1,328 research outputs found
Firefly Algorithm: Recent Advances and Applications
Nature-inspired metaheuristic algorithms, especially those based on swarm
intelligence, have attracted much attention in the last ten years. Firefly
algorithm appeared in about five years ago, its literature has expanded
dramatically with diverse applications. In this paper, we will briefly review
the fundamentals of firefly algorithm together with a selection of recent
publications. Then, we discuss the optimality associated with balancing
exploration and exploitation, which is essential for all metaheuristic
algorithms. By comparing with intermittent search strategy, we conclude that
metaheuristics such as firefly algorithm are better than the optimal
intermittent search strategy. We also analyse algorithms and their implications
for higher-dimensional optimization problems.Comment: 15 page
Bio-inspired optimization algorithms for smart antennas
This thesis studies the effectiveness of bio-inspired optimization algorithms in
controlling adaptive antenna arrays. Smart antennas are able to automatically
extract the desired signal from interferer signals and external noise. The angular
pattern depends on the number of antenna elements, their geometrical arrangement,
and their relative amplitude and phases. In the present work different
antenna geometries are tested and compared when their array weights are optimized
by different techniques. First, the Genetic Algorithm and Particle Swarm
Optimization algorithms are used to find the best set of phases between antenna
elements to obtain a desired antenna pattern. This pattern must meet several
restraints, for example: Maximizing the power of the main lobe at a desired direction
while keeping nulls towards interferers. A series of experiments show that
the PSO achieves better and more consistent radiation patterns than the GA in
terms of the total area of the antenna pattern. A second set of experiments use
the Signal-to-Interference-plus-Noise-Ratio as the fitness function of optimization
algorithms to find the array weights that configure a rectangular array. The results
suggest an advantage in performance by reducing the number of iterations
taken by the PSO, thus lowering the computational cost. During the development
of this thesis, it was found that the initial states and particular parameters of
the optimization algorithms affected their overall outcome. The third part of this
work deals with the meta-optimization of these parameters to achieve the best
results independently from particular initial parameters. Four algorithms were
studied: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing
and Hill Climb. It was found that the meta-optimization algorithms Local Unimodal
Sampling and Pattern Search performed better to set the initial parameters
and obtain the best performance of the bio-inspired methods studied
A Brief Review of Cuckoo Search Algorithm (CSA) Research Progression from 2010 to 2013
Cuckoo Search Algorithm is a new swarm intelligence algorithm which based on
breeding behavior of the Cuckoo bird. This paper gives a brief insight of the advancement of the
Cuckoo Search Algorithm from 2010 to 2013.
The first half of this paper presents the publication trend of Cuckoo Search Algorithm. The
remaining of this paper briefly explains the contribution of the individual publication related to
Cuckoo Search Algorithm. It is believed that this paper will greatly benefit the reader who needs a
bird-eyes view of the Cuckoo Search Algorithm’s publications trend
Comparison of Evolutionary Algorithms for Synthesis of Non-Uniformly Spaced Linear Array of Unequal Length Parallel Dipole Antennas for Impedance Matching with low side lobe level
This work presents a comparative study of three evolutionary algorithms such as quantum particle swarm optimization (QPSO), firefly algorithm (FA) and cuckoo search algorithm (CS) for synthesis of linear array of non-uniformly spaced parallel unequal length very thin dipole antennas for impedance matching of all the antenna elements of an array with low side lobe level. Performance of the above three algorithms for impedance matching are compared here in terms of side lobe level as well as statistical parameters such as global best fitness value, worst fitness value, mean and standard deviation. Mutual coupling effect exists between the parallel dipole antennas and it is analyzed by induced electro-motive force (EMF) method, assuming Current distribution on each dipole to be sinusoidal. In addition to it, the obtained results from simulation of the entire optimization algorithm on Matlab is also validated by results obtained from FEKO analysis. One example is presented to show the effectiveness of the proposed approach. Moreover the applied method seems very effective for a linear array of dipole antennas; however, the principle can easily be extended to other type of arrays
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