Volume 18, Issue 3 (12-2021)
JSDP 2021, 18(3): 127-146 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
moradi M, nejatian S, parvin H, bagherifard K, rezaei V. Clustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic Optimization. JSDP 2021; 18 (3) :127-146
URL: http://jsdp.rcisp.ac.ir/article-1-1025-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1025-en.html
Department of Electrical Engineering, Yasooj Branch, Islamic Azad University
Abstract: (1589 Views)
In the real world, we face some complex and important problems that should be optimized, most of the real-world problems are dynamic. Solving dynamic optimization problems are very difficult due to possible changes in the location of the optimal solution. In dynamic environments, we are faced challenges when the environment changes. To respond to these changes in the environment, any change can be considered as the input of a new optimization problem that should be solved from the beginning, which is not suitable because it is time consuming. One technique for improving optimization and learning in dynamic environments is by using information from the past. By using solutions from previous environments, it is often easier to find promising solutions in a new environment. A common way to maintain and exploit information from the past is the use of memory, where solutions are stored periodically and can be retrieved and refined at the time that the environment changes. Memory can help search respond quickly and efficiently to change in a dynamic problem. Given that a memory has a finite size, if one wishes to store new information in the memory, one of the existing entries must be discarded. The mechanism used to decide whether the candidate entry should be included in the memory or not, and if so, which of the old entries should be replaced it, is called the replacement strategy. This paper explores ways to improve memory for optimization and learning in dynamic environments. In this paper, a memory with clustering and new replacement strategy for storing and restoring memory solutions has been used to enhance memory performance. The evolutionary algorithms that have been presented so far have the problem of rebuilding populations when multiple populations converge to an optimum. For this reason, we proposed algorithm with exclution mechanism that have the ability to explore the environment (Exploration) and extraction (Explitation). Thus, an optimization algorithm is required to solve the problems in dynamic environments well. In this paper, a novel collective optimization algorithm, namely the Clustering and Memory-based Parent-Child Swarm Algorithm (CMPCS), is presented. This method relies on both individual and group behavior. The proposed CMPCS method has been tested on the moving peaks benchmark (MPB). The MPB is a good Benchmark to evaluate the efficiency of the optimization algorithms in dynamic environments. The experimental results on the MPB reveal the appropriate efficiency of the proposed CMPCS method compared to the other state-of-the-art methods in solving the dynamic optimization problems.
Type of Study: Research |
Subject:
Paper
Received: 2019/05/28 | Accepted: 2020/01/22 | Published: 2022/01/20 | ePublished: 2022/01/20
Received: 2019/05/28 | Accepted: 2020/01/22 | Published: 2022/01/20 | ePublished: 2022/01/20
References
1. [1] S. Saremi, S. Mirjalili and A. Lewis, "Biogeography-based optimisation with chaos," Neural Computing and Applications, pp. 1077-1097, 2014. [DOI:10.1007/s00521-014-1597-x]
2. [2] S. Mirjalili, S. Mirjalili and A. Lewis, "Grey Wolf Optimizer," Advances in Engineering Software, pp. 69: 46-61, 2014. [DOI:10.1016/j.advengsoft.2013.12.007]
3. [3] D. Yazdani, B. Nasiri, A. Sepas-Moghaddam and M. Meybodi, "a novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization," Applied Soft Computing, 2013. [DOI:10.1016/j.asoc.2012.12.020]
4. [4] R. Lung and D. Dumitrescu, "A Collaborative Model for Tracking Optima in Dynamic Environments," in In: IEEE Congress on Evolutionary Computation, 2007. [DOI:10.1109/CEC.2007.4424520]
5. [5] F. Ozsoydan and A. Baykasoglu, "A multi-population firefly algorithm for dynamic optimization problems," in Evolving and Adaptive Intelligent Systems (EAIS), 2015 IEEE International Conference, 2015. [DOI:10.1109/EAIS.2015.7368777]
6. [6] X. Hu and R. Eberhart, "Adaptive particle swarm optimisation: detection and response to dynamic systems," In Congress on Evolutionary Computation, pp. 1666-1670, 2002.
7. [7] S. Sadeghi, H. Parvin and F. Rad, "Particle Swarm Optimization for Dynamic Environments," in Springer International Publishing, 14th Mexican International Conference on Artificial intelligence, MICAI, 2015.
8. [8] S. Yang and C. Li, "A clustering particle swarm optimizer for dynamic optimization," in Proc. Congr. Evol. Com, pp. 439-446, 2009.
