Abstract
Cuckoo search (CS) is a one of the most efficient evolutionary for global optimization and widely applied to solve diverse problems in the real world. Despite its efficiency and wide use, CS suffers from premature convergence and poor balance between exploitation and exploration. To cope with these issues, a new cuckoo search algorithm extension based on self-adaptive knowledge learning (I-PKL-CS) is proposed. In this study, learning model with individual history knowledge and population knowledge is introduced into the CS algorithm. Individuals constantly adjust their position by using historical knowledge and communicate with each other by using their own knowledge in the optimization process. In order to reduce complexity of the I-PKL-CS algorithm, the optimal learning model is selected to exploit the potential of individual knowledge learning and population knowledge learning by adopting threshold statistics learning strategy, which provides a good trade-off between the exploration and exploitation. The accuracy and performance of the proposed approach are evaluated by eighteen classic benchmark functions and CEC 2013 test suite. Statistical comparisons of the experimental results showed that the proposed I-PKL-CS algorithm made an appropriate trade-off between exploration and exploitation. Comparing the proposed I-PKL-CS with various CS algorithms, variants of differential evolution, and improved particle swarm optimization algorithms, the results demonstrate that I-PKL-CS is a competitive new type of algorithm.
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References
Wang GG, Tan Y (2017) Improving metaheuristic algorithms with information feedback models. IEEE Trans Cybern. https://doi.org/10.1109/tcyb.2017.2780274
Wang GG, Cai X, Cui Z, Min G, Chen J (2017) High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm. IEEE Trans Emerg Top Comput. https://doi.org/10.1109/tetc.2017.2703784
Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2015.12.022
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris F, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw. https://doi.org/10.1016/j.advengsoft.2017.07.002
Mirjalili S, Jangir P, Saremi S (2016) Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl Intell. https://doi.org/10.1007/s10489-016-0825-8
Mirjalili S, Jangir P, Mirjalili SZ, Saremi S, Trivedi IN (2017) Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2017.07.018
Mirjalili S, Lewis A (2015) Novel performance metrics for robust multi-objective optimization algorithms. Swarm Evol Comput 21:1–23. https://doi.org/10.1016/j.swevo.2014.10.005
Gong DW, Sun J, Miao Z (2018) A set-based genetic algorithm for interval many-objective optimization problems. IEEE Trans Evol Comput 22(1):47–60
Gong DW, Liu YP, Yen Gary G (2018) A meta-objective approach for many-objective evolutionary optimization. Evol Comput. https://doi.org/10.1162/evco_a_00243
Gong DW, Sun J, Ji XF (2013) Evolutionary algorithms with preference polyhedron for interval multi-objective optimization problems. Info Sci 233(1):141–161
Rong M, Gong DW, Zhang Y, Jin YC, Pedrycz W (2018) Multi-directional prediction approach for dynamic multi-objective optimization problems. IEEE Trans Cybern 99:1–13. https://doi.org/10.1109/TCYB.2018.2842158
Liu YP, Gong DW, Sun J, Jin YC (2017) A many-objective evolutionary algorithm using a one-by-one selection strategy. IEEE Trans Cybern 47(9):2689–2702
Liu YP, Gong DW, Sun XY, Zhang Y (2017) Many-objective evolutionary optimization based on reference points. Appl Soft Comput 50:344–355
Mirjalili S, Lewis A, Mostaghim S (2015) Confidence measure: a novel metric for robust meta-heuristic optimisation algorithms. Inf Sci 317:114–142. https://doi.org/10.1016/j.ins.2015.04.010
Mirjalili S, Lewis A, Dong JS (2018) Confidence-based robust optimisation using multi-objective meta-heuristics. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2018.04.002
Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073. https://doi.org/10.1007/s00521-015-1920-1
