Fluid Genetic Algorithm (FGA)

다국어 초록 (Multilingual Abstract)

Genetic Algorithm (GA) has been one of the most popular methods for many challenging optimizationproblems when exact approaches are too computationally expensive. A review of the literature showsextensive research attempting to adapt and develop the standard GA. Nevertheless, the essence of GAwhich consists of concepts such as chromosomes, individuals, crossover, mutation, and others rarelyhas been the focus of recent researchers. In this paper method, Fluid Genetic Algorithm (FGA), some ofthese concepts are changed, removed, and furthermore, new concepts are introduced. The performanceof GA and FGA are compared through seven benchmark functions. FGA not only shows a better successrate and better convergence control, but it can be applied to a wider range of problems including multiobjectiveand multi-level problems. Also, the application of FGA for a real engineering problem, QuadricAssignment Problem (AQP), is shown and experienced.

참고문헌 (Reference)

1 Gibbs, M. S., "Using characteristics of the optimisation problem to determine the genetic algorithm population size when the number of evaluations is limited" 69 : 226-239, 2015

2 Uysal, A. K., "Text classification using genetic algorithm oriented latent semantic features" 41 : 5938-5947, 2014

3 Salomon, R., "Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms" 39 : 263-278, 1996

4 Liang, J., "Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization (Zhengzhou University, Zhengzhou China and Technical Report)" Nanyang Technological University 2013

5 Kakandikar, G. M., "Prediction and optimization of thinning in automotive sealing cover using genetic algorithm" 3 : 63-70, 2016

6 Auger, A., "Performance evaluation of an advanced local search evolutionary algorithm" IEEE 1777-1784, 2005

7 Azadeh, A., "Optimum estimation of missing values in randomized complete block design by genetic algorithm" 37 : 37-47, 2013

8 Van Veldhuizen, D. A., "Multiobjective evolutionary algorithms : Analyzing the state-of-the-art" 8 : 125-147, 2000

9 Oujebbour, F. -Z., "Multicriteria shape design of a sheet contour in stamping" 1 : 187-193, 2014

10 Qu, X., "Multi-objective robust optimization method for the modified epoxy resin sheet molding compounds of the impeller" 2016

11 Konak, A., "Multi-objective optimization using genetic algorithms: A tutorial" 91 : 992-1007, 2006

12 Deb, K., "Multi-objective genetic algorithms : Problem difficulties and construction of test problems" 7 : 205-230, 1999

13 Minaei-Bidgoli, B., "Mining numerical association rules via multi-objective genetic algorithms" 233 : 15-24, 2013

14 Zhang, Q., "Minimum time path planning for robotic manipulator in drilling/spot welding tasks" 2015

15 Talbi, E. -G., "Metaheuristics : From design to implementation" John Wiley & Sons 2009

16 Osman, I. H., "Meta-heuristics" Springer 1-21, 1996

17 Yazdani, M., "Lion optimization algorithm(LOA) : A nature-inspired metaheuristic algorithm" 3 : 24-36, 2016

18 Tsai, J. -T., "Hybrid Taguchi-genetic algorithm for global numerical optimization" 8 : 365-377, 2004

19 Glover, F. W., "Handbook of metaheuristics" Springer Science & Business Media 2006

20 Glover, F., "Genetic algorithms and tabu search : Hybrids for optimization" 22 : 111-134, 1995

21 Srinivas, M., "Genetic algorithms : A survey" 27 : 17-26, 1994

22 Younes, M., "Genetic algorithm-particle swarm optimization (GA-PSO) for economic load dispatch" 4 : 2011

23 Maulik, U., "Genetic algorithm-based clustering technique" 33 : 1455-1465, 2000

24 Settles, M., "Breeding swarms: A GA/PSO hybrid" ACM 161-168, 2005

25 Keramatia, A., "Addressing churn prediction problem with Metaheuristic, Machine learning, Neural Network and data mining techniques: A case study of a telecommunication company" 171-, 2014

26 Ho, W. -H., "Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm" 36 : 3216-3222, 2009

27 Jafari-Marandi, R., "A system of system approach for smart complex energy system operation decision" American Society of Mechanical Engineers V01BT02A04-V001BT002A, 2015

28 Loiola, E. M., "A survey for the quadratic assignment problem" 176 : 657-690, 2007

29 Tavakkoli-Moghaddam, R., "A novel multi-objective genetic algorithm for cell formation problems" 2013

30 Rabbani, M., "A new hybrid GA-PSO method for solving multi-period inventory routing problem with considering financial decisions" 6 : 909-929, 2013

31 Wang, K., "A new fuzzy genetic algorithm based on population diversity" IEEE 108-112, 2001

32 Chica, M., "A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand" 58 : 55-68, 2016

33 Yang, G., "A hybrid approach based on stochastic competitive Hopfield neural network and efficient genetic algorithm for frequency assignment problem" 39 : 104-116, 2016

34 Tate, D. M., "A genetic approach to the quadratic assignment problem" 22 : 73-83, 1995

35 Long, Q., "A flow-based three-dimensional collaborative decision-making model for supply-chain networks" 2016

36 Deb, K., "A fast and elitist multiobjective genetic algorithm : NSGA-II" 6 : 182-197, 2002

37 Jafari-Marandi, R., "A distributed decision framework for building clusters with different heterogeneity settings" 165 : 393-404, 2016

38 Yu, H., "A combined genetic algorithm/simulated annealing algorithm for large scale system energy integration" 24 : 2023-2035, 2000