1,536 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
Cuckoo Search Inspired Hybridization of the Nelder-Mead Simplex Algorithm Applied to Optimization of Photovoltaic Cells
A new hybridization of the Cuckoo Search (CS) is developed and applied to
optimize multi-cell solar systems; namely multi-junction and split spectrum
cells. The new approach consists of combining the CS with the Nelder-Mead
method. More precisely, instead of using single solutions as nests for the CS,
we use the concept of a simplex which is used in the Nelder-Mead algorithm.
This makes it possible to use the flip operation introduces in the Nelder-Mead
algorithm instead of the Levy flight which is a standard part of the CS. In
this way, the hybridized algorithm becomes more robust and less sensitive to
parameter tuning which exists in CS. The goal of our work was to optimize the
performance of multi-cell solar systems. Although the underlying problem
consists of the minimization of a function of a relatively small number of
parameters, the difficulty comes from the fact that the evaluation of the
function is complex and only a small number of evaluations is possible. In our
test, we show that the new method has a better performance when compared to
similar but more compex hybridizations of Nelder-Mead algorithm using genetic
algorithms or particle swarm optimization on standard benchmark functions.
Finally, we show that the new method outperforms some standard meta-heuristics
for the problem of interest
Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications
Business optimization is becoming increasingly important because all business
activities aim to maximize the profit and performance of products and services,
under limited resources and appropriate constraints. Recent developments in
support vector machine and metaheuristics show many advantages of these
techniques. In particular, particle swarm optimization is now widely used in
solving tough optimization problems. In this paper, we use a combination of a
recently developed Accelerated PSO and a nonlinear support vector machine to
form a framework for solving business optimization problems. We first apply the
proposed APSO-SVM to production optimization, and then use it for income
prediction and project scheduling. We also carry out some parametric studies
and discuss the advantages of the proposed metaheuristic SVM.Comment: 12 page
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