Multi-objective Optimization Algorithms with the Island Metaheuristic for Effective Project Management Problem Solving

Alves, M.J., & Almeida, M. (2007). MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem. Computers & Operations Research, 34(11), 3458-3470, http://dx.doi.org/10.1016/j.cor.2006.02.00810.1016/j.cor.2006.02.008Open DOISearch in Google Scholar

Aslan, O., Mert Kantar, Y., & Usta, I. (2015). Genetic algorithms for solving portfolio allocation models based on relative-entropy, mean and variance. Journal of Scientific Research and Development, 2 (12), 7-12. Retrieved from http://jsrad.org/wp-content/2015/Issue%2012,%202015/2jj.pdfSearch in Google Scholar

Brester Ch. & Semenkin E. (2015). Cooperative Multi-objective Genetic Algorithm with Parallel Implementation. Advances in Swarm and Computational Intelligence, LNCS vol. 9140. pp. 471–478. Springer Nature.10.1007/978-3-319-20466-6_49Search in Google Scholar

Crainic, T. G., Toulouse, M. (2010). Parallel metaheuristics. In Handbook of metaheuristics, pp. 497–541. Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-1-4419-1665-5_17Search in Google Scholar

Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6 (2), 182–197, https://doi.org/10.1109/4235.99601710.1109/4235.996017Open DOISearch in Google Scholar

Drezewski, R., & Doro, K. (2017). An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization, Symmetry, 9, 168, http://dx.doi.org/10.3390/sym909016810.3390/sym9090168Open DOISearch in Google Scholar

Feo, T. A., & Resende, M. G. C. (1995). Greedy randomized adaptive search procedures. Journal of Global Optimization, 6, 109-133, http://dx.doi.org/10.1007/BF0109676310.1007/BF01096763Open DOISearch in Google Scholar

Florios, K., Mavrotas, G., & Diakoulaki, D. (2010). Solving multiobjective, multiconstraint knapsack problems using mathematical programming and evolutionary algorithms. European Journal of Operational Research, 203, (1), pp. 14-21, http://dx.doi.org/10.1016/j.ejor.2009.06.02410.1016/j.ejor.2009.06.024Open DOISearch in Google Scholar

Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading: MA: Addison-Wesley Professional.Search in Google Scholar

Kennedy, J., & Eberhart, R. (1995). Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948, https://doi.org/10.1109/ICNN.1995.48896810.1109/ICNN.1995.488968Open DOISearch in Google Scholar

Kuo, R. J. & Hong, C. W. (2013). Integration of Genetic Algorithm and Particle Swarm Optimization for Investment Portfolio Optimization. Appl. Math. Inf. Sci., 7(6), 2397-2408, http://dx.doi.org/10.12785/amis/07063310.12785/amis/070633Open DOISearch in Google Scholar

Markowitz, H. (1952). Portfolio selection. J. Finance, 7, 77–91, http://dx.doi.org/10.1111/j.1540-6261.1952.tb01525.x10.1111/j.1540-6261.1952.tb01525.xOpen DOISearch in Google Scholar

Rom, B. M., & K. Ferguson. (1993). Post-Modern Portfolio Theory Comes of Age. Journal of Investing, Winter 1993. retrieved from http://www.actuaries.org/AFIR/colloquia/Orlando/Ferguson_Rom.pdf10.3905/joi.2.4.27Search in Google Scholar

Preuss, M. (2015). Multimodal Optimization by Means of Evolutionary Algorithms, Springer International Publishing, http://dx.doi.org/10.1007/978-3-319-07407-810.1007/978-3-319-07407-8Open DOISearch in Google Scholar

Vianna, D. S., Vianna, M. F. D. (2013). Local search-based heuristics for the multiobjective multidimensional knapsack problem. Produção, 23(3), 478-487, http://dx.doi.org/10.1590/S0103-6513201200500008110.1590/S0103-65132012005000081Open DOISearch in Google Scholar

Wang, R. (2013). Preference-inspired co-evolutionary algorithms. A thesis submitted in partial fulfilment for the degree of the Doctor of Philosophy, University of Sheffield, p. 231.Search in Google Scholar

Whitley, D., Rana, S. & Heckendorn, R. (1997). Island model genetic algorithms and linearly separable problems. Proceedings of AISB Workshop on Evolutionary Computation, Manchester, UK. Springer, volume 1305 of LNCS, pp. 109-125.10.1007/BFb0027170Search in Google Scholar

Zhang, Q., Zhou, A., Zhao, S., Suganthan, P. N., Liu, W., Tiwari, S. (2008). Multi-objective optimization test instances for the CEC 2009 special session and competition. University of Essex and Nanyang Technological University, Tech. Rep. CES-487. Retrieved from http://dces.essex.ac.uk/staff/zhang/MOEAcompetition/cec09testproblem0904.pdfSearch in Google Scholar

Zitzler, E., Laumanns, M. & Thiele, L. (2002). SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. Evolutionary Methods for Design Optimisation and Control with Application to Industrial Problems EUROGEN 2001, 3242 (103), pp. 95–100.Search in Google Scholar