PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

"An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization"

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Designing an efficient optimization method which also has a simple structure is generally required by users for its applications to a wide range of practical problems. In this research, an enhanced differential evolution algorithm with adaptation of switching crossover strategy (DEASC) is proposed as a general-purpose population-based optimization method for continuous optimization problems. DEASC extends the solving ability of a basic differential evolution algorithm (DE) whose performance significantly depends on user selection of the control parameters: scaling factor, crossover rate and population size. Like the original DE, the proposed method is aimed at efficiency, simplicity and robustness. The appropriate population size is selected to work in accordance with good choices of the scaling factors. Then, the switching crossover strategy of using low or high crossover rates are incorporated and adapted to suit the problem being solved. In this manner, the adaptation strategy is just a convenient add-on mechanism. To verify the performance of DEASC, it is tested on several benchmark problems of various types and difficulties, and compared with some well-known methods in the literature. It is also applied to solve some practical systems of nonlinear equations. Despite its much simpler algorithmic structure, the experimental results show that DEASC greatly enhances the basic DE. It is able to solve all the test problems with fast convergence speed and overall outperforms the compared methods which have more complicated structures. In addition, DEASC also shows promising results on high dimensional test functions.
Rocznik
Strony
97--124
Opis fizyczny
Bibliogr. 31 poz., rys., tab.
Twórcy
  • Deparment of Mathematics, Faculty of Science, Khon Kaen University, 40002, Khon Kaen, Thailand
  • Deparment of Mathematics, Faculty of Science, Khon Kaen University, 40002, Khon Kaen, Thailand
Bibliografia
  • [1] Al-Dabbagh R.D., Neri F., Idris N., Baba M.S., Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy, Swarm and Evolutionary Computation, 43, 2018 , 284-311.
  • [2] Boussaïd I., Lepagnot J., Siarry P., A survey on optimization metaheuristics, Information sciences, 237, 2013 , 82-117.
  • [3] Brest J., Greiner S., Bokovi B., Mernik M., Zumer V., Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems, IEEE Trans Evol Comput, 10, 6, 2006, 646-657.
  • [4] Das S., Konar A., Chakraborty U.K., Two improved differential evolution schemes for faster global search, in : Proceedings of the 2005 conference on genetic and evolutionary computation, 2005, 991-998.
  • [5] Das S., Mullick S.S., Suganthan P., Recent advances in differential evolution - An updated survey, Swarm and Evolutionary Computation, 27, 2016, 1-30.
  • [6] Dennis J.E., Schnabel R.B., Numerical methods for unconstrained optimization and nonlinear equations, SIAM,Philadelphia, 1996.
  • [7] Gamperle R. , Muller S.D., Koumoutsakos P., A parameter study for differential evolution, in: A. Gremla, N. E. Mastorakis (eds.), Advances in intelligent systems, fuzzy systems, evolutionary computation, WSEAS Press, Interlaken, 2002, 293-298.
  • [8] Hamm L., Brorsen B.W., Hagan M.T., Comparison of stochastic global optimization methods to estimate neural network weights, Neural Process Lett, 26, 2007, 145-158.
  • [9] Hirsch M.J., Pardalos P.M., Resende M.G.C., Solving systems of nonlinear equations with continuous GRASP, Nonlinear Analysis: Real World Applications, 10, 2009, 2000-2006.
  • [10] Lampinen J., Zelinka I., On stagnation of the differential evolution algorithm, in: R. Matouek, P. Omera (eds.), Proceedings of Mendel 2000, 6th international conference on soft computing, 2000, 76-83.
  • [11] Leon M., Xiong N., Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters, Journal of artificial intelligence and soft computing research, 6, 2, 2016, 103-118.
  • [12] Li W., Li S., Chen Z., Zhong L., Ouyang C., Self-feedback differential evolution adapting to fitness landscape characteristics, Soft Comput, 23, 2019, 1151-1163.
  • [13] Mallipeddi R., Suganthan P.N., Empirical study on the effect of population size on differential evolution algorithm, in: Proceeding of the IEEE congress on evolutionary computation (CEC-2008), 2008, 3663-3670.
  • [14] Meintjes K., Morgan A., Chemical equilibrium systems as numerical test problems, ACM Transaction on Mathematical Software, 16, 1990, 143-151.
  • [15] Mezura-Montes E., Velzquez-Reyes J., Coello Coello C.A., A comparative study of differential evolution variants for global optimization, in: Proceedings of the 8th annual genetic and evolutionary computation conference (GECCO-2006), 2006, 485-492.
  • [16] Morgan A., Shapiro V., Box-bisection for solving second-degree systems and the problem of clustering, ACM Transaction on Mathematical Software, 13, 1987 , 152-167.
  • [17] Nanda S.J., Panda G., A survey on nature inspired metaheuristic algorithms for partitional clustering, Swarm and Evolutionary Computation, 16, 2014, 1-18.
  • [18] Neri F., Tirronen V., Recent advances in differential evolution: A survey and experimental analysis, Artif Intell Rev, 33, 2010, 61-106.
  • [19] Oliveira H.A., Petraglia A., Solving nonlinear systems of functional equations with fuzzy adaptive simulated annealing, Applied Soft Computing, 13, 2013, 4349-4357.
  • [20] Ortega J.M., Rheinboldt W.C., Iterative solution of nonlinear equations inseveral variables, SIAM, Philadelphia, 2000.
  • [21] Pramanik S., Kinematic synthesis of a six-member mechanism for automotive steering, ASME Journal of Mechanical Design, 124, 2002, 642-645.
  • [22] Qin A.K., Huang V.L., Suganthan P.N., Differential evolution algorithm with strategy adaptation for global numerical optimization, IEEE Trans Evol Comput, 13, 2, 2009, 398-417.
  • [23] Ronkkonen J., Kukkonen S., Price K.V., Real parameter optimization with differential evolution, in : Proceedings of the IEEE congress evolutionary computation (CEC-2005) vol 1, IEEE Press, 2005, 506-513.
  • [24] Storn R., Differential evolution research-trends and open questions, in: U. K. Chakraborty (ed.), Advances in Differential Evolution, Springer, Berlin, 2008, 1-31.
  • [25] Storn R., Price K., Differential evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report TR-95-012, ICSI, 1995.
  • [26] Storn R., Price K., Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces, J Glob Optim, 11, 4, 1997, 341-359.
  • [27] Venter G., Review of optimization techniques, in: R. Blockley, S. Wei (eds.), Encyclopedia of aerospace engineering, Wiley and Sons, 2010, doi: 10.1002/9780470686652.eae495.
  • [28] Verschelde J., Verlinden P., Cools R., Homotopies exploiting newton polytopes for solving sparse polynomial systems, SIAM J. Numer. Anal, 31, 1994, 915-930.
  • [29] Wongpen J., Wetweerapong J., Puphasuk P., Finding a maximum clique in social networks using a modified differential evolution algorithm, WSEAS Transactions on Systems and Control, 14, 2019 , 333-342.
  • [30] Zhang J., Sanderson A.C., JADE: adaptive differential evolution with optional external archive, IEEE Trans Evol Comput, 13, 5, 2009, 945-958.
  • [31] Zielinski K., Weitkemper P., Laur R., Kammeyer K., Parameter study for differential evolution using a power allocation problem including interference cancellation, in: IEEE congress on evolutionary computation (CEC-2006), 2006, 1857-1864.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-74365109-6f5d-4197-bd69-c67f0175a44d
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.