4,628 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
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
Chemical and biological reactions of solidification of peat using ordinary portland cement (OPC) and coal ashes
Construction over peat area have often posed a challenge to geotechnical engineers.
After decades of study on peat stabilisation techniques, there are still no absolute
formulation or guideline that have been established to handle this issue. Some
researchers have proposed solidification of peat but a few researchers have also
discovered that solidified peat seemed to decrease its strength after a certain period of
time. Therefore, understanding the chemical and biological reaction behind the peat
solidification is vital to understand the limitation of this treatment technique. In this
study, all three types of peat; fabric, hemic and sapric were mixed using Mixing 1 and
Mixing 2 formulation which consisted of ordinary Portland cement, fly ash and bottom
ash at various ratio. The mixtures of peat-binder-filler were subjected to the
unconfined compressive strength (UCS) test, bacterial count test and chemical
elemental analysis by using XRF, XRD, FTIR and EDS. Two pattern of strength over
curing period were observed. Mixing 1 samples showed a steadily increase in strength
over curing period until Day 56 while Mixing 2 showed a decrease in strength pattern
at Day 28 and Day 56. Samples which increase in strength steadily have less bacterial
count and enzymatic activity with increase quantity of crystallites. Samples with lower
strength recorded increase in bacterial count and enzymatic activity with less
crystallites. Analysis using XRD showed that pargasite
(NaCa2[Mg4Al](Si6Al2)O22(OH)2) was formed in the higher strength samples while in
the lower strength samples, pargasite was predicted to be converted into monosodium
phosphate and Mg(OH)2 as bacterial consortium was re-activated. The Michaelis�Menten coefficient, Km of the bio-chemical reaction in solidified peat was calculated
as 303.60. This showed that reaction which happened during solidification work was
inefficient. The kinetics for crystallite formation with enzymatic effect is modelled as
135.42 (1/[S] + 0.44605) which means, when pargasite formed is lower, the amount
of enzyme secretes is higher
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