[1] R. Xiao and Z. Tao, “Solution to holes machining path planning by evolutionary methods,” Computer Integrated Manufacturing Systems, vol. 11, no. 5, p. 682, 2005.
[2] Z. Zhou and T. Ding, “Research on holes machining path planning optimization with tsp and ga,” Modular Machine Tool & Automatic Manufacturing Tech- nique, vol. 7, p. 008, 2007.
[3] J. B. Tenenbaum, C. Kemp, T. L. Griffiths, and N. D. Goodman, “How to grow a mind: Statistics, structure, and abstraction,” science, vol. 331, no. 6022, pp. 1279–1285, 2011.
[4] E. Bonabeau, M. Dorigo, and G. Theraulaz, “Inspiration for optimization from social insect behaviour,” Nature, vol. 406, no. 6791, p. 39, 2000.
[5] X. Hu and R. C. Eberhart, “Adaptive particle swarm optimization: detection and response to dynamic systems,” in Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No. 02TH8600), vol. 2. IEEE, 2002, pp. 1666–1670.
[6] P. B. Mullen, C. K. Monson, K. D. Seppi, and S. C. Warnick, “Particle swarm optimization in dynamic pricing,” in 2006 IEEE International Conference on Evolutionary Computation. IEEE, 2006, pp. 1232–1239.
[7] M. Mavrovouniotis and S. Yang, “Ant colony optimization with immigrants schemes in dynamic environments,” in International Conference on Parallel Problem Solving from Nature. Springer, 2010, pp. 371–380.
[8] W. Zhong, J. Xing, Y. Liang, and F. Qian, “Dynamic optimization with an improved θ-pso based on memory recall,” in 2010 8th World Congress on In- telligent Control and Automation. IEEE, 2010, pp. 3225–3229.
[9] T. Blackwell and J. Branke, “Multiswarms, exclusion, and anti-convergence in dynamic environments,” IEEE transactions on evolutionary computation, vol. 10, no. 4, pp. 459–472, 2006.
[10] T. Smith, P. Husbands, and M. O’Shea, “Fitness landscapes and evolvability,” Evolutionary computation, vol. 10, no. 1, pp. 1–34, 2002.
[11] P. A. Bosman and H. La Poutre, “Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case,” in Proceedings of the 9th annual conference on Genetic and evolutionary computation. ACM, 2007, pp. 1165–1172.
[12] C. W. Lee, “Dynamic optimization of the grinding process in batch production,” Journal of Manufacturing Science and Engineering, vol. 131, no. 2, p. 021006, 2009.
[13] H. Cheng and S. Yang, “Genetic algorithms with immigrants schemes for dy- namic multicast problems in mobile ad hoc networks,” Engineering Applications of Artificial Intelligence, vol. 23, no. 5, pp. 806–819, 2010.
[14] J. H. Holland et al., Adaptation in natural and artificial systems: an intro- ductory analysis with applications to biology, control, and artificial intelligence. MIT press, 1992.
[15] Y. Yu and Z.-H. Zhou, “A new approach to estimating the expected first hitting time of evolutionary algorithms,” Artificial Intelligence, vol. 172, no. 15, pp. 1809–1832, 2008.
[16] D. E. Goldberg and K. Deb, “A comparative analysis of selection schemes used in genetic algorithms,” in Foundations of genetic algorithms. Elsevier, 1991, vol. 1, pp. 69–93.
[17] A. Pru¨gel-Bennett and J. L. Shapiro, “Analysis of genetic algorithms using statistical mechanics,” Physical Review Letters, vol. 72, no. 9, p. 1305, 1994.
[18] D. H. Wolpert, W. G. Macready et al., “No free lunch theorems for optimiza- tion,” IEEE transactions on evolutionary computation, vol. 1, no. 1, pp. 67–82, 1997.
[19] M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and conver- gence in a multidimensional complex space,” IEEE transactions on Evolution- ary Computation, vol. 6, no. 1, pp. 58–73, 2002.
[20] E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and V. G. Da Fonseca, “Per- formance assessment of multiobjective optimizers: An analysis and review,” IEEE Transactions on evolutionary computation, vol. 7, no. 2, pp. 117–132, 2003.
[21] M. Cˇrepinˇsek, S.-H. Liu, and M. Mernik, “Exploration and exploitation in evo- lutionary algorithms: A survey,” ACM Computing Surveys (CSUR), vol. 45, no. 3, p. 35, 2013.
[22] J. Liu, H. A. Abbass, D. G. Green, and W. Zhong, “Motif difficulty (md): a predictive measure of problem difficulty for evolutionary algorithms using network motifs,” Evolutionary computation, vol. 20, no. 3, pp. 321–347, 2012.
