Ombuki B, Ross BJ, Hanshar F (2006) Multi-objective genetic algorithms for vehicle routing problem with time windows. Appl Intell 24:17–30
Coello Coello CA, Van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems. Springer US, New York
Fonseca CM, Fleming PJ (1995) An overview of evolutionary algorithms in multi-objective optimization. Evol Comput 3:1–16
Van Veldhuizen DA, Lamont GB (2000) Multi-objective evolutionary algorithms: analyzing the state-of-the-art. Evol Comput 8:125–147
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York
Goldberg DE (1998) Genetic algorithm in search, optimization, and machine learning. Addison-Wesley, Reading
Haupt RL, Haupt HS (2004) Practical genetic algorithms, 2nd edn. Wiley, Hoboken
Arulmozhiyal R, Jubril AM (2012) A nonlinear weights selection in weighted sum for convex multi-objective optimization. Facta Univ Ser Math Inform 27:357–372
Zadeh LA (1963) Optimality & non-scalar-valued performance criteria. IEEE Trans Autom Control 8:59–60
Konak A, Coitb DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliabil Eng Syst Saf 91:992–1007
Athan TW, Papalambros PY (1996) A note on weighted criteria methods for compromise solutions in multi-objective optimization. Eng Opt 27:155–176
Ryu JH, Kim S, Wan H (2009) Pareto front approximation with adaptive weighted sum method in multi-objective simulation optimization. In: Proceedings of 2009 Winter Simulation Conference, Austin, TX, pp 623–633
Schmaranzer D, Braune R, Doerner KF (2019) Multi-objective simulation optimization for complex urban mass rapid transit systems. Ann Oper Res 1–38
Dantzig GB, Ramser JH (1959) The truck dispatching problem. Manag Sci 6(1):80–91
Carić T, Galić A, Fosin J, Gold H, Reinholz A (2008) A modelling and optimization framework for real-world vehicle routing problems, vehicle routing problem. I-Tech Education and Publishing, Vienna
Golden B, Raghavan S, Wasil E (2008) The vehicle routing problem: latest advances and new challenges. Operations research-computer science interfaces series. Springer, Berlin, p 43
Han Y, Shan J, Wang M, Yang G (2017) Optimization design and evaluation of parking route based on automatic assignment mechanism of parking lot. Adv Mech Eng 9:1–9
Siemens (2020) Intelligent Parking Solutions—Siemens. www.mobility.siemens.com/global/en/portfolio/road/parking-solutions/intelligent-parking-solutions.html. Accessed 23 June 2020
Xiao Y, Konak A (2017) A genetic algorithm with exact dynamic programming for green vehicle routing & scheduling problem. J Clean Prod 167:1450–1463
Lee HY, Shin H, Chae J (2018) Path planning for mobile agents using a genetic algorithm with a direction guided factor. Electronics 7:212
Fogel D (2005) Evolutionary computation: toward a new philosophy of machine intelligence, 3rd edn. Wiley, Hoboken
Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. MIT Press, Cambridge
Bryant K (2000) Genetic algorithms and the traveling salesman problem. Master’s Dissertation. Harvey Mudd College, Claremont, United States
Chand P, Mohanty JR (2013) A Multi-objective vehicle, routing problem using dominant rank method. In: Proceedings of international conference in Distributed Computing & Internet Technology, International Journal of Computer Applications 0975-8887, pp 29–34
Pelikan M (2010) Genetic algorithms. MEDAL Report No. 2010007
Athan TW, Papalambros PY (1996) A quasi-Monte Carlo method for multi-criteria optimization. Eng Opt 27:177–198
Gennert MA, Yuille AL (1998) Determining the optimal weights in multiple objective function optimization. In: 2nd International Conference on Computer Vision, IEEE, Los Alamos, CA, pp 87–89
Martins MSR, Delgado MRBS, Lüders R (2018) Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem. J Heuristics 24:25–47
Tang Y, Marshall L, Sharma A, Ajami H (2018) A Bayesian alternative for multi-objective eco-hydrological model specification. J Hydrol 556:25–38
Wada T, Hino H (2019) Bayesian optimization for multi-objective optimization and multi-point search. ArXiv, abs/1905.02370
Brochu E, Cora VM, de Freitas N (2010) A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv:1012.2599
Hernández-Lobato D, Hernandez-Lobato J, Shah A, Adams R (2016) Predictive entropy search for multi-objective bayesian optimization. In: International conference on machine learning, pp 1492–1501
Tsoulos IG, Stavrou V, Mastorakis NE, Tsalikakis D (2019) GenConstraint: a programming tool for constraint optimization problems. SoftwareX 10:100355
Mockus J (1989) Bayesian approach to global optimization: theory and applications. Springer, Dordrecht
Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems, vol 25, pp 2951–2959
Tajbakhsh SD (2012) A fully Bayesian approach to the efficient global optimization algorithm. Ph.D. Dissertation. The Pennsylvania State University, Pennsylvania
Galuzio PP, Hochsteiner de Vasconcelos Segundo E, Santos-Coelho LD, Mariani VC (2020) MOBOpt—multi-objective Bayesian optimization. SoftwareX 12:100520