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Title: optimization of combinatorial problems with parallel hybrid evolutionary algorithms


1
optimization of combinatorial problems with
parallel hybrid evolutionary algorithms
  • Tansel Dökeroglu, Ph.D.
  • July, 2015

2
Content
  • Problem definition
  • Metahueristics
  • Genetic Algorithms and Tabu Search
  • Parallel Algorithms and Message Passing Interface
  • Proposed Parallel Hybrid Algorithm
  • Experimental Setup and Results
  • Conclusion and Future Work

3
Problem Definition
  • Combinatorial optimization is an area of research
    at the intersection of computer science, applied
    mathematics, and operations research.
  • The most widely studied problems of this area
    are
  • The Traveling Salesman
  • Bin Packing
  • Data Allocation
  • The Facility Layout Problem
  • Quadratic Assignment Problem

4
The Quadratic Assignment Problem (QAP)
This assignment can be written as a permutation
such that p2,1,4,3. The exact solution of the
QAP problem for size 35 is peformed with hundreds
of processors by working for months. Large QAP
instances are still optimally unsolvable.
5
Formal Definition of the QAP
6
NP-Hard problems and metaheuristics
7
Genetic Algorithms
Generations (iterations)
8
Crossover and Mutation Operators
9
Generations
10
Tabu Search Algorithm
  • A neighborhood is constructed to identify
    adjacent solutions that can be reached from
    current solution.
  • Classifies a subset of the moves as forbidden (or
    tabu).
  • The classification depends on the history of the
    search, and particularly on the frequency that
    certain move or solution components, called
    attributes, have participated in generating past
    solutions.
  • With an attractive evaluation where it would
    result in a solution better than any visited so
    far, its tabu classification may be overridden,
    aspiration criterion.

11
Parameters of Tabu Search
  • Neighborhood structure
  • Local search procedure
  • Aspiration conditions
  • Form of tabu moves
  • Addition of a tabu move
  • Maximum size of tabu list
  • Number of failures

12
Why do we need parallel programs? (from the
perspective of Moores law)
the of transistors in an integrated circuit has
doubled every two years
13
Message Passing Interface
  • Message Passing Interface (MPI) is a standard and
    portable message-passing system designed to
    function on a wide variety of parallel computers.
  • There are several well-tested and efficient
    implementations of MPI which are portable and
    scalable for large-scale parallel applications.
  • The standard defines the syntax and semantics of
    a core of library routines useful to a wide range
    of users writing portable message-passing
    programs in different computer programming
    languages such as Fortran, C, C and Java.

14
The communication topology of the proposed
algorithm
15
Proposed Algorithm
migrate individuals
Global best
Genetic Algorithm Phase (at each slave processor)
Master Node
population
Robust Tabu Engine (at each slave processor)
best individual
16
Experimental Setup and Performance Evaluation
17
QAP Benchmark Instances http//www.opt.math.tu-gra
z.ac.at/qaplib/inst.html
There exist problem instances having size 12 n
256 136 problem instances and 111 solutions
18
46 nodes, each with two CPUs, giving 92 CPUs.
Intel Xeon 5110 Dual-Core CPU (1.60 GHz, 4 MB
L2 Cache, 1066 MHz FSB) Each CPU has four cores
giving a total number of 368 processors. Each
node has 16 GB of RAM giving 736 GB of total
memory high-bandwidth communication among the
HPC nodes, Gigabit Ethernet Switches, and
Infiniband switch.
19
Setting parameters for of individuals and
generations
in order to prevent stagnation to local optima
20
Parameters settings for Tabu Search Algorithm
Phase
For small problem instances, the small parameter
settings are used, while the larger parameter
settings are used for harder/larger problems
21
improvement of the solution quality of as the
number of generations, populations, and
processors are increased
22
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23
Comparing the results with the state-of-the-art
parallel algorithms
24
Conclusion
  • A robust algorithm is developed with 0.049
    error deviation for hard/large problem instances
    of the QAP.
  • A wider fitness landscape analysis is enabled
    with parallel computation for the QAP.
  • Execution time of the proposed algorithm is
    reasonable (it can find (near-) optimal solutions
    in minutes rather than days or months).
  • The proposed algorithm is reported to be among
    the best performing ones in the literature.
  • The hybridization of metaheuristics is proved to
    be an efficient approach for the solution of the
    QAP.

