3,387 research outputs found
PasMoQAP: A Parallel Asynchronous Memetic Algorithm for solving the Multi-Objective Quadratic Assignment Problem
Multi-Objective Optimization Problems (MOPs) have attracted growing attention
during the last decades. Multi-Objective Evolutionary Algorithms (MOEAs) have
been extensively used to address MOPs because are able to approximate a set of
non-dominated high-quality solutions. The Multi-Objective Quadratic Assignment
Problem (mQAP) is a MOP. The mQAP is a generalization of the classical QAP
which has been extensively studied, and used in several real-life applications.
The mQAP is defined as having as input several flows between the facilities
which generate multiple cost functions that must be optimized simultaneously.
In this study, we propose PasMoQAP, a parallel asynchronous memetic algorithm
to solve the Multi-Objective Quadratic Assignment Problem. PasMoQAP is based on
an island model that structures the population by creating sub-populations. The
memetic algorithm on each island individually evolve a reduced population of
solutions, and they asynchronously cooperate by sending selected solutions to
the neighboring islands. The experimental results show that our approach
significatively outperforms all the island-based variants of the
multi-objective evolutionary algorithm NSGA-II. We show that PasMoQAP is a
suitable alternative to solve the Multi-Objective Quadratic Assignment Problem.Comment: 8 pages, 3 figures, 2 tables. Accepted at Conference on Evolutionary
Computation 2017 (CEC 2017
Ecosystem-Oriented Distributed Evolutionary Computing
We create a novel optimisation technique inspired by natural ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
genes which are distributed in a peer-to-peer network, operating continuously
in time; this process feeds a second optimisation based on evolutionary
computing that operates locally on single peers and is aimed at finding
solutions to satisfy locally relevant constraints. We consider from the domain
of computer science distributed evolutionary computing, with the relevant
theory from the domain of theoretical biology, including the fields of
evolutionary and ecological theory, the topological structure of ecosystems,
and evolutionary processes within distributed environments. We then define
ecosystem- oriented distributed evolutionary computing, imbibed with the
properties of self-organisation, scalability and sustainability from natural
ecosystems, including a novel form of distributed evolu- tionary computing.
Finally, we conclude with a discussion of the apparent compromises resulting
from the hybrid model created, such as the network topology.Comment: 8 pages, 5 figures. arXiv admin note: text overlap with
arXiv:1112.0204, arXiv:0712.4159, arXiv:0712.4153, arXiv:0712.4102,
arXiv:0910.067
Soft Computing Techiniques for the Protein Folding Problem on High Performance Computing Architectures
The protein-folding problem has been extensively studied during the last
fifty years. The understanding of the dynamics of global shape of a protein and the influence
on its biological function can help us to discover new and more effective
drugs to deal with diseases of pharmacological relevance. Different computational approaches
have been developed by different researchers in order to foresee the threedimensional
arrangement of atoms of proteins from their sequences. However, the
computational complexity of this problem makes mandatory the search for new models,
novel algorithmic strategies and hardware platforms that provide solutions in a
reasonable time frame. We present in this revision work the past and last tendencies
regarding protein folding simulations from both perspectives; hardware and software.
Of particular interest to us are both the use of inexact solutions to this computationally hard problem as
well as which hardware platforms have been used for running this kind of Soft Computing techniques.This work is jointly supported by the FundaciónSéneca (Agencia Regional de Ciencia y TecnologÃa, Región de Murcia) under grants 15290/PI/2010 and 18946/JLI/13, by the Spanish MEC and European Commission FEDER under grant with reference TEC2012-37945-C02-02 and TIN2012-31345, by the Nils Coordinated Mobility under grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF). We also thank NVIDIA for hardware donation within UCAM GPU educational and research centers.IngenierÃa, Industria y Construcció
Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey
Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalability and, therefore, their relevance in tackling computationally expensive applications. This paper presents a survey of pMOEAs, describing a refined taxonomy, an up-to-date review of methods and the key contributions to the field. Furthermore, some of the open questions that require further research are also briefly discussed
Optimization as a design strategy. Considerations based on building simulation-assisted experiments about problem decomposition
In this article the most fundamental decomposition-based optimization method
- block coordinate search, based on the sequential decomposition of problems in
subproblems - and building performance simulation programs are used to reason
about a building design process at micro-urban scale and strategies are defined
to make the search more efficient. Cyclic overlapping block coordinate search
is here considered in its double nature of optimization method and surrogate
model (and metaphore) of a sequential design process. Heuristic indicators apt
to support the design of search structures suited to that method are developed
from building-simulation-assisted computational experiments, aimed to choose
the form and position of a small building in a plot. Those indicators link the
sharing of structure between subspaces ("commonality") to recursive
recombination, measured as freshness of the search wake and novelty of the
search moves. The aim of these indicators is to measure the relative
effectiveness of decomposition-based design moves and create efficient block
searches. Implications of a possible use of these indicators in genetic
algorithms are also highlighted.Comment: 48 pages. 12 figures, 3 table
Biology of Applied Digital Ecosystems
A primary motivation for our research in Digital Ecosystems is the desire to
exploit the self-organising properties of biological ecosystems. Ecosystems are
thought to be robust, scalable architectures that can automatically solve
complex, dynamic problems. However, the biological processes that contribute to
these properties have not been made explicit in Digital Ecosystems research.
Here, we discuss how biological properties contribute to the self-organising
features of biological ecosystems, including population dynamics, evolution, a
complex dynamic environment, and spatial distributions for generating local
interactions. The potential for exploiting these properties in artificial
systems is then considered. We suggest that several key features of biological
ecosystems have not been fully explored in existing digital ecosystems, and
discuss how mimicking these features may assist in developing robust, scalable
self-organising architectures. An example architecture, the Digital Ecosystem,
is considered in detail. The Digital Ecosystem is then measured experimentally
through simulations, with measures originating from theoretical ecology, to
confirm its likeness to a biological ecosystem. Including the responsiveness to
requests for applications from the user base, as a measure of the 'ecological
succession' (development).Comment: 9 pages, 4 figure, conferenc
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Ant colony system-based applications to electrical distribution system optimization
Chapter 16, February 201
Parallel surrogate-assisted global optimization with expensive functions – a survey
Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention.United States. Dept. of Energy (National Nuclear Security Administration. Advanced Simulation and Computing Program. Cooperative Agreement under the Predictive Academic Alliance Program. DE-NA0002378
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