16,255 research outputs found
Bat Algorithm: Literature Review and Applications
Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and
BA has been found to be very efficient. As a result, the literature has
expanded significantly in the last 3 years. This paper provides a timely review
of the bat algorithm and its new variants. A wide range of diverse applications
and case studies are also reviewed and summarized briefly here. Further
research topics are also discussed.Comment: 10 page
Evolutionary framework with reinforcement learning-based mutation adaptation
Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution algorithm and a co-variance matrix adaptation evolution strategy. In the former, reinforcement learning is used to automatically choose the best differential evolution operator. To judge the performance of the proposed framework, three benchmark sets of bound-constrained optimization problems (73 problems) with 10, 30 and 50 dimensions are solved. Further, the proposed algorithm has been tested by solving optimization problems with 100 dimensions taken from CEC2014 and CEC2017 benchmark problems. A real-world application data set has also been solved. Several experiments are designed to analyze the effects of different components of the proposed framework, with the best variant compared with a number of state-of-the-art algorithms. The experimental results show that the proposed algorithm is able to outperform all the others considered.</p
Synthesis of Spherical 4R Mechanism for Path Generation using Differential Evolution
The problem of path generation for the spherical 4R mechanism is solved using
the Differential Evolution algorithm (DE). Formulas for the spherical geodesics
are employed in order to obtain the parametric equation for the generated
trajectory. Direct optimization of the objective function gives the solution to
the path generation task without prescribed timing. Therefore, there is no need
to separate this task into two stages to make the optimization. Moreover, the
order defect problem can be solved without difficulty by means of manipulations
of the individuals in the DE algorithm. Two examples of optimum synthesis
showing the simplicity and effectiveness of this approach are included.Comment: Submitted to Mechanism and Machine Theor
Oscillatory Dynamics in Rock-Paper-Scissors Games with Mutations
We study the oscillatory dynamics in the generic three-species
rock-paper-scissors games with mutations. In the mean-field limit, different
behaviors are found: (a) for high mutation rate, there is a stable interior
fixed point with coexistence of all species; (b) for low mutation rates, there
is a region of the parameter space characterized by a limit cycle resulting
from a Hopf bifurcation; (c) in the absence of mutations, there is a region
where heteroclinic cycles yield oscillations of large amplitude (not robust
against noise). After a discussion on the main properties of the mean-field
dynamics, we investigate the stochastic version of the model within an
individual-based formulation. Demographic fluctuations are therefore naturally
accounted and their effects are studied using a diffusion theory complemented
by numerical simulations. It is thus shown that persistent erratic oscillations
(quasi-cycles) of large amplitude emerge from a noise-induced resonance
phenomenon. We also analytically and numerically compute the average escape
time necessary to reach a (quasi-)cycle on which the system oscillates at a
given amplitude.Comment: 25 pages, 9 figures. To appear in the Journal of Theoretical Biolog
Collaborative Verification-Driven Engineering of Hybrid Systems
Hybrid systems with both discrete and continuous dynamics are an important
model for real-world cyber-physical systems. The key challenge is to ensure
their correct functioning w.r.t. safety requirements. Promising techniques to
ensure safety seem to be model-driven engineering to develop hybrid systems in
a well-defined and traceable manner, and formal verification to prove their
correctness. Their combination forms the vision of verification-driven
engineering. Often, hybrid systems are rather complex in that they require
expertise from many domains (e.g., robotics, control systems, computer science,
software engineering, and mechanical engineering). Moreover, despite the
remarkable progress in automating formal verification of hybrid systems, the
construction of proofs of complex systems often requires nontrivial human
guidance, since hybrid systems verification tools solve undecidable problems.
It is, thus, not uncommon for development and verification teams to consist of
many players with diverse expertise. This paper introduces a
verification-driven engineering toolset that extends our previous work on
hybrid and arithmetic verification with tools for (i) graphical (UML) and
textual modeling of hybrid systems, (ii) exchanging and comparing models and
proofs, and (iii) managing verification tasks. This toolset makes it easier to
tackle large-scale verification tasks
A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification
Convolutional auto-encoders have shown their remarkable performance in
stacking to deep convolutional neural networks for classifying image data
during past several years. However, they are unable to construct the
state-of-the-art convolutional neural networks due to their intrinsic
architectures. In this regard, we propose a flexible convolutional auto-encoder
by eliminating the constraints on the numbers of convolutional layers and
pooling layers from the traditional convolutional auto-encoder. We also design
an architecture discovery method by using particle swarm optimization, which is
capable of automatically searching for the optimal architectures of the
proposed flexible convolutional auto-encoder with much less computational
resource and without any manual intervention. We use the designed architecture
optimization algorithm to test the proposed flexible convolutional auto-encoder
through utilizing one graphic processing unit card on four extensively used
image classification datasets. Experimental results show that our work in this
paper significantly outperform the peer competitors including the
state-of-the-art algorithm.Comment: Accepted by IEEE Transactions on Neural Networks and Learning
Systems, 201
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