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This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcalá de Henares, Madrid, Spain. [ABSTRACT FROM AUTHOR]

Our paper introduces a new methodology to solve group decision-making problems under fuzzy and dynamic environment. The methodology takes group members’ linguistically defined pair wise preferences of alternatives in different time intervals and aggregates them across the intervals to obtain each member's net preference levels. Each member's net preference levels are again aggregated across the members to obtain the group's preference. Our paper attaches higher importance to the members whose involvement in the decision process is more recent than the members who opined their views in the past. The fuzzy aggregation operator, IOWA (Induced Ordered Weighted Average) is used to aggregate their views in accordance to their importance in the group. The Ranked_List algorithm, introduced in our paper, inputs the aggregated views of the members in pair wise form and produces the set of sequences of ranked list of alternatives representing the group's consensus view as output. The Ranked_List algorithm is validated and analyzed through a series of synthetic data sets and its results are compared with a movie selection case study. The methodology is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

The state-of-art speaker recognition system employs vocal tract information for modeling through different supervised and unsupervised models. Whereas, the baseline of the paper uses LP-residual as the acoustic feature for the following studies. Fuzzy C-Means (FCM) is used to model the information extracted from LP-residual to develop speaker models. FCM is a well-known unsupervised fuzzy model used in speech recognition. Speakers are modeled in order to develop a text-dependent Automatic Speaker Verification (ASV) system. The performance of FCM model have been observed for different codebook sizes varying from 32 to 1024. Also studies are carried out for different fuzzy membership values varying from 1.39 to 4. For LP-residual features, the performance of FCM remains unchanged even after changing the codebook sizes whereas with the change of fuzzy membership the performance of the system is observed to vary. [ABSTRACT FROM AUTHOR]

Constraint programming effectively solves constraint satisfaction and optimization problems by basically building, pruning, and exploring a search tree of potential solutions. In this context, a main component is the enumeration strategy, which is responsible for selecting the order in which variables and values are selected to build a possible solution. This process is known to be quite important; indeed a correct selection can reach a solution without failed explorations. However, it is well known that selecting the right strategy is quite challenging as their performance is notably hard to predict. During the last years, adaptive enumeration appeared as a proper solution to this problem. Adaptive enumeration allows the solving algorithm being able to autonomously modifying its strategies in solving time depending on performance information. In this way, the most suitable order for variables and values is employed along the search. In this paper, we present a new and more lightweight approach for performing adaptive enumeration. We incorporate a powerful classification technique named Top-k in order to adaptively select strategies along the resolution. We report results on a set of well-known benchmarks where the proposed approach noticeably competes with classical and modern adaptive enumeration methods for constraint satisfaction. [ABSTRACT FROM AUTHOR]