[23] Y. Xu, L. Wang, S.-y. Wang, and M. Liu, “An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time,” Neurocomputing, vol. 148, pp. 260–268, 2015.
[24] G. Yang, S. Wu, Q. Jin, and J. Xu, “A hybrid approach based on stochastic competitive hopfield neural network and efficient genetic algorithm for frequen- cy assignment problem,” Applied Soft Computing, vol. 39, pp. 104–116, 2016.
[25] A. R. Yildiz, “A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing,” Applied Soft Computing, vol. 13, no. 5, pp. 2906– 2912, 2013.
[26] S. Gao, Y. Wang, J. Wang, and J. Cheng, “Understanding differential evolu- tion: A poisson law derived from population interaction network,” Journal of Computational Science, vol. 21, pp. 140–149, 2017.
[27] Z. Wu, L. Kang, and X. Zou, “A parallel global-local mixed evolutionary al- gorithm for multimodal function optimization,” in Fifth International Confer- ence on Algorithms and Architectures for Parallel Processing, 2002. Proceedings. IEEE, 2002, pp. 247–250.
[28] S. Gao, S. Song, J. Cheng, Y. Todo, and M. Zhou, “Incorporation of solvent ef- fect into multi-objective evolutionary algorithm for improved protein structure prediction,” IEEE/ACM transactions on computational biology and bioinfor- matics, vol. 15, no. 4, pp. 1365–1378, 2018.
[29] V. Hatzikos, J. H¨at¨onen, and D. Owens, “Genetic algorithms in norm-optimal linear and non-linear iterative learning control,” International Journal of con- trol, vol. 77, no. 2, pp. 188–197, 2004.
[30] S. Gao, M. Zhou, Y. Wang, J. Cheng, H. Yachi, and J. Wang, “Dendritic neu- ron model with effective learning algorithms for classification, approximation, and prediction,” IEEE transactions on neural networks and learning systems, vol. 30, no. 2, pp. 601–614, 2018.
[31] J. Rayno, M. F. Iskander, and M. H. Kobayashi, “Hybrid genetic programming with accelerating genetic algorithm optimizer for 3-d metamaterial design,” IEEE Antennas and Wireless Propagation Letters, vol. 15, pp. 1743–1746, 2016.
[32] S. Manaseer, W. Manasir, M. Alshraideh, N. A. Hashish, and O. Adwan, “Au- tomatic test data generation for java card applications using genetic algorithm,” Journal of Software Engineering and Applications, vol. 8, no. 12, p. 603, 2015.
[33] A. Pachauri and G. Srivastava, “Automated test data generation for branch testing using genetic algorithm: An improved approach using branch ordering, memory and elitism,” Journal of Systems and Software, vol. 86, no. 5, pp. 1191–1208, 2013.
[34] M. H. Moradi and M. Abedini, “A combination of genetic algorithm and particle swarm optimization for optimal dg location and sizing in distribution systems,” International Journal of Electrical Power & Energy Systems, vol. 34, no. 1, pp. 66–74, 2012.
[35] J. F. Gon¸calves and M. G. Resende, “A parallel multi-population biased random-key genetic algorithm for a container loading problem,” Computers & Operations Research, vol. 39, no. 2, pp. 179–190, 2012.
[36] T. Vidal, T. G. Crainic, M. Gendreau, and C. Prins, “A hybrid genetic algo- rithm with adaptive diversity management for a large class of vehicle routing problems with time-windows,” Computers & Operations Research, vol. 40, no. 1, pp. 475–489, 2013.
[37] S. Mallick, S. Ghoshal, P. Acharjee, and S. Thakur, “Optimal static state esti- mation using improved particle swarm optimization and gravitational search al- gorithm,” International Journal of Electrical Power & Energy Systems, vol. 52, pp. 254–265, 2013.
[38] W.-f. Gao and S.-y. Liu, “A modified artificial bee colony algorithm,” Computers & Operations Research, vol. 39, no. 3, pp. 687–697, 2012.
[39] S. Gao, Y. Wang, J. Cheng, Y. Inazumi, and Z. Tang, “Ant colony optimiza- tion with clustering for solving the dynamic location routing problem,” Applied Mathematics and Computation, vol. 285, pp. 149–173, 2016.
[40] S. A. Hofmeyr and S. Forrest, “Architecture for an artificial immune system,” Evolutionary Computation, vol. 8, no. 4, pp. 443–473, 2000.
