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TY - JOUR
T1 - Military antenna design using simple and competent genetic algorithms
JO - Mathematical and Computer Modelling
VL - 43
IS - 9-10
SP - 990
EP - 1022
PY - 2006/5//
T2 - Optimization and Control for Military Applications
AU - Santarelli, Scott
AU - Yu, Tian-Li
AU - Goldberg, David E.
AU - Altshuler, Edward
AU - O'Donnell, Teresa
AU - Southall, Hugh
AU - Mailloux, Robert
SN - 0895-7177
M3 - doi: DOI: 10.1016/j.mcm.2005.05.024
UR -
کد:
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KW - Genetic algorithm (GA)
KW - Optimization technique
KW - Evolutionary computation
KW - Competent genetic algorithm
KW - Hierarchical Bayesian optimization algorithm (hBOA)
AB -
Over the past decade, the Air Force Research Laboratory (AFRL) Antenna Technology Branch at Hanscom AFB has employed the simple genetic algorithm (SGA) as an optimization tool for a wide variety of antenna applications. Over roughly the same period, researchers at the Illinois Genetic Algorithm Laboratory (IlliGAL) at the University of Illinois at Urbana Champaign have developed GA design theory and advanced GA techniques called competent genetic algorithms--GAs that solve hard problems quickly, reliably, and accurately. Recently, under the guidance and direction of the Air Force Office of Scientific Research (AFOSR), the two laboratories have formed a collaboration, the common goal of which is to apply simple, competent, and hybrid GA techniques to challenging antenna problems.
This paper is composed of two parts. The first part of this paper summarizes previous research conducted by AFRL at Hanscom for which SGAs were implemented to obtain acceptable solutions to several antenna problems. This research covers diverse areas of interest, including array pattern synthesis, antenna test-bed design, gain enhancement, electrically small single bent wire elements, and wideband antenna elements.
The second part of this paper starts by briefly reviewing the design theory and design principles necessary for the invention and implementation of fast, scalable genetic algorithms. A particular procedure, the hierarchical Bayesian optimization algorithm (hBOA) is then briefly outlined, and the remainder of the paper describes collaborative efforts of AFRL and IlliGAL to solve more difficult antenna problems. In particular, recent results of using hBOA to optimize a novel, wideband overlapped subarray system to achieve -35 dB sidelobes over a 20% bandwidth. The problem was sufficiently difficult that acceptable solutions were not obtained using SGAs. The case study demonstrates the utility of using more advanced GA techniques to obtain acceptable solution quality as problem difficulty increases.
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TY - JOUR
T1 - Review of utilization of genetic algorithms in heat transfer problems
JO - International Journal of Heat and Mass Transfer
VL - 52
IS - 9-10
SP - 2169
EP - 2188
PY - 2009/4//
T2 -
AU - Gosselin, Louis
AU - Tye-Gingras, Maxime
AU - Mathieu-Potvin, François
SN - 0017-9310
M3 - doi: DOI: 10.1016/j.ijheatmasstransfer.2008.11.015
UR -
کد:
برای مشاهده محتوا ، لطفا وارد شوید یا ثبت نام کنید
KW - Genetic Algorithms (GA)
KW - Optimization
KW - Heat transfer
KW - Inverse problems
KW - Design
KW - Correlation
KW - Evolutionary algorithms
AB -
This review presents when and how Genetic Algorithms (GAs) have been used over the last 15 years in the field of heat transfer. GAs are an optimization tool based on Darwinian evolution. They have been developed in the 1970s, but their utilization in heat transfer problems is more recent. In particular, the last couple of years have seen a sharp increase of interest in GAs for heat transfer related optimization problems. Three main families of heat transfer problems using GAs have been identified: (i) thermal systems design problems, (ii) inverse heat transfer problems, and (iii) development of heat transfer correlations. We present here the main features of the problems addressed with GAs including the modeling, number of variables, and GA settings. This information is useful for future use of GAs in heat transfer. Future possibilities and accomplishments of GAs in heat transfer are also drawn.
