10,677 research outputs found
An Improved Artificial Bee Colony Algorithm for Staged Search
Artificial Bee Colony(ABC) or its improved algorithms used in solving high dimensional complex function optimization issues has some disadvantages, such as lower convergence, lower solution precision, lots of control parameters of improved algorithms, easy to fall into a local optimum solution. In this letter, we propose an improved ABC of staged search. This new algorithm designs staged employed bee search strategy which makes that employed bee has different search characters in different stages. That reduces probability of falling into local extreme value. It defines the escape radius which can guide precocious individual to jump local extreme value and avoid the blindness of flight behavior. Meanwhile, we adopt initialization strategy combining uniform distribution and backward learning to prompt initial solution with uniform distribution and better quality. Finally, we make simulation experiments for eight typical high dimensional complex functions. Results show that the improved algorithm has a higher solution precision and faster convergence rate which is more suitable for solving high dimensional complex functions
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
Memetic Artificial Bee Colony Algorithm for Large-Scale Global Optimization
Memetic computation (MC) has emerged recently as a new paradigm of efficient
algorithms for solving the hardest optimization problems. On the other hand,
artificial bees colony (ABC) algorithms demonstrate good performances when
solving continuous and combinatorial optimization problems. This study tries to
use these technologies under the same roof. As a result, a memetic ABC (MABC)
algorithm has been developed that is hybridized with two local search
heuristics: the Nelder-Mead algorithm (NMA) and the random walk with direction
exploitation (RWDE). The former is attended more towards exploration, while the
latter more towards exploitation of the search space. The stochastic adaptation
rule was employed in order to control the balancing between exploration and
exploitation. This MABC algorithm was applied to a Special suite on Large Scale
Continuous Global Optimization at the 2012 IEEE Congress on Evolutionary
Computation. The obtained results the MABC are comparable with the results of
DECC-G, DECC-G*, and MLCC.Comment: CONFERENCE: IEEE Congress on Evolutionary Computation, Brisbane,
Australia, 201
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