[41] S. Forrest, A. S. Perelson, L. Allen, and R. Cherukuri, “Self-nonself discrimina- tion in a computer,” in Research in Security and Privacy, 1994. Proceedings., 1994 IEEE Computer Society Symposium on. IEEE, 1994, pp. 202–212.
[42] J. Timmis, “Artificial immune systems: A novel data analysis technique inspired by the immune network theory,” Ph.D. dissertation, Department of Computer Science, 2000.
[43] Z. Tang, K. Tashima, and Q. Cao, “A pattern recognition system using clon- al selection-based immune network,” IEICE Transactions on Information and Systems, vol. 84, no. 12, pp. 2615–2622, 2001.
[44] C. Wu and C. Ku, “Stress-enhanced clonal selection algorithm for structural topology optimization,” International Journal for Numerical Methods in Engi- neering, vol. 89, no. 8, pp. 957–974, 2012.
[45] X. Zuo and Y. Fan, “A chaos search immune algorithm with its application to neuro-fuzzy controller design,” Chaos, Solitons & Fractals, vol. 30, no. 1, pp. 94–109, 2006.
[46] L. N. De Castro and F. J. Von Zuben, “Learning and optimization using the clonal selection principle,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 3, pp. 239–251, 2002.
[47] S. F. M. Burnet, The clonal selection theory of acquired immunity. Vanderbilt University Press Nashville, 1959.
[48] S. Gao, H. Chai, B. Chen, and G. Yang, “Hybrid gravitational search and clonal selection algorithm for global optimization,” in International Conference in Swarm Intelligence. Springer, 2013, pp. 1–10.
[49] B. S. Rao and K. Vaisakh, “Multi-objective adaptive clonal selection algorithm for solving environmental/economic dispatch and opf problems with load uncer- tainty,” International Journal of Electrical Power & Energy Systems, vol. 53, pp. 390–408, 2013.
[50] Y. Cai, J. Wang, J. Yin, and Y. Zhou, “Memetic clonal selection algorithm with eda vaccination for unconstrained binary quadratic programming problems,” Expert Systems with Applications, vol. 38, no. 6, pp. 7817–7827, 2011.
[51] L. Hong and Z. Ji, “An adaptive multi-objective clonal selection algorithm,” in Computer Communication Control and Automation (3CA), 2010 International Symposium on, vol. 1. IEEE, 2010, pp. 233–236.
[52] Y. Zhong, L. Zhang, and P. Li, “Multispectral remote sensing image classifi- cation based on simulated annealing clonal selection algorithm,” in Geoscience and Remote Sensing Symposium, 2005. IGARSS’05. Proceedings. 2005 IEEE International, vol. 6. IEEE, 2005, pp. 3745–3748.
[53] H. Dai, Y. Yang, C. Li, J. Shi, S. Gao, and Z. Tang, “Quantum interference crossover-based clonal selection algorithm and its application to traveling sales- man problem,” IEICE Transactions on Information and Systems, vol. 92, no. 1, pp. 78–85, 2009.
[54] S. Gao, H. Dai, G. Yang, and Z. Tang, “A novel clonal selection algorithm and its application to traveling salesman problem,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. 90, no. 10, pp. 2318–2325, 2007.
[55] A. R. Yıldız, “A novel hybrid immune algorithm for global optimization in design and manufacturing,” Robotics and Computer-Integrated Manufacturing, vol. 25, no. 2, pp. 261–270, 2009.
[56] A. R. Yildiz, “A comparative study of population-based optimization algorithms for turning operations,” Information Sciences, vol. 210, pp. 81–88, 2012.
[57] V. Kouskoff and D. Nemazee, “Role of receptor editing and revision in shaping the b and t lymphocyte repertoire,” Life Sciences, vol. 69, no. 10, pp. 1105– 1113, 2001.
[58] R. A. Goldsby, T. J. Kindt, B. A. Osborne, and J. Kuby, Immunology. WH Freeman, 2003.
[59] R. Pelanda and R. M. Torres, “Receptor editing for better or for worse,” Current Opinion in Immunology, vol. 18, no. 2, pp. 184–190, 2006.
[60] L. K. Verkoczy, A. S. M˚artensson, and D. Nemazee, “The scope of receptor edit- ing and its association with autoimmunity,” Current Opinion in Immunology, vol. 16, no. 6, pp. 808–814, 2004.
[61] M. C. Nussenzweig, “Immune receptor editing: revise and select,” Cell, vol. 95, no. 7, pp. 875–878, 1998.
[62] S. Tonegawa, “Somatic generation of antibody diversity,” Nature, vol. 302, no. 5909, pp. 575–581, 1983.
