
Since the recent advancements in computer storage capacities and processing speed have reached enhanced levels, researches are exerting more demand on higher storage capacity of multidimensional data and elevated computational strengths for research areas. Consequently, two-dimensional pattern matching is a primary topic of current research making good use of current technologies. In this paper, we...
Feature selection is fundamental to knowledge discovery from massive amount of high-dimensional data. In an effort to establish theoretical justification for feature selection algorithms, this paper presents a theoretically optimal criterion, namely, the discriminative optimal criterion (DoC) for feature selection. Compared with the existing representative optimal criterion (RoC, [CHECK END OF SENTENCE])...
Utilizing feature selection in intrusion detection can remove redundant features and improve the speed of the intrusion detection system efficiently on the basis of protecting the integrity of the original data. This paper proposes a new feature selection method that is based on KNN and Tabu search algorithm. The experiment result shows that this method can remove the redundant features, and reduce...
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