408 research outputs found
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
In many technical fields, single-objective optimization procedures in
continuous domains involve expensive numerical simulations. In this context, an
improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial
super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide
fast convergence speed, high solution accuracy and robust performance over a
wide range of problems. It implements enhancements of the ABC structure and
hybridizations with interpolation strategies. The latter are inspired by the
quadratic trust region approach for local investigation and by an efficient
global optimizer for separable problems. Each modification and their combined
effects are studied with appropriate metrics on a numerical benchmark, which is
also used for comparing AsBeC with some effective ABC variants and other
derivative-free algorithms. In addition, the presented algorithm is validated
on two recent benchmarks adopted for competitions in international conferences.
Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc
Direct sequential based firefly algorithm for the α-pinene isomerization problem
Publicado em: "Computational science and its applications – ICCSA 2016: 16th International Conference, Beijing, China, July 4-7, 2016, Proceedings, Part I"The problem herein addressed is a parameter estimation
problem of the α-pinene process. The state variables of this bioengineering
process satisfy a set of differential equations and depend on a
set of unknown parameters. A dynamic system based parameter estimation
problem aiming to estimate the model parameter values in a way
that the predicted state variables best fit the experimentally observed
state values is used. A numerical direct method, known as direct sequential
procedure, is implemented giving rise to a finite bound constrained
nonlinear optimization problem, which is solved by the metaheuristic
firefly algorithm (FA). A Matlab programming environment is developed
with the mathematical model and the computational application
of the method. The results produced by FA, when compared to those of
the fmincon function and other metaheuristics, are competitive.COMPETE: POCI-01- 0145-FEDER-007043FCT - Fundação para a Ciência e Tecnologia, within the projects UID/CEC/00319/2013 and UID/MAT/00013/201
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
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
How does the number of objective function evaluations impact our understanding of metaheuristics behavior?
Comparing various metaheuristics based on an equal number of objective function evaluations has become standard practice. Many contemporary publications use a specific number of objective function evaluations by the benchmarking sets definitions. Furthermore, many publications deal with the recurrent theme of late stagnation, which may lead to the impression that continuing the optimization process could be a waste of computational capabilities. But is it? Recently, many challenges, issues, and questions have been raised regarding fair comparisons and recommendations towards good practices for benchmarking metaheuristic algorithms. The aim of this work is not to compare the performance of several well-known algorithms but to investigate the issues that can appear in benchmarking and comparisons of metaheuristics performance (no matter what the problem is). This article studies the impact of a higher evaluation number on a selection of metaheuristic algorithms. We examine the effect of a raised evaluation budget on overall performance, mean convergence, and population diversity of selected swarm algorithms and IEEE CEC competition winners. Even though the final impact varies based on current algorithm selection, it may significantly affect the final verdict of metaheuristics comparison. This work has picked an important benchmarking issue and made extensive analysis, resulting in conclusions and possible recommendations for users working with real engineering optimization problems or researching the metaheuristics algorithms. Especially nowadays, when metaheuristic algorithms are used for increasingly complex optimization problems, and meet machine learning in AutoML frameworks, we conclude that the objective function evaluation budget should be considered another vital optimization input variable.Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2021/001]; AI Laboratory, Faculty of Applied Informatics, Tomas Bata University in ZlinIGA/CebiaTech/2021/001; Univerzita Tomáše Bati ve Zlín
Computational Optimization, Modelling and Simulation: Recent Trends and Challenges
Modelling, simulation and optimization form an integrated part of modern
design practice in engineering and industry. Tremendous progress has been
observed for all three components over the last few decades. However, many
challenging issues remain unresolved, and the current trends tend to use
nature-inspired algorithms and surrogate-based techniques for modelling and
optimization. This 4th workshop on Computational Optimization, Modelling and
Simulation (COMS 2013) at ICCS 2013 will further summarize the latest
developments of optimization and modelling and their applications in science,
engineering and industry. In this review paper, we will analyse the recent
trends in modelling and optimization, and their associated challenges. We will
discuss important topics for further research, including parameter-tuning,
large-scale problems, and the gaps between theory and applications
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