[63] L. N. De Castro and F. J. Von Zuben, “The clonal selection algorithm with engineering applications,” in Proceedings of GECCO, vol. 2000, 2000, pp. 36– 39.
[64] A. S. Perelson, “Immune network theory,” Immunological Reviews, vol. 110, no. 1, pp. 5–36, 1989.
[65] E. L. Lawler, J. K. Lenstra, A. H. G. R. Kan, and D. B. Shmoys, “A guided tour of combinatorial optimization,” Estimation, Simulation, and Control–Wiley- Interscience Series, 1985.
[66] G. Reinelt, “Tspliba traveling salesman problem library,” ORSA journal on computing, vol. 3, no. 4, pp. 376–384, 1991.
[67] K. Maekawa, N. Mori, H. Tamaki, H. Kita, and Y. Nishikawa, “A genetic solution for the traveling salesman problem by means of a thermodynamical se- lection rule,” Transactions of the Society of Instrument and Control Engineers, vol. 33, no. 9, pp. 939–946, 1997.
[68] P.-C. Chang, W.-H. Huang, and C.-J. Ting, “Dynamic diversity control in ge- netic algorithm for mining unsearched solution space in tsp problems,” Expert Systems with Applications, vol. 37, no. 3, pp. 1863–1878, 2010.
[69] R. Xiao, Z. Tao, and T. Chen, “An analytical approach to the similarities between swarm intelligence and artificial neural network,” Transactions of the Institute of Measurement and Control, vol. 34, no. 6, pp. 736–745, 2012.
[70] M. Liu, W. Zeng, and J. Zhao, “A fast bi-objective non-dominated sorting algorithm,” Pattern Recognition and Artificial Intelligence, vol. 24, no. 4, pp. 538–547, 2011.
[71] S. H. H. Madni, M. S. A. Latiff, J. Ali et al., “Multi-objective-oriented cuckoo search optimization-based resource scheduling algorithm for clouds,” Arabian Journal for Science and Engineering, vol. 44, no. 4, pp. 3585–3602, 2019.
[72] Https://www.ibm.com/cloud/learn/cloud-computing.
[73] A. Gawanmeh, S. Parvin, and A. Alwadi, “A genetic algorithmic method for scheduling optimization in cloud computing services,” Arabian Journal for Sci- ence and Engineering, vol. 43, no. 12, pp. 6709–6718, 2018.
[74] S. Parthasarathy and C. J. Venkateswaran, “Scheduling jobs using oppositional- gso algorithm in cloud computing environment,” Wireless Networks, vol. 23, no. 8, pp. 2335–2345, 2017.
[75] A. Choudhary, I. Gupta, V. Singh, and P. K. Jana, “A GSA based hybrid algo- rithm for bi-objective workflow scheduling in cloud computing,” Future Gener- ation Computer Systems, vol. 83, pp. 14–26, 2018.
[76] K. Dubey, M. Kumar, and S. Sharma, “Modified heft algorithm for task schedul- ing in cloud environment,” Procedia Computer Science, vol. 125, pp. 725–732, 2018.
[77] R. Bala and G. Singh, “An improved heft algorithm using multi-criterian re- source factors,” Int. J. Comput. Sci. Inf. Technol.(IJCSIT), vol. 5, no. 6, pp. 6958–6963, 2014.
[78] B. Kanani and B. Maniyar, “Review on max-min task scheduling algorithm for cloud computing,” Journal of Emerging Technologies and Innovative Research, vol. 2, no. 3, pp. 781–784, 2015.
[79] X. Li, Y. Mao, X. Xiao, and Y. Zhuang, “An improved max-min task-scheduling algorithm for elastic cloud,” in 2014 International Symposium on Computer, Consumer and Control. IEEE, 2014, pp. 340–343.
[80] E. De Coninck, T. Verbelen, B. Vankeirsbilck, S. Bohez, P. Simoens, and B. D- hoedt, “Dynamic auto-scaling and scheduling of deadline constrained service workloads on iaas clouds,” Journal of Systems and Software, vol. 118, pp. 101– 114, 2016.
[81] M. Kumar, K. Dubey, and S. Sharma, “Elastic and flexible deadline constraint load balancing algorithm for cloud computing,” Procedia Computer Science, vol. 125, pp. 717–724, 2018.
[82] M. Kumar, S. Sharma, A. Goel, and S. Singh, “A comprehensive survey for scheduling techniques in cloud computing,” Journal of Network and Computer Applications, vol. 143, pp. 1–33, 2019.