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TY - JOUR
T1 - Development of a customized processor architecture for accelerating genetic algorithms
JO - Microprocessors and Microsystems
VL - 31
IS - 5
SP - 347
EP - 359
PY - 2007/8/1/
T2 -
AU - Kavvadias, Nikolaos
AU - Giannakopoulou, Vasiliki
AU - Nikolaidis, Spiridon
SN - 0141-9331
M3 - doi: DOI: 10.1016/j.micpro.2006.12.002
UR -
کد:
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KW - 89.20.Ff
KW - Embedded systems
KW - Field-programmable gate arrays
KW - Genetic algorithms
KW - Application-specific processors
KW - Hardware description languages
AB -
In this paper, a new programmable RISC processor architecture named VGP-I is proposed, aiming to the acceleration of genetic algorithms in embedded systems. Compared to other GA engines, the VGP-I specification defines a compact instruction set supporting multiple operator types, with scalable instruction encodings, programmer-visible and auxiliary registers and optional extensions. Apart from the programmable accelerator approach, VGP-I instructions have been tightly integrated to the Nios II soft-core processor as well. For performance assessment, a cycle-accurate reference VGP-I model has been developed while VGP-I subsets have been realized on a prototype microarchitecture and as Nios II custom instructions, both verified on programmable logic. Performance improvements on the execution of genetic operators are typically at the level of two orders of magnitude with application kernels written in ANSI C being accelerated by about 20× due to the usage of GA instruction set extensions.
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TY - JOUR
T1 - Extensive experiments with hybrid genetic algorithms for the solution of the quadratic assignment problem
JO - Computers & Operations Research
VL - 35
IS - 3
SP - 717
EP - 736
PY - 2008/3//
T2 - Part Special Issue: New Trends in Locational Analysis
AU - Drezner, Zvi
SN - 0305-0548
M3 - doi: DOI: 10.1016/j.cor.2006.05.004
UR -
کد:
برای مشاهده محتوا ، لطفا وارد شوید یا ثبت نام کنید
KW - Genetic algorithms
KW - Memetic algorithms
KW - Tabu search
KW - Simple tabu
KW - Quadratic assignment
AB -
In this paper we perform extensive computational experiments solving quadratic assignment problems using various variants of a hybrid genetic algorithm. We introduce a new tabu search (simple tabu). We compared the modified robust tabu and the simple tabu as improvement algorithms in a hybrid genetic algorithm with other tabu searches (concentric tabu, ring moves, all moves, robust tabu) with superior results. We also tested several modifications of the hybrid genetic algorithm and all of them produced good results.
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TY - JOUR
T1 - A hybrid real-parameter genetic algorithm for function optimization
JO - Advanced Engineering Informatics
VL - 20
IS - 1
SP - 7
EP - 21
PY - 2006/1//
T2 -
AU - Hwang, Shun-Fa
AU - He, Rong-Song
SN - 1474-0346
M3 - doi: DOI: 10.1016/j.aei.2005.09.001
UR -
کد:
برای مشاهده محتوا ، لطفا وارد شوید یا ثبت نام کنید
KW - Genetic algorithm
KW - Simulated annealing
KW - Adaptive mechanism
KW - Function optimization
KW - Design optimization
AB -
One drawback of genetic algorithm is that it may spend much computation time in the encoding and decoding processes. Also, since genetic algorithm lacks hill-climbing capacity, it may easily fall in a trap and find a local minimum not the true solution. In this paper, a novel adaptive real-parameter simulated annealing genetic algorithm (ARSAGA) that maintains the merits of genetic algorithm and simulated annealing is proposed. Adaptive mechanisms are also included to insure the solution quality and to improve the convergence speed. The performance of the proposed operators has been discussed in detail and compared to other operators, and the performance of the proposed algorithm is demonstrated in 16 benchmark functions and two engineering optimization problems. Due to their versatile characteristics, these examples are suitable to test the ability of the proposed algorithm. The results indicate that the global searching ability and the convergence speed of this novel hybrid algorithm are significantly better, even though small population size is used. Also, the proposed algorithm has good application to engineering optimization problems. Hence, the proposed algorithm is efficient and improves the drawbacks of genetic algorithm.
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TY - JOUR
T1 - Improved genetic algorithm for multidisciplinary optimization of composite laminates
JO - Computers & Structures
VL - 86
IS - 19-20
SP - 1894
EP - 1903
PY - 2008/10//
T2 -
AU - Park, Chung Hae
AU - Lee, Woo Il
AU - Han, Woo Suck
AU - Vautrin, Alain
SN - 0045-7949
M3 - doi: DOI: 10.1016/j.compstruc.2008.03.001
UR -
کد:
برای مشاهده محتوا ، لطفا وارد شوید یا ثبت نام کنید
KW - Genetic algorithm (GA)
KW - Multidisciplinary optimization
KW - Memory
KW - Permutation
KW - Local learning
AB -
We suggest new approaches to reduce the number of fitness function evaluations in genetic algorithms (GAs) applied to multidisciplinary optimization of composite laminates. In the stacking sequence design of laminated structures, the design criteria are classified into two groups, which are layer combination dependent criteria and layer sequence dependent criteria. The memory approach is employed to lessen the number of fitness function evaluations for the identical design individuals that appear during the search. The permutation operator with local learning or random shuffling is applied to the same design individual to improve the fitness for layer sequence dependent criterion, while maintaining the same performance for layer combination dependent criterion. The numerical efficiency of the present method is validated by the sample problem of weight minimization of composite laminated plate under multiple design constraints.
