2,239 research outputs found
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
Nature-inspired Cuckoo Search Algorithm for Side Lobe Suppression in a Symmetric Linear Antenna Array
In this paper, we proposed a newly modified cuckoo search (MCS) algorithm integrated with the Roulette wheel selection operator and the inertia weight controlling the search ability towards synthesizing symmetric linear array geometry with minimum side lobe level (SLL) and/or nulls control. The basic cuckoo search (CS) algorithm is primarily based on the natural obligate brood parasitic behavior of some cuckoo species in combination with the Levy flight behavior of some birds and fruit flies. The CS metaheuristic approach is straightforward and capable of solving effectively general N-dimensional, linear and nonlinear optimization problems. The array geometry synthesis is first formulated as an optimization problem with the goal of SLL suppression and/or null prescribed placement in certain directions, and then solved by the newly MCS algorithm for the optimum element or isotropic radiator locations in the azimuth-plane or xy-plane. The study also focuses on the four internal parameters of MCS algorithm specifically on their implicit effects in the array synthesis. The optimal inter-element spacing solutions obtained by the MCS-optimizer are validated through comparisons with the standard CS-optimizer and the conventional array within the uniform and the Dolph-Chebyshev envelope patterns using MATLABTM. Finally, we also compared the fine-tuned MCS algorithm with two popular evolutionary algorithm (EA) techniques include particle swarm optimization (PSO) and genetic algorithms (GA)
Development of a Dynamic Cuckoo Search Algorithm
This research is aimed at the developing a modified cuckoo search algorithm called dynamic cuckoo search algorithm (dCSA). The standard cuckoo search algorithm is a metaheuristics search algorithm that mimic the behavior of brood parasitism of some cuckoo species and Levy flight behavior of some fruit flies and birds. It, however uses fixed value for control parameters (control probability and step size) and this method have drawbacks with respect to quality of the solutions and number of iterations to obtain optimal solution. Therefore, the dCSA is developed to address these problems in the CSA by introducing random inertia weight strategy to the control parameters so as to make the control parameters dynamic with respect to the proximity of a cuckoo to the optimal solution. The developed dCSA was compared with CSA using ten benchmark test functions. The results obtained indicated the superiority of dCSA over CSA by generating a near global optimal result for 9 out of the ten benchmark test functions
Is swarm intelligence able to create mazes?
In this paper, the idea of applying Computational Intelligence in the process
of creation board games, in particular mazes, is presented. For two different
algorithms the proposed idea has been examined. The results of the experiments
are shown and discussed to present advantages and disadvantages
A novel implementation of computational aerodynamic shape optimisation using Modified Cuckoo Search
This paper outlines a new computational aerodynamic design optimisation algorithm using a novel method of parameterising a computational mesh using `control nodes'. The shape boundary movement as well as the mesh movement is coupled to the movement of user--defined control nodes via a Delaunay Graph Mapping technique. A Modified Cuckoo Search algorithm is employed for optimisation within the prescribed design space defined by the allowed range of control node displacement. A finite volume compressible Navier--Stokes solver is used for aerodynamic modelling to predict aerodynamic design `fitness'. The resulting coupled algorithm is applied to a range of test cases in two dimensions including aerofoil lift--drag ratio optimisation intake duct optimisation under subsonic, transonic and supersonic flow conditions. The discrete (mesh--based) optimisation approach presented is demonstrated to be effective in terms of its generalised applicability and intuitiveness
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
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