[83] A. Arunarani, D. Manjula, and V. Sugumaran, “Task scheduling techniques in cloud computing: A literature survey,” Future Generation Computer Systems, vol. 91, pp. 407–415, 2019.
[84] S. Kaur and A. Verma, “An efficient approach to genetic algorithm for task scheduling in cloud computing environment,” International Journal of Infor- mation Technology and Computer Science (IJITCS), vol. 4, no. 10, pp. 74–79, 2012.
[85] K. Dasgupta, B. Mandal, P. Dutta, J. K. Mandal, and S. Dam, “A genetic algorithm (ga) based load balancing strategy for cloud computing,” Procedia Technology, vol. 10, pp. 340–347, 2013.
[86] B. Keshanchi, A. Souri, and N. J. Navimipour, “An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: for- mal verification, simulation, and statistical testing,” Journal of Systems and Software, vol. 124, pp. 1–21, 2017.
[87] K. KRISHNASAMY, “Task scheduling algorithm based on hybrid particle swar- m optimization in cloud computing environment.” Journal of Theoretical & Applied Information Technology, vol. 55, no. 1, pp. 33–38, 2013.
[88] A. Verma and S. Kaushal, “A hybrid multi-objective particle swarm optimiza- tion for scientific workflow scheduling,” Parallel Computing, vol. 62, pp. 1–19, 2017.
[89] H. Duan, C. Chen, G. Min, and Y. Wu, “Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems,” Future Generation Com- puter Systems, vol. 74, pp. 142–150, 2017.
[90] D. P. Mahato, R. S. Singh, A. K. Tripathi, and A. K. Maurya, “On scheduling transactions in a grid processing system considering load through ant colony optimization,” Applied Soft Computing, vol. 61, pp. 875–891, 2017.
[91] S. K. Addya, A. K. Turuk, B. Sahoo, M. Sarkar, and S. K. Biswash, “Simulated annealing based vm placement strategy to maximize the profit for cloud ser- vice providers,” Engineering science and technology, an international journal, vol. 20, no. 4, pp. 1249–1259, 2017.
[92] L. Tang, Z. Li, P. Ren, J. Pan, Z. Lu, J. Su, and Z. Meng, “Online and offline based load balance algorithm in cloud computing,” Knowledge-Based Systems, vol. 138, pp. 91–104, 2017.
[93] H. Dai, Y. Yang, H. Li, and C. Li, “Bi-direction quantum crossover-based clonal selection algorithm and its applications,” Expert Systems with Applications, vol. 41, no. 16, pp. 7248–7258, 2014.
[94] S. Gao, Q. Cao, Z. Zhang, and Z. Tang, “A chaotic clonal selection algorithm and its application to synthesize multiple-valued logic functions,” IEEJ Trans- actions on Electrical and Electronic Engineering, vol. 5, no. 1, pp. 105–114, 2010.
[95] M. Rafiei, T. Niknam, and M. H. Khooban, “Probabilistic electricity price fore- casting by improved clonal selection algorithm and wavelet preprocessing,” Neu- ral Computing and Applications, vol. 28, no. 12, pp. 3889–3901, 2017.
[96] T. Gong, “Improved immune computation for high-precision face recognition,” Soft Computing, vol. 21, no. 20, pp. 5989–5999, 2017.
[97] Y.-C. Chou and M. Nakajima, “A clonal selection algorithm for energy-efficient mobile agent itinerary planning in wireless sensor networks,” Mobile Networks and Applications, vol. 23, no. 5, pp. 1233–1246, 2018.
[98] X.-H. Liu, M.-Y. Shan, R.-L. Zhang, and L.-H. Zhang, “Green vehicle rout- ing optimization based on carbon emission and multiobjective hybrid quantum immune algorithm,” Mathematical Problems in Engineering, vol. 2018, 2018.
[99] A. Lasisi, R. Ghazali, and H. Chiroma, “Utilizing clonal selection theory in- spired algorithms and k-means clustering for predicting opec carbon dioxide emissions from petroleum consumption,” in International Conference on Soft Computing and Data Mining. Springer, 2016, pp. 101–110.
[100] Y. Zhong, L. Zhang, and P. Li, “Multispectral remote sensing image classifica- tion based on simulated annealing clonal selection algorithm,” in Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS’05., vol. 6. IEEE, 2005, pp. 3745–3748.
[101] G. Chen and J. Liu, “Mobile robot path planning using ant colony algorithm and improved potential field method,” Computational intelligence and neuroscience, vol. 2019, 2019.
[102] B. Akay and D. Karaboga, “A modified artificial bee colony algorithm for real- parameter optimization,” Information sciences, vol. 192, pp. 120–142, 2012.
