3,661 research outputs found
Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm
This paper proposes a hybrid self-adaptive evolutionary algorithm for graph
coloring that is hybridized with the following novel elements: heuristic
genotype-phenotype mapping, a swap local search heuristic, and a neutral
survivor selection operator. This algorithm was compared with the evolutionary
algorithm with the SAW method of Eiben et al., the Tabucol algorithm of Hertz
and de Werra, and the hybrid evolutionary algorithm of Galinier and Hao. The
performance of these algorithms were tested on a test suite consisting of
randomly generated 3-colorable graphs of various structural features, such as
graph size, type, edge density, and variability in sizes of color classes.
Furthermore, the test graphs were generated including the phase transition
where the graphs are hard to color. The purpose of the extensive experimental
work was threefold: to investigate the behavior of the tested algorithms in the
phase transition, to identify what impact hybridization with the DSatur
traditional heuristic has on the evolutionary algorithm, and to show how graph
structural features influence the performance of the graph-coloring algorithms.
The results indicate that the performance of the hybrid self-adaptive
evolutionary algorithm is comparable with, or better than, the performance of
the hybrid evolutionary algorithm which is one of the best graph-coloring
algorithms today. Moreover, the fact that all the considered algorithms
performed poorly on flat graphs confirms that this type of graphs is really the
hardest to color
Quantum Encoded Quantum Evolutionary Algorithm for the Design of Quantum Circuits
In this paper we present Quanrum Encoded Quantum Evolutionary Algorithm
(QEQEA) and compare its performance against a a classical GPU accelerated
Genetic Algorithm (GPUGA). The proposed QEQEA differs from existing quantum
evolutionary algorithms in several points: representation of candidates
circuits is using qubits and qutrits and the proposed evolutionary operators
can in theory be implemented on quantum computer provided a classical control
exists. The synthesized circuits are obtained by a set of measurements
performed on the encoding units of quantum representation. Both algorithms are
accelerated in GPGPU. The main target of this paper, is not to propose a
completely novel quantum genetic algorithm but to rather experimentally
estimate the advantages of certain components of genetic algorithm being
encoded and implemented in a quantum compatible manner. The algorithms are
compared and evaluated on several reversible and quantum circuits. The results
demonstrate that on one hand the quantum encoding and quantum implementation
compatible implementation provides certain disadvantages with respect to the
classical evolutionary computation. On the other hand, encoding certain
components in a quantum compatible manner could in theory allow to accelerate
the search. This acceleration would in turn counter weight the implementation
limitations.Comment: 7 figures, 8 pagge
Solution of the Multi-objective Economic and Emission Load Dispatch Problem Using Adaptive Real Quantum Inspired Evolutionary Algorithm
Economic load dispatch is a complex and significant problem in power generation. The inclusion of emission with economic operation makes it a Multi-objective economic emission load dispatch (MOEELD) problem. So it is a tough task to resolve a constrained MOEELD problem with antagonistic multiple objectives of emission and cost. Evolutionary Algorithms (EA) have been widely used for solving such complex multi-objective problems. However, the performance of EAs on such problems is dependent on the choice of the operators and their parameters, which becomes a complex issue to solve in itself. The present work is carried out to solve a Multi-objective economic emission load dispatch problem using a Multi-objective adaptive real coded quantum-inspired evolutionary algorithm (MO-ARQIEA) with gratifying all the constraints of unit and system. A repair-based constraint handling and adaptive quantum crossover operator (ACO) are used to satisfy the constraints and preserve the diversity of the suggested approach. The suggested approach is evaluated on the IEEE 30-Bus system consisting of six generating units. These results obtained for different test cases are compared with other reputed and well-known techniques
Variations on Memetic Algorithms for Graph Coloring Problems
Graph vertex coloring with a given number of colors is a well-known and
much-studied NP-complete problem.The most effective methods to solve this