9. [9] A. Hashemi and M. Meybodi, "Cellular PSO: A PSO for Dynamic Environments," Advances in Computation and Intelligence, pp. 422-433, 2009. [DOI:10.1007/978-3-642-04843-2_45]
10. [10] T. Blackwell, J. Branke and X. Li, "Particle swarms for dynamic optimization problems," Swarm Intelligence. Springer Berlin Heidelberg, pp. 193-217, 2008. [DOI:10.1007/978-3-540-74089-6_6]
11. [11] T. Blackwell and J. Branke, "MultiswarmT Exclusion, and Anti-Convergence in Dynamic Environment," 2006. [DOI:10.1109/TEVC.2005.857074]
12. [12] M. Kamosi, A. Hashemi and M. Meybodi, "A New Particle Swarm Optimization Algorithm for Dynamic Environments," SEMCCO, pp. 129-138, 2010. [DOI:10.1007/978-3-642-17563-3_16]
13. [13] W. Su, H. Chen, F. Liu, S. Jing and W. Li, "A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problem," Saudi Journal of Biological Sciences, pp. 695-702, 2017. [DOI:10.1016/j.sjbs.2017.01.044] [PMID] [PMCID]
14. [14] S. Nseef, S. Abdullah, A. Turky and G. Kendall, "An adaptive multi-population artificial bee colony algorithm for dynamic optimization problems," Knowledge-based Systems Center for Artificial Intelligence and Technology (CAIT), pp. 14-23, 2015. [DOI:10.1016/j.knosys.2016.04.005]
15. [15] D. Yazdani, B. Nasiri and AND..., ""mNAFSA: (2014) A novel approach for optimization in dynamic Environments with global Changes," Swarm and Evolutionary Computation, 2014. [DOI:10.1016/j.swevo.2014.05.002]
16. [16] Y. Bravo, G. Luque and E. Alba, "Global memory schemes for dynamic optimization," Springer Science,Business Media Dordrecht, 2015. [DOI:10.1007/s11047-015-9497-2]
17. [17] S. Biswas, S. Kundu, S. Das and A. Vasilakos, "Information sharing in bee colony for etecting multiple niches in non-stationary environments," 2013. [DOI:10.1145/2464576.2464588]
18. [18] S. Yang, "Associative memory scheme for genetic algorithms in dynamic environments," In Applications of Evolutionary Computing: EvoWorkshops, pp. 788-799, 2006. [DOI:10.1007/11732242_76]
19. [19] S. Yang and C. Li, "A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments," in IEEE Transactions on Evolutionary Computation, 2010. [DOI:10.1109/TEVC.2010.2046667]
20. [20] D. Yazdani, B. Nasiri, A. Sepas-Moghaddam and M. Meybodi, "novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization," Applied Soft Computing, 2013. [DOI:10.1016/j.asoc.2012.12.020]
21. [21] J. Kordestani, A. Rezvanian and M. Meybodi, ""CDEPSO: a bi-population hybrid approach for dynamic optimization problems," Applied intelligence, pp. vol. 40, pp. 682-694, 2014. [DOI:10.1007/s10489-013-0483-z]
22. [22] M. Kamosi, A. Hashemi and M. Meybodi, "A Hibernating Multi-Swarm Optimization Algorithm for Dynamic Environments," in Proceedings of World Congress on Nature and Biologically Inspired Computing, NaBIC, Kitakyush, pp. 370-376, 2010. [DOI:10.1109/NABIC.2010.5716372]
23. [23] X. Chen, D. Zhang and X. Zeng, "A Stable Matching-Based Selection and Memory Enhanced MOEDA/D for Evolutionary Dynamic Multiobjective Optimization," in Tools with Artificial intelligence (ICTAI),IEEE 27th International Conference, 2015. [DOI:10.1109/ICTAI.2015.77]
24. [24] N. Baktash and M. Meybodi, "A New Hybrid Model of PSO and ABC Algorithms for Optimization in Dynamic Environment," Int'l Journal of Computing Theory Engineering, pp. vol. 4, pp. 362-364, 2012. [DOI:10.7763/IJCTE.2012.V4.484]
25. [25] M. mojarad, H. parvin, S. nejatiyan and K. A. Bagheri , "Combining a Ensemble Clustering Method and a New Similarity Criterion for Modeling the Hereditary Behavior of Diseases," JSDP, vol. 18, no. 2, pp. 97-114, 2021.
26. [26] F. najafi, H. parvin, K. mirzaei and S. nejatiyan, "A new ensemble clustering method based on fuzzy cmeans clustering while maintaining diversity in ensemble," JSDP, vol. 17, no. 4, pp. 103-122, 2021. [DOI:10.29252/jsdp.17.4.103]
27. [27] A. Prajapati and J. Chhabra, "Harmony search based remodularization for object-oriented software systems," Computer Languages, Systems & Structures, 2017.