Wang GG, Hu CHE, Mirjalili S (2016) Three-dimensional path planning for UCAV using an improved bat algorithm. Aerosp Sci Technol 49:231–238
Deb K (1999) An introduction to genetic algorithms. Sadhan 24(4–5):293–315
Mirjalili S, Wang GG, Coelho LdS (2014) Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput Appl 25(6):1423–1435. https://doi.org/10.1007/s00521-014-1629-6
Rahnamayan S, Tizhoosh HR, Salama MMA (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12(1):64–79
Jia GB, Wang Y, Cai ZX, Jin YC (2013) An improved (l + k)-constrained differential evolution for constrained optimization. Inf Sci 222:302–322
Wang GG, Guo L, Gandomi AH, Hao GS, Wang H (2014) Chaotic krill herd algorithm. Inf Sci 274:17–34
Wang GG, Gandomi AH, Alavi AH (2014) Stud krill herd algorithm. Neurocomputing 128(5):363–370
Wang H, Yi JH (2017) An improved optimization method based on krill herd and artificial bee colony with information exchange. Memet Comput. https://doi.org/10.1007/s12293-017-0241-6
Rizk-Allah RM, El-Sehiemy RA, Wang GG (2018) A novel parallel hurricane optimization algorithm for secure emission/economic load dispatch solution. Appl Soft Compt 63:206–222. https://doi.org/10.1016/j.asoc.2017.12.002
Wang GG, Gandomi AH, Alavi AH (2014) An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Model 38(9–10):2246–2454
Wang GG, Deb S, Gandomi AH, Alavi AH (2016) Opposition-based krill herd algorithm with Cauchy mutation and position clamping. Neurocomputing 177:147–157. https://doi.org/10.1016/j.neucom.2015.11.018
Zhang Z, Feng Z (2012) Two-stage updating pheromone for invariant ant colony optimization algorithm. Expert Syst Appl 39(1):706–712
Wang GG, Guo L, Duan H, Wang H, Liu L (2013) Hybridizing harmony search with biogeography based optimization for global numerical optimization. J Comput Theor Nanos 10(10):2318–2328
Wang GG, Gandomi AH, Zhao X, Chu HE (2016) Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Comput 20(1):273–285. https://doi.org/10.1007/s00500-014-1502-7
Yildiz AR (2013) A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing. Appl Soft Comput 13(5):2906–2912
Feng Y, Wang GG (2018) Binary moth search algorithm for discounted 0–1 knapsack problem. IEEE Access 6:10708–10719. https://doi.org/10.1109/ACCESS.2018.2809445
Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006
Wang GG, Deb S, Cui Z (2015) Monarch butterfly optimization. Neural Comput Appl. https://doi.org/10.1007/s00521-015-1923-y
Wang GG, Deb S, Zhao XC, Cui ZH (2017) A new monarch butterfly optimization with an improved crossover operator. Oper Res. https://doi.org/10.1007/s12351-016-0251-z
Wang GG, Deb S, Gao XZ, Coelho LdS (2016) A new metaheuristic optimization algorithm motivated by elephant herding behavior. Int J Bio-Inspir Com 8(6):394–409. https://doi.org/10.1504/IJBIC.2016.10002274
Wang Y, Gao S, Yu Y, Xu Z (2017) The discovery of population interaction with a power law distribution in brain storm optimization. Memet Comput 5939:1–23. https://doi.org/10.1007/s12293-017-0248-z
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Wang GG, Deb S, Gandomi AH, Zhang ZJ, Alavi AH (2016) Chaotic cuckoo search. Soft Comput 20(1):3349–3362
Yang XS, Deb S (2010) Cuckoo search via Lévy flights. World Cong Nat Biol Inspir Comput 71(1):210–214
Nguyen TT, Vo DN (2015) Modified cuckoo search algorithm for short-term hydrothermal scheduling. Electr Power Energy Syst 65:271–281
Yang XS, Deb S (2010) Engineering optimization by cuckoo search. J Mathl Model Numer Optim 1(4):330–343
Vallan E, Tavakoli S, Mohanna S, Haghi A (2013) Improved cuckoo search for reliability optimization problems. Comput Ind Eng 64(1):459–568
Li XT, Wang JN, Yin MH (2014) Enhancing the performance of cuckoo search algorithm using orthogonal learning method. Neural Comput Appl 24(6):1233–1247
Ouaarab A, Ahiod B, Yang XS (2014) Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput Appl 24(7–8):1659–1669
Meijun D, Hongyu Y, Hong L, Junyi Ch (2018) A differential evolution algorithm with dual preferred learning mutation. Appl Intel. https://doi.org/10.1007/s10489-018-1267-2
