1,227 research outputs found
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
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
The design and applications of the african buffalo algorithm for general optimization problems
Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development
of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’
stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful
grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained,
separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the
successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature
Cuckoo search for business optimization applications
Cuckoo search has become a popular and powerful metaheuristic algorithm for global optimization. In business optimization and applications, many studies have focused on support vector machine and neural networks. In this paper, we use cuckoo search to carry out optimization tasks and compare the performance of cuckoo search with support vector machine. By testing benchmarks such as project scheduling and bankruptcy predictions, we conclude that cuckoo search can perform better than support vector machine
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