28. [28] X. Peng, K. Liu and Y. Jin, "A dynamic optimization approach to the design of cooperative co-evolutionary algorithms. Knowl," Based Syst, 2016. [DOI:10.1016/j.knosys.2016.07.001]
29. [29] D. Wang, F. Liu and Y. Jin, "A multi-objective evolutionary algorithm guided by directed search for dynamic scheduling," Computers & OR, 2017. [DOI:10.1016/j.cor.2016.04.024]
30. [30] W. Luo, J. Sun, C. Bu and H. Liang, "Species-based Particle Swarm Optimizer enhanced by memory for dynamic optimization," Appl. Soft Comput, 2016. [DOI:10.1016/j.asoc.2016.05.032]
31. [31] B. Yildiz, "A comparative investigation of eight recent population-based optimisation algorithms for mechanical and structural design problems," International Journal of Vehicle Design, pp. 73,1-3,208-218, 2017. [DOI:10.1504/IJVD.2017.10003412]
32. [32] B. Yildiz and H. Lekesiz, "Fatigue-based structural optimisation of vehicle components," International Journal of Vehicle Design, pp. 73, 1-3, 54-62, 2017. [DOI:10.1504/IJVD.2017.082579]
33. [33] D. Simon, EVOLUTIONARY OPTIMIZATION ALGORITHMS, 2013.
34. [34] J. Branke, "Evolutionary Optimization in Dynamic Environments," Kluwer, 2002. [DOI:10.1007/978-1-4615-0911-0]
35. [35] A. Simoes, IMPROVING MEMORY BASED EVOLUTIONARY ALGORITHM FOR DYNAMIC ENVIRONMENTS, Ph.D. Thesis, Comberia University, March., 2010.
36. [36] R. SARKER, M. MOHAMMADIAN and X. YAO, EVOLUTIONARY OPTIMIZATION, 2003. [DOI:10.1007/b101816]
37. [37] T. Blackwell, "Particle swarms and population diversity II: Experiments," GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, p. 14-18, 2003.
38. [38] Y. Jin and J. Branke, "Evolutionary optimization in uncertain environments-a survey," in IEEE Transactions on Evolutionary Computation, 2005. [DOI:10.1109/TEVC.2005.846356]
39. [39] H. Cobb, "An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments," Technical Report AIC-90-001, Naval Research Laboratory, Washington, 1990. [DOI:10.21236/ADA229159]
40. [40] F. Vavak, T. Fogarty and K. Jukes, "A genetic algorithm with variable range of local search for tracking changing environments," In Parallel Problem Solving from Nature, pp. 376-385, 1996. [DOI:10.1007/3-540-61723-X_1002]
41. [41] f. Vavak and k. Jukes, "Performance of a genetic algorithm with variable local search range relative to frequency for the environmental changes," in In International Conference on Genetic Programming, 1998.
42. [42] J. Grefenstette, "Genetic algorithms for changing environments," In Parallel Problem Solving from Nature, pp. 137-144, 1992.
43. [43] J. Grefenstette and L. Connie, "An approach to anytime learning," in In International Conference on Machine Learning, 1992. [DOI:10.1016/B978-1-55860-247-2.50029-2]
44. [44] N. Mori, H. Kita and Y. Nishikawa, "Adaptation to a changing environment by means of the thermodynamical genetic algorithm," In Parallel Problem Solving from Nature, pp. 513-522, 1996. [DOI:10.1007/3-540-61723-X_1015]
45. [45] W. Cedeno and R. Vemuri, "On the use of niching for dynamic landscapes," 1997.
46. [46] S. Yang, Genetic algorithms with elitism-based immigrants for changing optimization problems, In Applications of Evolutionary Computing: EvoWorkshops, 2007, pp. 627-636. [DOI:10.1007/978-3-540-71805-5_69]
47. [47] C. Ryan, "Diploidy without dominance," In Nordic Workshop on Genetic Algorithms, pp. 45-52, 1997.
48. [48] S. Yang, "Explicit memory schemes for evolutionary algorithms in dynamic environments," In Evolutionary Computation in Dynamic and Uncertain Environments. Springer, 2007. [DOI:10.1007/978-3-540-49774-5_1]
49. [49] S. Yang., "Non-stationary problems optimization using the primal-dual genetic algorithm," In Congress on Evolutionary Computation, pp. 2246-2253, 2003.
50. [50] A. Younes, "Adapting Evolutionary Approaches For Optimization in Dynamic Environments," A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Doctor of Philosophy in Systems Design Engineering, Waterloo, Ontario, Canada, 2006.
51. [51] R. Morrison, Performance Measurement in Dynamic Environments, 2015.
52. [52] M. Zarei, H. Parvin and M. Dadvar, "A New Method to Optimize Dynamic Environments with Global Changes Using the Chickens-Hen' Algorithm," Springer International Publishing AG, pp. 331-340, 2017. [DOI:10.1007/978-3-319-62428-0_26]
53. [53] A. Simoes, "Improving Memory Based Evolutionary Algorithm for Dynamic Environments, Ph.D. Thesis, Comberia University, March., 2010.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
