Abstract: This paper describes an approach to detect the
transmitted signals for 2×2 Multiple Input Multiple Output (MIMO)
setup using roulette wheel based ant colony optimization technique.
The results obtained are compared with classical zero forcing and
least mean square techniques. The detection rates achieved using
this technique are consistently larger than the one achieved using
classical methods for 50 number of attempts with two different
antennas transmitting the input stream from a user. This paves the
path to use alternative techniques to improve the throughput achieved
in advanced networks like Long Term Evolution (LTE) networks.
Abstract: In this paper, we present a binary cat swarm
optimization for solving the Set covering problem. The set covering
problem is a well-known NP-hard problem with many practical
applications, including those involving scheduling, production
planning and location problems. Binary cat swarm optimization
is a recent swarm metaheuristic technique based on the behavior
of discrete cats. Domestic cats show the ability to hunt and are
curious about moving objects. The cats have two modes of behavior:
seeking mode and tracing mode. We illustrate this approach with
65 instances of the problem from the OR-Library. Moreover, we
solve this problem with 40 new binarization techniques and we select
the technical with the best results obtained. Finally, we make a
comparison between results obtained in previous studies and the new
binarization technique, that is, with roulette wheel as transfer function
and V3 as discretization technique.
Abstract: Prior literature in the field of adaptive and
personalized learning sequence in e-learning have proposed and
implemented various mechanisms to improve the learning process
such as individualization and personalization, but complex to
implement due to expensive algorithmic programming and need of
extensive and prior data. The main objective of personalizing
learning sequence is to maximize learning by dynamically selecting
the closest teaching operation in order to achieve the learning
competency of learner. In this paper, a revolutionary technique has
been proposed and tested to perform individualization and
personalization using modified reversed roulette wheel selection
algorithm that runs at O(n). The technique is simpler to implement
and is algorithmically less expensive compared to other revolutionary
algorithms since it collects the dynamic real time performance matrix
such as examinations, reviews, and study to form the RWSA single
numerical fitness value. Results show that the implemented system is
capable of recommending new learning sequences that lessens time
of study based on student's prior knowledge and real performance
matrix.
Abstract: This article proposes a novel Pareto-based multiobjective
meta-heuristic algorithm named non-dominated ranking
genetic algorithm (NRGA) to solve multi-facility location-allocation
problem. In NRGA, a fitness value representing rank is assigned to
each individual of the population. Moreover, two features ranked
based roulette wheel selection including select the fronts and choose
solutions from the fronts, are utilized. The proposed solving
methodology is validated using several examples taken from the
specialized literature. The performance of our approach shows that
NRGA algorithm is able to generate true and well distributed Pareto
optimal solutions.