Adaptive differential evolution algorithm with a pheromone-based learning strategy for global continuous optimization

• Autorzy: Singsathid Pirapong , Puphasuk Pikul , Wetweerapong Jeerayut

Identyfikatory

DOI

10.2478/fcds-2023-0010

• Języki publikacji: EN

Abstrakty

EN

Differential evolution algorithm (DE) is a well-known population-based method for solving continuous optimization problems. It has a simple structure and is easy to adapt to a wide range of applications. However, with suitable population sizes, its performance depends on the two main control parameters: scaling factor (F) and crossover rate (CR). The classical DE method can achieve high performance by a time-consuming tunning process or a sophisticated adaptive control implementation. We propose in this paper an adaptive differential evolution algorithm with a pheromone-based learning strategy (ADE-PS) inspired by ant colony optimization (ACO). The ADE-PS embeds a pheromone-based mechanism that manages the prob- abilities associated with the partition values of F and CR. It also introduces a resetting strategy to reset the pheromone at a specific time to unlearn and relearn the progressing search. The preliminary experiments find a suitable number of subintervals (ns) for partitioning the control parameter ranges and the reset period (rs) for resetting the pheromone. Then the comparison experiments evaluate ADE-PS using the suitable ns and rs against some adaptive DE methods in the literature. The results show that ADE-PS is more reliable and outperforms several well-known methods in the literature.

Słowa kluczowe

EN

differential evolution algorithm; ant colony optimization; pheromone strategy; optimization

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2023
• Tom: Vol. 48, No. 2
• Strony: 243--266
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Singsathid Pirapong

• Deparment of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand

autor

Puphasuk Pikul

• Deparment of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand

autor

Wetweerapong Jeerayut

• Deparment of Mathematics, Faculty of Science, Khon Kaen University, 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] Brest J., Greiner S., Boskovic B., Mernik M., Zumer V., Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems, IEEE Transactions on Evolutionary Computation, 10, 2006, 646-657.
• [3] Brest J., Bošković B., Žumer V., An improved self-adaptive differential evolution algorithm in single objective constrained real-parameter optimization, 2010 IEEE Congress on Evolutionary Computation (CEC), 2010, 1-8.
• [4] Brest J., Maučec M. S., Bošković B., iL-SHADE: Improved L-SHADE algorithm for single objective real-parameter optimization, 2016 IEEE Congress on Evolutionary Computation (CEC), 2016, 1188-1195.
• [5] Brest J., Maučec M. S., and Bošković B., Single objective real-parameter optimization: Algorithm jSO, 2017 IEEE Congress on Evolutionary Computation (CEC), 2017, 1311-1318.
• [6] Cheng J., Pan Z., Liang H., Gao Z., Gao J., Differential evolution algorithm with fitness and diversity ranking-based mutation operator, Swarm and Evolutionary Computation, 61, 2021, 100816.
• [7] Das S., Suganthan P. N., Differential evolution : A survey of the state-of-the-art, IEEE Transactions on Evolutionary Computation, 15, 2011, 4-31.
• [8] Das S., Mullick S. S., Suganthan P. N., Recent advances in differential evolution - An updated survey, Swarm and Evolutionary Computation, 27, 2016, 1-30.
• [9] Dorigo M., Stützle T., Ant colony optimization, MIT Press, Cambridge, MA, 2004.
• [10] Dorigo M., Socha K., Ant colony optimization for continuous domains, European Journal of Operational Research, 185, 2008, 1155-1173.
• [11] Hinterding R., Michalewicz Z., Eiben A. E., Adaptation in evolutionary computation: A survey, in: Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC'97), 1997, 65-69.
• [12] Leon M., Xiong N., Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters, J. Artif. Intell. Soft Comput. Res., 6, 2016, 103-118.
• [13] Mallipeddi R., Suganthan P. N., Pan Q. K., Tasgetiren M. F., Differential evolution algorithm with ensemble of parameters and mutation strategies, Appl. Soft Comput., 11, 2011, 1679-1696.
• [14] Meng Z., Pan J. S., PaDE: An enhanced differential evolution algorithm with novel control parameter adaptation schemes for numerical optimization, Knowledge-Based Systems, 168, 2019, 80-99.
• [15] Price K., Storn R., Differential evolution: a simple evolution strategy for fast optimization, Dr Dobb's. J. Softw. Tools, 22, 1997, 18-24.
• [16] Qin A. K., Huang V. L., Suganthan P. N., Differential evolution algorithm with strategy adaptation for global numerical optimization, IEEE Congress on Evolutionary Computation, 13, 2009, 398-417.
• [17] Singsathid P., Wetweerapong J., Solving Continuous Optimization Problems by Ant Colony Optimization with Domain Partitioning Technique, in: Proceedings of the 23rd annual meeting in mathematics (AMM2018), 2018, 257-262.
• [18] Storn R., Price K., Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report TR-95-012, ICSI, Berkeley, 1995.
• [19] Storn R., Price K., Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11, 1997, 341-359.
• [20] Storn R., Differential evolution research-trends and open question, in: U. K. Chakraborty (ed.), Advances in Differential Evolution, Springer, Berlin, 2008, 1-31.
• [21] Suganthan P. N., Hansen N., Liang J., Deb K., Chen Y., Auger A., Tiwari S., Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization, Natural Computing, 2005, 341-357.
• [22] Tanabe R., Fukunaga A., Success-History based parameter adaptation for differential evolution, 2013 IEEE Congress on Evolutionary Computation (CEC), 2013, 71-78.
• [23] Tanabe R., Fukunaga A., Improving the search performance of SHADE using linear population size reduction, 2014 IEEE Congress on Evolutionary Computation (CEC), 2014, 1658-1665.
• [24] Tvrdík J., Competitive differential evolution, in: R., Matoušek and P. Ošmera (eds.) MENDEL 2006, 12th International Conference on Soft Computing, University of Technology, Brno, 2006, 7-12.
• [25] Wang Y., Cai Z., Zhang Q., Differential evolution with composite trial vector generation strategies and control parameters, IEEE Transactions on Evolutionary Computation, 15, 2011, 55-66.
• [26] Wetweerapong J., Puphasuk P., An improved differential evolution algorithm with a restart technique to solve systems of nonlinear equations, An International Journal of Optimization and Control: Theories & Applications, 10, 2020, 118-136.
• [27] 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.
• [28] Wu G., Shen X., Li H., Chen H., Lin A., Suganthan P. N., Ensemble of differential evolution variants, Information Sciences, 423, 2018, 172-186.
• [29] Xiao J., Li L. P., A hybrid ant colony optimization for continuous domains, Expert Systems with Applications, 38, 2011, 11072-11077.
• [30] Zhang J., Sanderson A. C., JADE: adaptive differential evolution with optional external archive, IEEE Congress on Evolutionary Computation, 13, 2009, 945-958.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).