Hussein S, Chee P, Junita MS (2016) A new reinforcement learning-based memetic particle swarm optimizer. Appl Soft Comput 43:276–297
Wang YH, Lin THS, Lin CHJ (2013) Backward Q-learning: the combination of Sarsa algorithm and Q-learning. Eng Appl Artif Intel 26(9):2184–2193
Alex A, Eva Ch, Haralambos S (2016) Cooperative learning for radial basis function networks using particle swarm optimization. Appl Soft Comput 49:485–497
Yingjie Z, Zhonghan G (2014) Hybrid differential evolution gravitation search algorithm based on threshold statistical learning. J Comput Res Dev 51(10):2187–2194
Wang F, He XS, Wang Y (2011) The cuckoo search algorithm based on Gaussian disturbance. J Xian Polytech Univ 4:566–569
Wang F, He XS, Luo LG, Wang Y (2011) Hybrid optimization algorithm of PSO and cuckoo search. In: International joint conference on artificial intelligence, pp 1172–1175
Brest J, Greiner S, Boskovic B, Mernik M (2007) Self-Adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. Trans Evol Comput 13(2):398–417
Jingqiao Z, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958
Kennedy J (2003) Bare bones particle swarms. In: Proceedings of the 2003 IEEE swarm intelligence symposium, pp 80–87
Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In: Proceedings of the swarm intelligence symposium, pp 174–181
Liang J, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295
Chen X, Tianfield H, Mei CL, Du WL, Liu GH (2017) Biogeography-based learning particle swarm optimization. Soft Comput 21(24):7519–7541
Wang F, Zhang H, Li KS, Lin ZY, Yang J, Shen XL (2018) A hybrid particle swarm optimization algorithm using adaptive learning strategy. Info Sci 436–437:162–177
Huang L, Ding S, Sh Yu, Wang J, Lu K (2016) Chaos-enhanced Cuckoo search optimization algorithms for global optimization. Appl Soft Compt 40(5–6):3860–3875
Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of the IEEE international conference on evolutionary computation, Anchorage, AK, USA, pp 69–73
Zhan ZH, Zhang J, Li Y, Shi YH (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847
Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(2):17–35
Deb K, Goyal M (1996) A combined genetic adaptive search (GeneAS) for engineering design. Comput Sci Inf 26(4):30–45
Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13(5):2592–2612
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
Mezura-Montes E, Coello CAC (2008) An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 37:443–473
Li L, Huang Z, Liu F, Wu Q (2007) A heuristic particle swarm optimizer for optimization of pin connected structures. Comput Struct 85(7–8):340–349
Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput Int J Comput Aid Eng 27(1):155–182
Mirjalili S, Lewis A (2014) Adaptive gbest-guided gravitational search algorithm. Neural Comput Appl 25(7–8):1569–1584
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2–4):311–338
Mirjalili S (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Zhang Y, Gong DW, Sun JY, Qu BY (2018) A decomposition-based archiving approach for multi-objective evolutionary optimization. Info Sci 430–431:397–413
Zhang Y, Song Xf, Gong Dw (2017) A return-cost-based binary firefly algorithm for feature selection. Info Sci 418–419:561–574
Zhang Y, Gong DW, Cheng J (2017) Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Trans Comput Biol Bioinform 14(1):64–75
Zhang Y, Gong DW, Hu Y (2015) Feature selection algorithm based on bare bones particle swarm optimization. Neurocomputing 148:150–157
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This work was supported by the scientific research project of Hubei Provincial Department of Education (No. B2017314) and National Natural Science Foundation of China (No. 61672391).
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Li, J., Li, Yx., Tian, Ss. et al. An improved cuckoo search algorithm with self-adaptive knowledge learning. Neural Comput & Applic 32, 11967–11997 (2020). https://doi.org/10.1007/s00521-019-04178-w
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DOI: https://doi.org/10.1007/s00521-019-04178-w