4,485 research outputs found
Artificial Bee Colony Algorithm with Improved Explorations for Numerical Function Optimization
A major problem with Artificial Bee Colony (ABC) algorithm is its
premature convergence to local optima, which originates from lack of explorative
search capability of the algorithm. This paper introduces ABC with Improved
Explorations (ABC-IX), a novel algorithm that modifies both the selection
and perturbation operations of the basic ABC algorithm in an explorative
way. Unlike the basic ABC algorithm, ABC-IX employs a probabilistic, explorative
selection scheme based on simulated annealing which can accept both better
and worse candidate solutions. ABC-IX also maintains a self-adaptive perturbation
rate, separately for each candidate solution, to promote more explorations.
ABC-IX is tested on a number of benchmark problems for numerical optimization
and compared with several recent variants of ABC. Results show that
ABC-IX often outperforms the other ABC-variants on most of the problems
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
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
Multivariate time series analysis for short-term forecasting of ground level ozone (O3) in Malaysia
The declining of air quality mostly affects the elderly, children, people with asthma,
as well as a restriction on outdoor activities. Therefore, there is an importance to
provide a statistical modelling to forecast the future values of surface layer ozone (O3)
concentration. The objectives of this study are to obtain the best multivariate time
series (MTS) model and develop an online air quality forecasting system for O3
concentration in Malaysia. The implementations of MTS model improve the recent
statistical model on air quality for short-term prediction. Ten air quality monitoring
stations situated at four (4) different types of location were selected in this study. The
first type is industrial represent by Pasir Gudang, Perai, and Nilai, second type is urban
represent by Kuala Terengganu, Kota Bharu, and Alor Setar. The third is suburban
located in Banting, Kangar, and Tanjung Malim, also the only background station at
Jerantut. The hourly record data from 2010 to 2017 were used to assess the
characteristics and behaviour of O3 concentration. Meanwhile, the monthly record data
of O3, particulate matter (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2),
carbon monoxide (CO), temperature (T), wind speed (WS), and relative humidity (RH)
were used to examine the best MTS models. Three methods of MTS namely vector
autoregressive (VAR), vector moving average (VMA), and vector autoregressive
moving average (VARMA), has been applied in this study. Based on the performance
error, the most appropriate MTS model located in Pasir Gudang, Kota Bharu and
Kangar is VAR(1), Kuala Terengganu and Alor Setar for VAR(2), Perai and Nilai for
VAR(3), Tanjung Malim for VAR(4) and Banting for VAR(5). Only Jerantut obtained
the VMA(2) as the best model. The lowest root mean square error (RMSE) and
normalized absolute error is 0.0053 and <0.0001 which is for MTS model in Perai and
Kuala Terengganu, respectively. Meanwhile, for mean absolute error (MAE), the
lowest is in Banting and Jerantut at 0.0013. The online air quality forecasting system
for O3 was successfully developed based on the best MTS models to represent each
monitoring station
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