73 research outputs found
A nonmonotone GRASP
A greedy randomized adaptive search procedure (GRASP) is an itera-
tive multistart metaheuristic for difficult combinatorial optimization problems. Each
GRASP iteration consists of two phases: a construction phase, in which a feasible
solution is produced, and a local search phase, in which a local optimum in the
neighborhood of the constructed solution is sought. Repeated applications of the con-
struction procedure yields different starting solutions for the local search and the
best overall solution is kept as the result. The GRASP local search applies iterative
improvement until a locally optimal solution is found. During this phase, starting from
the current solution an improving neighbor solution is accepted and considered as the
new current solution. In this paper, we propose a variant of the GRASP framework that
uses a new “nonmonotone” strategy to explore the neighborhood of the current solu-
tion. We formally state the convergence of the nonmonotone local search to a locally
optimal solution and illustrate the effectiveness of the resulting Nonmonotone GRASP
on three classical hard combinatorial optimization problems: the maximum cut prob-
lem (MAX-CUT), the weighted maximum satisfiability problem (MAX-SAT), and
the quadratic assignment problem (QAP)
Coordination of multiple mobile manipulators for ordered sorting of cluttered objects
We present a coordination method for multiple mobile manipulators to sort
objects in clutter. We consider the object rearrangement problem in which the
objects must be sorted into different groups in a particular order. In clutter,
the order constraints could not be easily satisfied since some objects occlude
other objects so the occluded ones are not directly accessible to the robots.
Those objects occluding others need to be moved more than once to make the
occluded objects accessible. Such rearrangement problems fall into the class of
nonmonotone rearrangement problems which are computationally intractable. While
the nonmonotone problems with order constraints are harder, involving with
multiple robots requires another computation for task allocation. The proposed
method first finds a sequence of objects to be sorted using a search such that
the order constraint in each group is satisfied. The search can solve
nonmonotone instances that require temporal relocation of some objects to
access the next object to be sorted. Once a complete sorting sequence is found,
the objects in the sequence are assigned to multiple mobile manipulators using
a greedy allocation method. We develop four versions of the method with
different search strategies. In the experiments, we show that our method can
find a sorting sequence quickly (e.g., 4.6 sec with 20 objects sorted into five
groups) even though the solved instances include hard nonmonotone ones. The
extensive tests and the experiments in simulation show the ability of the
method to solve the real-world sorting problem using multiple mobile
manipulators.Comment: Presented at iROS 202
Solving systems of nonlinear equations by harmony search
In this paper, we aim to analyze the performance of some variants of the harmony
search (HS) metaheuristic when solving systems of nonlinear equations through the
global optimization of an appropriate merit function. The HS metaheuristic draws its
inspiration from an artistic process, the improvisation process of musicians seeking a
wonderful harmony. A new differential best HS algorithm, based on an improvisation
operator that mimics the best harmony and uses a differential variation, is proposed.
Computational experiments involving a well-known set of small-dimensional problems
are presented.Fundação para a Ciência e a Tecnologia (FCT
\u3cem\u3eGRASP News\u3c/em\u3e, Volume 8, Number 1
A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory. Edited by Thomas Lindsay
GRASP News Volume 9, Number 1
A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory
Accelerating gradient projection methods for -constrained signal recovery by steplength selection rules
We propose a new gradient projection algorithm that compares favorably with
the fastest algorithms available to date for -constrained sparse
recovery from noisy data, both in the compressed sensing and inverse problem
frameworks. The method exploits a line-search along the feasible direction and
an adaptive steplength selection based on recent strategies for the alternation
of the well-known Barzilai-Borwein rules. The convergence of the proposed
approach is discussed and a computational study on both well-conditioned and
ill-conditioned problems is carried out for performance evaluations in
comparison with five other algorithms proposed in the literature.Comment: 11 pages, 4 figure
Research on Symbolic Inference in Computational Vision
This paper provides an overview of ongoing research in the GRASP laboratory which focuses on the general problem of symbolic inference in computational vision. In this report we describe a conceptual framework for this research, and describe our current research programs in the component areas which support this work
\u3cem\u3eGRASP News\u3c/em\u3e: Volume 9, Number 1
The past year at the GRASP Lab has been an exciting and productive period. As always, innovation and technical advancement arising from past research has lead to unexpected questions and fertile areas for new research. New robots, new mobile platforms, new sensors and cameras, and new personnel have all contributed to the breathtaking pace of the change. Perhaps the most significant change is the trend towards multi-disciplinary projects, most notable the multi-agent project (see inside for details on this, and all the other new and on-going projects). This issue of GRASP News covers the developments for the year 1992 and the first quarter of 1993
Mathematical Programming for the Dynamics of Opinion Diffusion
The focus of this paper is on analyzing the role and the choice of parameters in sociophysics diffusion models, by leveraging the potentialities of sociophysics from a mathematical programming perspective. We first present a generalised version of Galam’s opinion diffusion model (see, e.g. [8,10])). For a given selection of the coefficients in our model, this proposal yields the original Galam’s model. The generalised model suggests guidelines for possible alternative selection of its parameters that allow to foster diffusion. Examples of the parameters selection process as steered by numerical optimisation, taking into account various objectives, are provided
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