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TY - JOUR
T1 - A new genetic algorithm for solving nonconvex nonlinear programming problems
JO - Applied Mathematics and Computation
VL - 199
IS - 1
SP - 186
EP - 194
PY - 2008/5/15/
T2 -
AU - Aryanezhad, M.B.
AU - Hemati, Mohammad
SN - 0096-3003
M3 - doi: DOI: 10.1016/j.amc.2007.09.047
UR -
کد:
برای مشاهده محتوا ، لطفا وارد شوید یا ثبت نام کنید
KW - Genetic algorithm
KW - Nonconvex programming
KW - Decomposition
KW - Sub problems
KW - Global optimum
AB -
In nonlinear programming problems (especially nonconvex problems), attaining the global optimum is crucial. To reach this purpose, the current paper represents a new genetic algorithm for solving nonconvex nonlinear programming problems. The new method is simpler and more intuitive than the existing models and finds the global optimum of the problem in a reasonable time. The proposed technique, to attain the global optimum of problem (especially in large scale problems) instead of increasing the size of population which usually conduces to the curse of dimensionality - that is widespread in usual genetic algorithms - decomposes the main problem into several sub problems, but with lower size of population in each sub problem. This decomposition has been formed in such a way that it could envelop the total solution space. The proposed genetic algorithm, which determines the sufficient sub problems, could converge to global optimum of problem. To be able to measure the proposed genetic algorithm, several problems have been solved in different spectrums. Herein, it has been shown that the proposed technique, both in run time and in solution quality, is preferred to the usual genetic algorithm in this domain (nonlinear continuous programming problems).
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TY - JOUR
T1 - Parallelisation of genetic algorithms for the 2-page crossing number problem
JO - Journal of Parallel and Distributed Computing
VL - 67
IS - 2
SP - 229
EP - 241
PY - 2007/2//
T2 -
AU - He, Hongmei
AU - Skora, Ondrej
AU - Salagean, Ana
AU - Mنkinen, Erkki
SN - 0743-7315
M3 - doi: DOI: 10.1016/j.jpdc.2006.08.002
UR -
کد:
برای مشاهده محتوا ، لطفا وارد شوید یا ثبت نام کنید
KW - 2-page crossing number
KW - Parallel genetic algorithms
KW - Evaluation measures
AB -
Genetic algorithms (GAs) have been applied to solve the 2-page crossing number problem successfully, but since they work with one global population, the search time and space are limited. Parallelisation provides an attractive prospect to improve the efficiency and solution quality of GAs. This paper investigates the complexity of parallel genetic algorithms (PGAs) based on two evaluation measures: computation time to communication time and population size to chromosome size. Moreover, the paper unifies the framework of PGA models with the function PGA (subpopulation size, cluster size, migration period, topology), and explores the performance of PGAs for the 2-page crossing number problem.
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TY - JOUR
T1 - Properties of a genetic algorithm equipped with a dynamic penalty function
JO - Computational Materials Science
VL - 45
IS - 1
SP - 77
EP - 83
PY - 2009/3//
T2 - Selected papers from the E-MRS 2007 Fall Meeting Symposium G: Genetic Algorithms in Materials Science and Engineering - GAMS-2007
AU - Paszkowicz, W.
SN - 0927-0256
M3 - doi: DOI: 10.1016/j.commatsci.2008.04.033
UR -
کد:
برای مشاهده محتوا ، لطفا وارد شوید یا ثبت نام کنید
KW - Genetic algorithm
KW - Penalty function
KW - Optimisation
KW - Powder pattern
KW - Indexing
KW - Multiple extrema
KW - Convergence
AB -
A genetic algorithm aiming for finding the global minimum and multiple deep local minima of a function exhibiting a complex landscape is studied. A feedback dynamic penalty function is used as a means to direct the algorithm to look for new local minima. The penalty is applied in close vicinity of all minima found before the current search stage. The last one, where the population tends to be trapped in, is treated smoothly. The penalty becomes progressively active there causing that the population progressively transfers outside the trapping area. The method ascertains that, unlike in more classical approaches, after finding the global minimum and a number of local ones on the way, the algorithm continues the exploration and identifies new local minima. Performance tests are described for a task of indexing of a powder diffraction pattern. The presented way of constructing the penalty function is to some extent problem specific, but the applied scheme may be adapted to other global search and optimisation problems, in particular to those requiring identification of multiple deep local minima.
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