25
Future work
  • Enhancing with machine learning techniques such
    as reinforcement learning.
  • Hyper-heuristics that execute several heuristics
    on the problem will be implemented .
  • Migrating the existing code to CUDA platform. A
    more cost-effective way of solution.

CUDA is a parallel computing platform and
programming model that enables dramatic increases
in computing performance by harnessing the power
of the graphics processing unit (GPU).
GeForce GTX 760 A Mid-Range GPU with 1152 CUDA
cores
26
Journal papers
  • Dokeroglu T., (2015) Hybrid teaching-learning-base
    d optimization algorithms for the Quadratic
    Assignment Problem, Computers and Industrial
    Engineering. 85 (2015) 86-101.
  • Dokeroglu T., Bayir M.A., Cosar A., (2015) Robust
    algorithms for exploiting the common tasks of
    relational cloud databases, Applied Soft
    Computing, Vol 30 72-82.
  • Dokeroglu, Tansel, and Ahmet Cosar. (2014)
    Optimization of one-dimensional Bin Packing
    Problem with island parallel grouping genetic
    algorithms. Computers Industrial Engineering 75
    (2014) 176-186.
  • Dokeroglu, T., Sert, S.A., and Cinar, M.S. (2014)
    Evolutionary multiobjective query workload
    optimization of Cloud data warehouses, The
    Scientific World Journal.
  • Tosun, U., Dokeroglu, T., Cosar, A. (2013). A
    robust island parallel genetic algorithm for the
    quadratic assignment problem. International
    Journal of Production Research, 51(14),
    4117-4133.
  • Dokeroglu, T., Ozal, S., Bayir, M. A., Cinar, M.
    S., Cosar, A. (2014). Improving the performance
    of Hadoop Hive by sharing scan and computation
    tasks. Journal of Cloud Computing, 3(1), 1-11.

27
Proceeding papers
  • Dokeroglu, T., Cosar, A. (2014), "Integer Linear
    Programming Solution Model for the Multiple Query
    Optimization Problem" ISCIS October 27-28th,
    2014, Krakow, Poland.
  • Dokeroglu, T., Sert, S.A., Cinar, M.S., and
    Cosar, A. (2014). Designing Cloud Data Warehouses
    using Multiobjective Evolutionary Algorithms, ACM
    International Conference on Agents and Artificial
    Intelligence (ICAART) Eseo, Angers, Loire Valley,
    France.
  • Dokeroglu, T. (supervised by Ahmet Cosar) (2012).
    Parallel Genetic Algorithms for the Optimization
    of Multi-Way Chain Join Queries of Distributed
    Databases 38th VLDB Ph.D. Workshop, August 27-31,
    Istanbul/TURKEY.
  • Dokeroglu, T., Tosun, U., and Cosar, A. (2012).
    Particle Swarm Intelligence as a Novel Heuristic
    for the Optimization of Distributed Database
    Queries, The 6th International Conference on
    Application of Information and Communication
    Technologies AICT2012 Georgia, Tbilisi, 17-19 .
  • Dokeroglu, T and Cosar, A. (2011). Dynamic
    Programming with Ant Colony Optimization
    Metaheuristic for The Optimization of Distributed
    Database Queries, Proceedings of the 26th ISCIS,
    London, UK.
  • Dokeroglu,T., Tosun, U., and Cosar, A. (2013).
    Evaluating the Performance of Recombination
    Operators with Island Parallel Genetic
    Algorithms, International Federation of Automatic
    Control (IFAC), Saint Petersburg, Russia.
  • Dokeroglu, T. Tosun, U., and Cosar, A. (2012).
    Parallel Optimization with Mutation Operator for
    the Quadratic Assignment Problem Proceedings of
    WIVACE, Italian Workshop on Artificial Life and
    Evolutionary Computation, Parma/Italy.

28
Thank You
29
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