1,477 research outputs found
Modeling preference time in middle distance triathlons
Modeling preference time in triathlons means predicting the intermediate
times of particular sports disciplines by a given overall finish time in a
specific triathlon course for the athlete with the known personal best result.
This is a hard task for athletes and sport trainers due to a lot of different
factors that need to be taken into account, e.g., athlete's abilities, health,
mental preparations and even their current sports form. So far, this process
was calculated manually without any specific software tools or using the
artificial intelligence. This paper presents the new solution for modeling
preference time in middle distance triathlons based on particle swarm
optimization algorithm and archive of existing sports results. Initial results
are presented, which suggest the usefulness of proposed approach, while remarks
for future improvements and use are also emphasized.Comment: ISCBI 201
Making up for the deficit in a marathon run
To predict the final result of an athlete in a marathon run thoroughly is the
eternal desire of each trainer. Usually, the achieved result is weaker than the
predicted one due to the objective (e.g., environmental conditions) as well as
subjective factors (e.g., athlete's malaise). Therefore, making up for the
deficit between predicted and achieved results is the main ingredient of the
analysis performed by trainers after the competition. In the analysis, they
search for parts of a marathon course where the athlete lost time. This paper
proposes an automatic making up for the deficit by using a Differential
Evolution algorithm. In this case study, the results that were obtained by a
wearable sports-watch by an athlete in a real marathon are analyzed. The first
experiments with Differential Evolution show the possibility of using this
method in the future.Comment: ISMSI 201
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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