problem are proved to be hybrid algorithms such as memetic algorithms or
quantum annealing. Those hybrid algorithms use a powerful local search inside a
population-based algorithm.This paper presents a new memetic algorithm based on
one of the most effective algorithms: the Hybrid Evolutionary Algorithm HEA
from Galinier and Hao (1999).The proposed algorithm, denoted HEAD - for HEA in
Duet - works with a population of only two individuals.Moreover, a new way of
managing diversity is brought by HEAD.These two main differences greatly
improve the results, both in terms of solution quality and computational
time.HEAD has produced several good results for the popular DIMACS benchmark
graphs, such as 222-colorings for \textless{}dsjc1000.9\textgreater{},
81-colorings for \textless{}flat1000\_76\_0\textgreater{} and even 47-colorings
for \textless{}dsjc500.5\textgreater{} and 82-colorings for
\textless{}dsjc1000.5\textgreater{}.Comment: 11 pages, 8 figures, 3 tables, 2 algorithm
Meta-Heuristic Solutions to a Student Grouping Optimization Problem faced in Higher Education Institutions
Combinatorial problems which have been proven to be NP-hard are faced in
Higher Education Institutions and researches have extensively investigated some
of the well-known combinatorial problems such as the timetabling and student
project allocation problems. However, NP-hard problems faced in Higher
Education Institutions are not only confined to these categories of
combinatorial problems. The majority of NP-hard problems faced in institutions
involve grouping students and/or resources, albeit with each problem having its
own unique set of constraints. Thus, it can be argued that techniques to solve
NP-hard problems in Higher Education Institutions can be transferred across the
different problem categories. As no method is guaranteed to outperform all
others in all problems, it is necessary to investigate heuristic techniques for
solving lesser-known problems in order to guide stakeholders or software
developers to the most appropriate algorithm for each unique class of NP-hard
problems faced in Higher Education Institutions. To this end, this study
described an optimization problem faced in a real university that involved
grouping students for the presentation of semester results. Ordering based
heuristics, genetic algorithm and the ant colony optimization algorithm
implemented in Python programming language were used to find feasible solutions
to this problem, with the ant colony optimization algorithm performing better
or equal in 75% of the test instances and the genetic algorithm producing
better or equal results in 38% of the test instances
A Novel Model for Optimized GSM Network Design
GSM networks are very expensive. The network design process requires too many
decisions in a combinatorial explosion. For this reason, the larger is the
network, the harder is to achieve a totally human based optimized solution. The
BSC (Base Station Control) nodes have to be geographically well allocated to
reduce the transmission costs. There are decisions of association between BTS
and BSC those impacts in the correct dimensioning of these BSC. The choice of
BSC quantity and model capable of carrying the cumulated traffic of its
affiliated BTS nodes in turn reflects on the total cost. In addition, the last
component of the total cost is due to transmission for linking BSC nodes to
MSC. These trunks have a major significance since the number of required E1
lines is larger than BTS to BSC link. This work presents an integer programming
model and a computational tool for designing GSM (Global System for Mobile
Communications) networks, regarding BSS (Base Station Subsystem) with optimized
cost.Comment: 6 Pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS 2009, ISSn 1947 5500, Impact Factor 0.423,
http://sites.google.com/site/ijcsis
Accelerator Codesign as Non-Linear Optimization
We propose an optimization approach for determining both hardware and
software parameters for the efficient implementation of a (family of)
applications called dense stencil computations on programmable GPGPUs. We first
introduce a simple, analytical model for the silicon area usage of accelerator
architectures and a workload characterization of stencil computations. We
combine this characterization with a parametric execution time model and
formulate a mathematical optimization problem. That problem seeks to maximize a
common objective function of 'all the hardware and software parameters'. The
solution to this problem, therefore "solves" the codesign problem:
simultaneously choosing software-hardware parameters to optimize total
performance.
