1,328 research outputs found

    Firefly Algorithm: Recent Advances and Applications

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    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

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    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

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    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

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    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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