[103] L. N. De Castro and F. J. Von Zuben, “Learning and optimization using the clonal selection principle,” IEEE transactions on evolutionary computation, vol. 6, no. 3, pp. 239–251, 2002.
[104] W. Zhou and J. Cao, “Cloud computing resource scheduling strategy based on prediction and aco algorithm,” Computer simulation, vol. 29, no. 9, pp. 239–242, 2012.
[105] H. Yuan, C. Li, and M. Du, “Optimal virtual machine resources scheduling based on improved particle swarm optimization in cloud computing.” JSW, vol. 9, no. 3, pp. 705–708, 2014.
[106] X. Wang, H. Gu, and Y. Yue, “The optimization of virtual resource allocation in cloud computing based on rbpso,” Concurrency and Computation: Practice and Experience, p. e5113, 2018.
[107] Y.-K. Lin and C. S. Chong, “Fast ga-based project scheduling for computing resources allocation in a cloud manufacturing system,” Journal of Intelligent Manufacturing, vol. 28, no. 5, pp. 1189–1201, 2017.
[108] T. Jena and J. Mohanty, “Ga-based customer-conscious resource allocation and task scheduling in multi-cloud computing,” Arabian Journal for Science and Engineering, vol. 43, no. 8, pp. 4115–4130, 2018.
[109] A. S. Sofia and P. GaneshKumar, “Multi-objective task scheduling to minimize energy consumption and makespan of cloud computing using nsga-ii,” Journal of Network and Systems Management, vol. 26, no. 2, pp. 463–485, 2018.
[110] Y. Dai, Y. Lou, and X. Lu, “A task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm with multi-qos constraints in cloud computing,” in 2015 7th International Conference on Intelligent Human- Machine Systems and Cybernetics, vol. 2. IEEE, 2015, pp. 428–431.
[111] B. Hayes, “Cloud computing,” Communications of the ACM, vol. 51, no. 7, pp. 9–11, 2008.
[112] R. N. Calheiros, R. Ranjan, C. A. De Rose, and R. Buyya, “Cloudsim: A novel framework for modeling and simulation of cloud computing infrastructures and services,” arXiv preprint arXiv:0903.2525, 2009.
[113] M. M. Drugan, “Reinforcement learning versus evolutionary computation: A survey on hybrid algorithms,” Swarm and Evolutionary Computation, vol. 44, pp. 228–246, 2019.
[114] J. Kennedy, “Particle swarm optimization,” in Encyclopedia of Machine Learn- ing. Springer, 2011, pp. 760–766.
[115] M. Dorigo and M. Birattari, “Ant colony optimization,” in Encyclopedia of Machine Learning. Springer, 2011, pp. 36–39.
[116] D. Karaboga and B. Akay, “A comparative study of artificial bee colony algo- rithm,” Applied Mathematics and Computation, vol. 214, no. 1, pp. 108–132, 2009.
[117] P. Civicioglu and E. Besdok, “A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony al- gorithms,” Artificial Intelligence Review, vol. 39, no. 4, pp. 315–346, 2013.
[118] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459–471, 2007.
[119] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist mul- tiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.
[120] R. Storn and K. Price, “Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997.
[121] K. Price, R. M. Storn, and J. A. Lampinen, Differential evolution: a practical approach to global optimization. Springer Science & Business Media, 2006.
[122] A. K. Qin and P. N. Suganthan, “Self-adaptive differential evolution algorithm for numerical optimization,” in IEEE Congress on Evolutionary Computation, vol. 2. IEEE, 2005, pp. 1785–1791.
[123] J. Sun, S. Gao, H. Dai, J. Cheng, M. Zhou, and J. Wang, “Bi-objective elite differential evolution for multivalued logic networks,” IEEE Transactions on Cybernetics, 2018, doi: 10.1109/TCYB.2018.2868493.
[124] J. Wang, L. Yuan, Z. Zhang, S. Gao, Y. Sun, and Y. Zhou, “Multiobjective multiple neighborhood search algorithms for multiobjective fleet size and mix location-routing problem with time windows,” IEEE Transactions on Systems, Man and Cybernetics: Systems, 2019, doi: 10.1109/TSMC.2019.2912194.
[125] K. R. Opara and J. Arabas, “Differential evolution: A survey of theoretical analyses,” Swarm and Evolutionary Computation, vol. 44, pp. 546–558, 2019.
[126] S. Das and P. N. Suganthan, “Differential evolution: A survey of the state-of- the-art,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 4–31, 2010.