We validate this approach by proposing architectural variants of the NVIDIA
Maxwell GTX-980 (respectively, Titan X) specifically tuned to a predetermined
workload of four common 2D stencils (Heat, Jacobi, Laplacian, and Gradient) and
two 3D ones (Heat and Laplacian). Our model predicts that performance would
potentially improve by 28% (respectively, 33%) with simple tweaks to the
hardware parameters such as adapting coarse and fine-grained parallelism by
changing the number of streaming multiprocessors and the number of compute
cores each contains. We propose a set of Pareto-optimal design points to
exploit the trade-off between performance and silicon area and show that by
additionally eliminating GPU caches, we can get a further 2-fold improvement.Comment: 10 pages, 4 figures, 2 table
The Progress, Challenges, and Perspectives of Directed Greybox Fuzzing
Most greybox fuzzing tools are coverage-guided as code coverage is strongly
correlated with bug coverage. However, since most covered codes may not contain
bugs, blindly extending code coverage is less efficient, especially for corner
cases. Unlike coverage-guided greybox fuzzers who extend code coverage in an
undirected manner, a directed greybox fuzzer spends most of its time allocation
on reaching specific targets (e.g., the bug-prone zone) without wasting
resources stressing unrelated parts. Thus, directed greybox fuzzing (DGF) is
particularly suitable for scenarios such as patch testing, bug reproduction,
and specialist bug hunting. This paper studies DGF from a broader view, which
takes into account not only the location-directed type that targets specific
code parts, but also the behaviour-directed type that aims to expose abnormal
program behaviours. Herein, the first in-depth study of DGF is made based on
the investigation of 32 state-of-the-art fuzzers (78% were published after
2019) that are closely related to DGF. A thorough assessment of the collected
tools is conducted so as to systemise recent progress in this field. Finally,
it summarises the challenges and provides perspectives for future research.Comment: 16 pages, 4 figure
The Design Of New Learning Automata For Problem Graph Coloring
Graph coloring issue, is one of satisfied existing constraints issues in the literature of artificial intelligence. Coloration apical includes assigning color to node graph so that any two adjacent vertices are isochromatic. The minimum number (colors numbers) that we assign to these graphs for coloring are called number of color. This issue is from the group of very difficult issue, NP – complete. Given the importance of graph coloring issue and its many uses, many algorithms suggested finding allowed coloration in graph. Among these can be noted in, exact algorithms, distributed algorithms, parallel algorithms, approximation algorithms and heuristic algorithms, …The concept of learning Automata at first was introduced by Tstlyn. He was interested in modeling the behavior of biological systems, and definite automata worked in a random environment, introduced as a model for learning. The aim of this research is to present new algorithm on the basis of learning automata to color with accuracy and high speed and the ability to learn graph vertices. The proposed method also has transfer chart and individual performance and this method was examined on the graph with low vertices and high vertices and medium vertices, on the bottom, a number of works steps and total dyes used for coloring of specific graph with optimization algorithms were matched. Evaluation results show high accuracy, speed and its performance of the proposed method is superior to other optimization methods.
A Hybrid multi-agent architecture and heuristics generation for solving meeting scheduling problem
Agent-based computing has attracted much attention as a promising technique for application domains that are distributed, complex and heterogeneous. Current research on multi-agent systems (MAS) has become mature enough to be applied as a technology for solving problems in an increasingly wide range of complex applications. The main formal architectures used to describe the relationships between agents in MAS are centralised and distributed architectures.
In computational complexity theory, researchers have classified the problems into the followings categories: (i) P problems, (ii) NP problems, (iii) NP-complete problems, and (iv) NP-hard problems.
A method for computing the solution to NP-hard problems, using the algorithms and computational power available nowadays in reasonable time frame remains undiscovered. And unfortunately, many practical problems belong to this very class. On the other hand, it is essential that these problems are solved, and the only possibility of doing this is to use approximation techniques.
Heuristic solution techniques are an alternative. A heuristic is a strategy that is powerful in general, but not absolutely guaranteed to provide the best (i.e. optimal) solutions or even find a solution. This demands adopting some optimisation techniques such as Evolutionary Algorithms (EA).
This research has been undertaken to investigate the feasibility of running computationally intensive algorithms on multi-agent architectures while preserving the ability of small agents to run on small devices, including mobile devices. To achieve this, the present work proposes a new Hybrid Multi-Agent Architecture (HMAA) that generates new heuristics for solving NP-hard problems. This architecture is hybrid because it is "semi-distributed/semi-centralised" architecture where variables and constraints are distributed among small agents exactly as in distributed architectures, but when the small agents become stuck, a centralised control becomes active where the variables are transferred to a super agent, that has a central view of the whole system, and possesses much more computational power and intensive algorithms to generate new heuristics for the small agents, which find optimal solution for the specified problem.
This research comes up with the followings: (1) Hybrid Multi-Agent Architecture (HMAA) that generates new heuristic for solving many NP-hard problems. (2) Two frameworks of HMAA have been implemented; search and optimisation frameworks. (3) New SMA meeting scheduling heuristic. (4) New SMA repair strategy for the scheduling process. (5) Small Agent (SMA) that is responsible for meeting scheduling has been developed. (6) “Local Search Programming” (LSP), a new concept for evolutionary approaches, has been introduced. (7) Two types of super-agent (LGP_SUA and LSP_SUA) have been implemented in the HMAA, and two SUAs (local and global optima) have been implemented for each type. (8) A prototype for HMAA has been implemented: this prototype employs the proposed meeting scheduling heuristic with the repair strategy on SMAs, and the four extensive algorithms on SUAs.
The results reveal that this architecture is applicable to many different application domains because of its simplicity and efficiency. Its performance was better than many existing meeting scheduling architectures. HMAA can be modified and altered to other types of evolutionary approaches
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