850 research outputs found
Digital Filter Design Using Multiobjective Cuckoo Search Algorithm
Digital filters can be divided into finite impulse response (FIR) digital filters and infinite impulse response (IIR) digital filters. Evolutionary algorithms are effective techniques in digital filter designs. One such evolutionary algorithm is Cuckoo Search Algorithm (CSA). The CSA is a heuristic algorithm which emulates a special parasitic hatching habit of some species of cuckoos and have been proved to be an effective method with various applications. This thesis compares CSA with Park-McClellan algorithm on linear-phase FIR Type-1 lowpass, highpass, bandpass and bandstop digital filter design. Furthermore, a multiobjective Cuckoo Search Algorithm (MOCSA) is applied on general FIR digital design with a comparison to Non-dominated Sorting Genetic Algorithm III (NSGA-III). Finally, a constrained multiobjective Cuckoo Search Algorithm is presented and used for IIR digital filter design. The design results of the constrained MOCSA approach compares favorably with other state-of-the-art optimization methods. CSA utilizes Levy flight with wide-range step length for the global walk to assure reaching the global optimum and the approach of local walk to orientate the direction toward the local minima. Furthermore, MOCSA incorporates a method of Euclidean distance combing objective-based equilibrating operations and the searching for the optimal solution into one step and simplifies the procedure of comparison
Bat Algorithm for Multi-objective Optimisation
Engineering optimization is typically multiobjective and multidisciplinary
with complex constraints, and the solution of such complex problems requires
efficient optimization algorithms. Recently, Xin-She Yang proposed a
bat-inspired algorithm for solving nonlinear, global optimisation problems. In
this paper, we extend this algorithm to solve multiobjective optimisation
problems. The proposed multiobjective bat algorithm (MOBA) is first validated
against a subset of test functions, and then applied to solve multiobjective
design problems such as welded beam design. Simulation results suggest that the
proposed algorithm works efficiently.Comment: 12 pages. arXiv admin note: text overlap with arXiv:1004.417
Bat Algorithm: Literature Review and Applications
Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and
BA has been found to be very efficient. As a result, the literature has
expanded significantly in the last 3 years. This paper provides a timely review
of the bat algorithm and its new variants. A wide range of diverse applications
and case studies are also reviewed and summarized briefly here. Further
research topics are also discussed.Comment: 10 page
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
Optimization and synthesis of multilayer frequency selective surfaces via bioinspired hybrid techniques
In this study, two bioinspired computation (BIC) techniques are discussed and applied to the project and synthesis of multilayer frequency selective surfaces (FSS) within the microwave band, specifically for C, X and Ku bands. The proposed BIC techniques consist of combining an artificial, general regression neural network to a genetic algorithm (GA) and a cuckoo search algorithm, respectively. The objective is to find the optimal values of separation between the investigated FSS. Numerical analysis of the electromagnetic properties of the device is made possible with the finite integration method (FIT) and validated through the finite element method (FEM), utilizing both softwares CST Microwave Studio and Ansys HFSS respectively. Thus, the BIC-optimized devices present good phase / angular stability for angles 10°, 20°, 30° and 40°, as well as being polarization independent. The cutoff frequencies to control the operating frequency range of the FSS, referring to transmission coefficient in decibels (dB), were obtained at a threshold of –10dB. Numerical results denote good accordance with measured data
Cuckoo Search via Levy Flights
In this paper, we intend to formulate a new metaheuristic algorithm, called
Cuckoo Search (CS), for solving optimization problems. This algorithm is based
on the obligate brood parasitic behaviour of some cuckoo species in combination
with the Levy flight behaviour of some birds and fruit flies. We validate the
proposed algorithm against test functions and then compare its performance with
those of genetic algorithms and particle swarm optimization. Finally, we
discuss the implication of the results and suggestion for further research
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