[1] M. Mavrovouniotis, Ch. Li and S. Yang, “A survey of swarm intelligence for dynamic optimization: Algorithm and application”, Swarm and Evolutionary Computation, Vol. 33, pp. 1-17, 2017.
[2] T. T. Nguyen, S. Yang, and J. Branke, “Evolutionary dynamic optimization: A survey of the state of the art”, Swarm and Evolutionary Computation, Vol. 6, pp. 1-24, 2012.
[3] M. Mavrovouniotis, S. Yang, “Adapting the pheromone evaporation rate in dynamic routing problems”, In European Conference on the Applications of Evolutionary Computation, pp. 606–615, 2013.
[4] A. Baykaso˘glu, F. Ozsoydan, “An improved firefly algorithm for solving dynamic multidimensional knapsack problems”, Expert Systems with Applications, Vol. 41, pp. 3712–3725, 2014.
[5] J. Euchi, A. Yassine, H. Chabchoub, “The dynamic vehicle routing problem: Solution with hybrid metaheuristic approach”, Swarm and Evolutionary Computation, Vol. 21, pp. 41–53, 2015.
[6] Y. E. Demirtas¸, E. ¨Ozdemir, U. Demirtas¸, “A particle swarm optimization for the dynamic vehicle routing problem”, In Modeling, Simulation, and Applied Optimization (ICMSAO), pp. 1–5, 2015.
[7] M. R. Khouadjia, B.Sarasola, E. Alba, L. Jourdan, E.-G. Talbi, “A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests”, Applied Soft Computing, Vol. 12, pp. 1426–1439, 2012.
[8] M. Okulewicz, J.Ma´ndziuk, “Application of particle swarm optimization algorithm to dynamic vehicle routing problem”, In Artificial Intelligence and Soft Computing (ICAISC), pp. 547–558, 2013.
[9] M. Mavrovouniotis, S. Yang, “Applying ant colony optimization to dynamic binary-encoded problem”, In Applications of Evolutionary Computation, Vol. 9028, pp. 845–856, 2015.
[10] U. Boryczka, Ł. Stra¸k, “Diversification and entropy improvement on the DPSO algorithm for DTSP”, In Intelligent Information and Database Systems (ACIIDS), pp. 337–347, 2015.
[11] M. Mavrovouniotis, S. Yang, “Ant colony optimization with self-adaptive evaporation rate in dynamic environments”, In IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp. 47–54, 2014.
[12] M. Mavrovouniotis, S. Yang, “Memory-based immigrants for ant colony optimization in changing environments”, In Applications of Evolutionary Computation, Vol. 6624, pp. 324–333, 2011,
[13] M. Mavrovouniotis, S. Yang, “Interactive and non-interactive hybrid immigrants schemes for ant algorithms in dynamic environments”, In IEEE Congress on Evolutionary Computation (CEC), pp. 1542– 1549, 2014.
[14] S. Gao, Y.Wang, J. Cheng, Y. Inazumi, Z. Tang, “Ant colony optimization with clustering for solving the dynamic location routing problem”, Applied Mathematics and Computation., Vol. 285, pp. 149–173, 2016.
[15] M. Mavrovouniotis, S. Yang, “Ant colony optimization with memorybased immigrants for the dynamic vehicle routing problem”, In 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 2645– 2652, 2012.
[16] M. Mavrovouniotis, S. Yang, “Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors”, Applied Soft Computing, Vol. 13, pp. 4023–4037, 2013.
[17] M. Mavrovouniotis, S. Yang, X. Yao, “Multi-colony ant algorithms for the dynamic travelling salesman problem”, In IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp. 9–16, 2014.
[18] B. van Veen, M. Emmerich, Z. Yang, T. B¨ack, J. Kok, “Ant colony algorithms for the dynamic vehicle routing problem with time windows”, In 5th International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC), pp. 1–10, 2013.
[19] Z. Yang, M. Emmerich, T. B¨ack, “Ant based solver for dynamic vehicle routing problem with time windows and multiple priorities”, In 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 2813– 2819, 2015.
[20] L. Melo, F. Pereira, E. Costa, “Multi-caste ant colony algorithm for the dynamic traveling salesperson problem”, In Adaptive and Natural Computing Algorithms, pp. 179–188, 2013.
[21] L. Melo, F. Pereira, E. Costa, “Extended experiments with ant colony optimization with heterogeneous ants for large dynamic traveling salesperson problems”, In 14th International Conference on Computational Science and Its Applications (ICCSA), pp. 171–175, 2014.
[22] U. Boryczka, Ł. Stra¸k, “Heterogeneous DPSO algorithm for DTSP”, In Computational Collective Intelligence (ICCCI), pp. 119–128, 2015.
[23] M. Okulewicz, J. Ma´ndziuk, “Two-phase multi-swarm PSO and the dynamic vehicle routing problem”, In IEEE Symposium on Computational Intelligence for Human-like Intelligence (CIHLI), pp. 1–8, 2014.
[24] M. Mavrovouniotis, S. Yang, “A memetic ant colony optimization algorithm for the dynamic travelling salesman problem”, Soft Computing, Vol. 15, No. 7, pp. 1405–1425, 2011.
[25] M. Mavrovouniotis, S. Yang, “Dynamic vehicle routing: A memetic ant colony optimization approach”, In Automated Scheduling and Planning, pp. 283– 301, 2013.