[127] Y. Yu, S. Gao, Y. Wang, and Y. Todo, “Global optimum-based search differ- ential evolution,” IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 2, pp. 379–394, 2018.
[128] G. Wu, X. Shen, H. Li, H. Chen, A. Lin, and P. N. Suganthan, “Ensemble of differential evolution variants,” Information Sciences, vol. 423, pp. 172–186, 2018.
[129] B. Dorronsoro and P. Bouvry, “Improving classical and decentralized differen- tial evolution with new mutation operator and population topologies,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 67–98, 2011.
[130] Y. Wang, Z. Cai, and Q. Zhang, “Enhancing the search ability of differential evolution through orthogonal crossover,” Information Sciences, vol. 185, no. 1, pp. 153–177, 2012.
[131] J. Zhang and A. C. Sanderson, “JADE: adaptive differential evolution with optional external archive,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 945–958, 2009.
[132] W. Gong, Z. Cai, and C. X. Ling, “DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization,” Soft Computing, vol. 15, no. 4, pp. 645–665, 2010.
[133] W.-Y. Lin, “A GA–DE hybrid evolutionary algorithm for path synthesis of four- bar linkage,” Mechanism and Machine Theory, vol. 45, no. 8, pp. 1096–1107, 2010.
[134] N. Awad, M. Ali, J. Liang, B. Qu, and P. Suganthan, “Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective bound constrained real-parameter numerical optimization,” in Technical Report. NTU, Singapore, 2016.
[135] S. Mirjalili, “The ant lion optimizer,” Advances in Engineering Software, vol. 83, pp. 80–98, 2015.
[136] E. Min, X. Guo, Q. Liu, G. Zhang, J. Cui, and J. Long, “A survey of cluster- ing with deep learning: From the perspective of network architecture,” IEEE Access, vol. 6, pp. 39 501–39 514, 2018.
[137] Mydata, http://yarpiz.com/64/ypml101-evolutionary-clustering.
[138] Y. Zhou, J. Wang, J. Chen, S. Gao, and L. Teng, “Ensemble of many-objective evolutionary algorithms for many-objective problems,” Soft Computing, vol. 21, no. 9, pp. 2407–2419, 2017.
[139] S. Song, S. Gao, X. Chen, D. Jia, X. Qian, and Y. Todo, “AIMOES: Archive information assisted multi-objective evolutionary strategy for ab initio protein structure prediction,” Knowledge-Based Systems, vol. 146, pp. 58–72, 2018.
[140] S. Gao, S. Song, J. Cheng, Y. Todo, and M. Zhou, “Incorporation of solvent ef- fect into multi-objective evolutionary algorithm for improved protein structure prediction,” IEEE/ACM Transactions on Computational Biology and Bioinfor- matics, vol. 15, no. 4, pp. 1365–1378, 2018.
[141] S. Gao, Y. Todo, T. Gong, G. Yang, and Z. Tang, “Graph planarization prob- lem optimization based on triple-valued gravitational search algorithm,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 9, no. 1, pp. 39–48, 2014.
[142] J. Cheng, X. Wu, M. Zhou, S. Gao, Z. Huang, and C. Liu, “A novel method for detecting new overlapping community in complex evolving networks,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, doi: 10.1109/TSMC.2017.2779138.
[143] S. Gao, R.-L. Wang, H. Tamura, and Z. Tang, “A multi-layered immune sys- tem for graph planarization problem,” IEICE Transactions on Information and Systems, vol. 92, no. 12, pp. 2498–2507, 2009.
[144] J. Cheng, J. Cheng, M. Zhou, F. Liu, S. Gao, and C. Liu, “Routing in internet of vehicles: A review,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2339–2352, 2015.
[145] J. Cheng, H. Mi, Z. Huang, S. Gao, D. Zang, and C. Liu, “Connectivity mod- eling and analysis for internet of vehicles in urban road scene,” IEEE Access, vol. 6, pp. 2692–2702, 2018.
[146] J. Ji, S. Gao, J. Cheng, Z. Tang, and Y. Todo, “An approximate logic neuron model with a dendritic structure,” Neurocomputing, vol. 173, pp. 1775–1783, 2016.
[147] T. Zhou, S. Gao, J. Wang, C. Chu, Y. Todo, and Z. Tang, “Financial time series prediction using a dendritic neuron model,” Knowledge-Based Systems, vol. 105, pp. 214–224, 2016.
[148] X. Yang, Nature-inspired metaheuristic algorithms. Luniver Press, 2010.
[149] S. Gao, H. Dai, G. Yang, and Z. Tang, “A novel clonal selection algorithm and its application to traveling salesman problem,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. 90, no. 10, pp. 2318–2325, 2007.
