44 research outputs found
Cuckoo Search via Levy Flights
In this paper, we intend to formulate a new metaheuristic algorithm, called
Cuckoo Search (CS), for solving optimization problems. This algorithm is based
on the obligate brood parasitic behaviour of some cuckoo species in combination
with the Levy flight behaviour of some birds and fruit flies. We validate the
proposed algorithm against test functions and then compare its performance with
those of genetic algorithms and particle swarm optimization. Finally, we
discuss the implication of the results and suggestion for further research
Two-Stage Eagle Strategy with Differential Evolution
Efficiency of an optimization process is largely determined by the search
algorithm and its fundamental characteristics. In a given optimization, a
single type of algorithm is used in most applications. In this paper, we will
investigate the Eagle Strategy recently developed for global optimization,
which uses a two-stage strategy by combing two different algorithms to improve
the overall search efficiency. We will discuss this strategy with differential
evolution and then evaluate their performance by solving real-world
optimization problems such as pressure vessel and speed reducer design. Results
suggest that we can reduce the computing effort by a factor of up to 10 in many
applications
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
From swarm intelligence to metaheuristics: nature-inspired optimization algorithms
Nature has provided rich models for computational problem solving, including optimizations based on the swarm intelligence exhibited by fireflies, bats, and ants. These models can stimulate computer scientists to think nontraditionally in creating tools to address application design challenges