[26] M. Mavrovouniotis, F. M. M¨uller and S. Yang, “Ant colony optimization with local search for the dynamic travelling salesman problems”, IEEE Transactions on Cybernetics, Vol. 99, pp. 1–14, 2016.
[27] L. Liu and S. R. Ranjithan, “An adaptive optimization technique for dynamic environments”, Engineering Applications of Artificial Intelligence, Vol. 23, No. 5, pp. 772– 779, 2010.
[28] D. Parrott, X. Li, “Locating and tracking multiple dynamic optima by a particle swarm model using speciation”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 4, pp. 440–458, 2006.
[29] S. Yang and C. Li, “A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 6, pp. 959–974, 2010.
[30] C. Li and S. Yang, “A clustering particle swarm optimizer for dynamic optimization”, In IEEE Congress on Evolutionary Computation, pp. 439–446, 2009.
[31] J. Branke, “Memory enhanced evolutionary algorithms for changing optimization problems”, In Proceedings of the 1999 Congress on Evolutionary Computation, Vol. 3, pp. 1875–1882, 1999.
[32] J. Yaochu and J. Branke, “Evolutionary optimization in uncertain environments-a survey”, IEEE Transactions on Evolutionary Computation, Vol. 9, pp. 303-317, 2005.
[33] M. Khouadjia, E. Alba, L. Jourdan, E.-G. Talbi, “Multi-swarm optimization for dynamic combinatorial problems: A case study on dynamic vehicle routing problem”, In Swarm Intelligence, pp. 227– 238, 2010.
[34] J. Branke, T. Kaußler, C. Schmidth, and H. Schmeck, “A multipopulation approach to dynamic optimization problem”, In Proceedings of the Congress on Evolutionary Computation, pp. 299–308, 2000.
[35] C. Li and S. Yang, “Fast multi-swarm optimization for dynamic optimization problems”, In 4th International Conference on Natural Computation, Vol. 7, pp. 624–628, 2008.
[36] M. Kamosi, A. B. Hashemi, and M. R. Meybodi, “A hibernating multiswarm optimization algorithm for dynamic environments”, In World Congress on Nature and Biologically Inspired Computing, NaBIC2010, pp. 363–369, 2010.
[37] T. M. Blackwell and J. Branke, “Multiswarms, exclusion, and anticonvergence in dynamic environments”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 4, pp. 459–472, 2006.
[38] I. del Amo, D. Pelta, Gonz´aez, and J. lez, “Using heuristic rules to enhance a multiswarm pso for dynamic environments”, In IEEE Congress on Evolutionary Computation, pp. 1–8, 2010.
[39] C. Li, S. Yang, “A general framework of multipopulation methods with clustering in undetectable dynamic environments”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 4, pp. 556–577, 2012.
[40] A. Simoes and E. Costa, “Evolutionary algorithms for dynamic environments: prediction using linear regression and markov chains”, In Parallel Problem Solving from Nature, pp. 306–315, 2008.
[41] J. Kennedy and R. C. Eberhart, “Particle swarm optimization”, In Proc. IEEE International Conference on Neural Networks, pp. 1942–1948, 1995.
[42] Y. Shi, R. Eberhart, “A modified particle swarm optimizer”, In IEEE World Congress on Computational Intelligence, pp. 69–73, 1998.
[43] R. I. Lung and D. Dumitrescu, “A collaborative model for tracking optima in dynamic environments”, In IEEE Congress on Evolutionary Computation, pp. 564–567, 2007.
[44] S. Bird and X. Li, “Using regression to improve local convergence”, In IEEE Congress on Evolutionary Computation, pp. 592–599, 2007.
[45] T. Blackwell, J. Branke, and X. Li, “Particle swarms for dynamic optimization problems", Swarm Intelligence, pp. 193-217, 2008.
[46] M. Kamosi, A. Hashemi, and M. Meybodi, “A New Particle Swarm Optimization Algorithm for Dynamic Environments", In International Conference on Swarm, Evolutionary, and Memetic Computing, pp. 129-138, 2010.
[47] I. Rezazadeh, M. Meybodi, and A. Naebi, “Adaptive particle swarm optimization algorithm for dynamic environments”, Advances in Swarm Intelligence, pp. 120-129, 2011.
[48] D. Yazdani, B. Nasiri, A. Sepas-Moghaddam, and M. R. Meybodi, “A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization”, Applied Soft Computing, Vol. 13, pp. 2144-2158, 2013.
[49] B. Nasiri and M. Meybodi, “Speciation based firefly algorithm for optimization in dynamic environments”, International Journal of Artificial Intelligence, Vol. 8, pp. 118-132, 2012.
[50] D. Yazdani, B. Nasiri, R. Azizi, A. Sepas-Moghaddam, and M. R. Meybodi, “Optimization in Dynamic Environments Utilizing a Novel Method Based on Particle Swarm Optimization”, International Journal of Artificial Intelligence, Vol. 11, pp. 170-192, 2013.
[51] J. K. Kordestani, A. Rezvanian, and M. R. Meybodi, “CDEPSO: a bi-population hybrid approach for dynamic optimization problems”, Applied intelligence, Vol. 40, pp. 682-694, 2014.
[52] R. Mukherjee, G. R. Patra, R. Kundu, and S. Das, “Cluster-based differential evolution with Crowding Archive for niching in dynamic environments”, Information Sciences, Vol. 267, pp. 58- 82, 2014.