[150] H. Dai, Y. Yang, C. Li, J. Shi, S. Gao, and Z. Tang, “Quantum interference crossover-based clonal selection algorithm and its application to traveling sales- man problem,” IEICE Transactions on Information and Systems, vol. 92, no. 1, pp. 78–85, 2009.
[151] S. Gao, Z. Tang, and C. Vairappan, “An effective immune algorithm for multiple-valued logic minimization problems,” International Journal of Inno- vative Computing, Information and Control, vol. 5, no. 11, 2009.
[152] S. Gao, R.-L. Wang, H. Tamura, and Z. Tang, “A multi-layered immune sys- tem for graph planarization problem,” IEICE Transactions on Information and Systems, vol. 92, no. 12, pp. 2498–2507, 2009.
[153] D. E. Goldberg, Genetic algorithms. Pearson Education India, 2006.
[154] J. Kennedy, “Particle swarm optimization,” Encyclopedia of machine learning, pp. 760–766, 2010.
[155] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Advances in Engineering Software, vol. 69, pp. 46–61, 2014.
[156] S. A. Hofmeyr and S. Forrest, “Architecture for an artificial immune system,” Evolutionary Computation, vol. 8, no. 4, pp. 443–473, 2000.
[157] S. Gao, Z. Tang, H. Dai, and J. Zhang, “A hybrid clonal selection algorith- m,” International Journal of Innovative Computing, Information and Control, vol. 4, no. 4, pp. 995–1008, 2008.
[158] S. Gao, H. Dai, J. Zhang, and Z. Tang, “An expanded lateral interactive clonal selection algorithm and its application,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. 91, no. 8, pp. 2223–2231, 2008.
[159] S. Gao, W. Wang, H. Dai, F. Li, and Z. Tang, “Improved clonal selection algorithm combined with ant colony optimization,” IEICE Transactions on In- formation and Systems, vol. 91, no. 6, pp. 1813–1823, 2008.
[160] D. Karaboga, “Artificial bee colony algorithm,” Scholarpedia, vol. 5, no. 3, p. 6915, 2010.
[161] J. Ji, S. Song, C. Tang, S. Gao, Z. Tang, and Y. Todo, “An artificial bee colony algorithm search guided by scale-free networks,” Information Sciences, vol. 473, pp. 142–165, 2019.
[162] Y. Yu, S. Gao, Y. Wang, J. Cheng, and Y. Todo, “ASBSO: An improved brain storm optimization with flexible search length and memory-based selection,” IEEE Access, vol. 6, pp. 36 977–36 994, 2018.
[163] J. Ji, S. Gao, S. Wang, Y. Tang, H. Yu, and Y. Todo, “Self-adaptive gravi- tational search algorithm with a modified chaotic local search,” IEEE Access, vol. 5, pp. 17 881–17 895, 2017.
[164] D. Tang, “Spherical evolution for solving continuous optimization problems,” Applied Soft Computing, vol. 81, p. 105499, 2019.
[165] D. Mustard, “Numerical integration over the n-dimensional spherical shell,” Mathematics of Computation, vol. 18, no. 88, pp. 578–589, 1964.
[166] Y. Wang, Y. Yu, S. Gao, H. Pan, and G. Yang, “A hierarchical gravitational search algorithm with an effective gravitational constant,” Swarm and Evolu- tionary Computation, vol. 46, pp. 118–139, 2019.
[167] Y.-J. Gong, J.-J. Li, Y. Zhou, Y. Li, H. S.-H. Chung, Y.-H. Shi, and J. Zhang, “Genetic learning particle swarm optimization,” IEEE Transactions on Cyber- netics, vol. 46, no. 10, pp. 2277–2290, 2015.
[168] Y. Liu, D. Cheng, Y. Wang, J. Cheng, and S. Gao, “A novel method for pre- dicting vehicle state in internet of vehicles,” Mobile Information Systems, vol. 2018, Article ID 9728328, 2018.
[169] S. Gao, Q. Cao, M. Ishii, and Z. Tang, “Local search with probabilistic model- ing for learning multiple-valued logic networks,” IEICE Transactions on Funda- mentals of Electronics, Communications and Computer Sciences, vol. 94, no. 2, pp. 795–805, 2011.
[170] S. Gao, Q. Cao, C. Vairappan, J. Zhang, and Z. Tang, “An improved local search learning method for multiple-valued logic network minimization with bi-objectives,” IEICE Transactions on Fundamentals of Electronics, Commu- nications and Computer Sciences, vol. 92, no. 2, pp. 594–603, 2009.
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