Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel Local Search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the Local Search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a Local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

In this paper, we propose an effective microaggregation algorithm to produce a more useful protected data for publishing. Microaggregation is mapped to a clustering problem with known minimum and maximum group size constraints. In this scheme, the goal is to cluster n records into groups of at least k and at most 2k-1 records, such that the sum of the within-group squared error (SSE) is minimized. We propose a Local Search algorithm which iteratively satisfies the constraints of the optimal solution of the problem. The algorithm solves the problem in O (n2) time. Experimental results on real and synthetic data sets with different distributions demonstrate the effectiveness of the method in producing useful protected data sets.

One of the problems with traditional genetic algorithms is its premature convergence that makes them incapable of Searching good solutions of the problem. A memetic algorithm (MA) which is an extension of the traditional genetic algorithm uses a Local Search method to either accelerate the discovery of good solutions, for which evolution alone would take too long to discover, or to reach solutions that would otherwise be unreachable by evolution or a Local Search method alone. In this paper, a memetic algorithm based on learning automata (LA) and memetic algorithm, called LA-MA, is introduced. This algorithm is composed of two parts, genetic section and memetic section. Evolution is performed in genetic section and Local Search is performed in memetic section. The basic idea of LA-MA is to use learning automata during the process of Searching for solutions in order to create a balance between exploration performed by evolution and exploitation performed by Local Search. To evaluate the efficiency of LA-MA, it has been used to solve two optimization problems: OneMax and graph isomorphism problems. The results of computer experimentations have shown that different versions of LA-MA outperform the others in terms of quality of solution and rate of convergence.

The personalized Local mobile Search aims at finding the right on the spot information that is most relevant to the user's requests. It is implemented as a mobile application where the user can access nearby places based on his/ her current location. In Today's technology driven world user profiles are the virtual representation of each user and they include a variety of user information such as personal, interest and preference data. These profiles are the outcome of the user profiling process and they are essential to service personalization. The user profile based personalization approach can be applied to enhance the power of mobile Local Search for Local spots and contributes to a significant convenience in location-based mobile Searching. The system takes the user information such as personal, health, entertainment and choice of preference and these parameters are passed to Google Maps API key for personalized query processing. As a result, the user will get prominent services rather than closing one.