Domination Measure: A New Metric for Solving Multiobjective Optimization

References

• Auger A, Bader J, Brockhoff D, Zitzler E (2012) Hypervolume-based multiobjective optimization: theoretical foundations and practical implications. Theoret. Comput. Sci. 425(March):75–103.Crossref, Google Scholar
• Bader J, Zitzler E (2011) Hype: an algorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 19(1):45–76.Crossref, Google Scholar
• Bader J, Deb K, Zitzler E (2010) Faster hypervolume-based search using Monte Carlo sampling. Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems (Springer, New York), 313–326.Crossref, Google Scholar
• Bekker J, Aldrich C (2011) The cross-entropy method in multi-objective optimisation: An assessment. Eur. J. Oper. Res. 211(1):112–121.Crossref, Google Scholar
• Beume N, Naujoks B, Emmerich M (2007) Sms-emoa: Multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181(3):1653–1669.Crossref, Google Scholar
• Bhatia SK (2004) Adaptive k-means clustering. Barr V, Markov Z, eds. Proc. 17th Internat. Florida Artificial Intelligence Res. Soc. Conf. (AAAI Press, Menlo Park, CA), 695–699.Google Scholar
• Coello CAC, Van Veldhuizen DA, Lamont GB (2002) Evolutionary Algorithms for Solving Multi-Objective Problems, vol. 242 (Springer, New York).Crossref, Google Scholar
• Corne DW, Jerram NR, Knowles JD, Oates MJ (2001) Pesa-ii: region-based selection in evolutionary multiobjective optimization. Aguirre H, ed. Proc. Genetic Evolutionary Comput. Conf. (GECCO’2001) (ACM, New York).Google Scholar
• Custódio AL, Madeira JA, Vaz AIF, Vicente LN (2011) Direct multisearch for multiobjective optimization. SIAM J. Optim. 21(3):1109–1140.Crossref, Google Scholar
• Deb K (2001) Multi-Objective Optimization Using Evolutionary Algorithms, vol. 16 (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Feldman G, Hunter SR, Pasupathy R (2015) Multi-objective simulation optimization on finite sets: optimal allocation via scalarization. 2015 Winter Simulation Conf. (WSC) (IEEE Press, Piscataway, NJ), 3610–3621.Google Scholar
• Fleischer M (2003) The measure of Pareto optima applications to multi-objective metaheuristics. Evolutionary Multi-Criterion Optimization (Springer, New York), 519–533.Crossref, Google Scholar
• Hu J, Fu MC, Marcus SI (2007) A model reference adaptive search method for global optimization. Oper. Res. 55(3):549–568.Link, Google Scholar
• Huband S, Hingston P, Barone L, While L (2006) A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans. Evolutionary Comput. 10(5):477–506.Crossref, Google Scholar
• Kim M, Hiroyasu T, Miki M, Watanabe S (2004) SPEA2+: Improving the performance of the strength Pareto evolutionary algorithm 2. Parallel Problem Solving from Nature-PPSN VIII (Springer, New York), 742–751.Crossref, Google Scholar
• Köksalan M, Phelps S (2007) An evolutionary metaheuristic for approximating preference-nondominated solutions. INFORMS J. Comput. 19(2):291–301.Link, Google Scholar
• Le Riche R, Saouab A, Bréard J (2003) Coupled compression rtm and composite layup optimization. Compos. Sci. Tech. 63(15):2277–2287.Crossref, Google Scholar
• Lee LH, Chew EP, Teng S, Chen Y (2008) Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem. Eur. J. Oper. Res. 189(2):476–491.Crossref, Google Scholar
• Lee LH, Chew EP, Teng S, Goldsman D (2010) Finding the non-dominated pareto set for multi-objective simulation models. IIE Trans. 42(9):656–674.Crossref, Google Scholar
• Mete HO, Zabinsky ZB (2014) Multiobjective interacting particle algorithm for global optimization. INFORMS J. Comput. 26(3):500–513.Link, Google Scholar
• Minella G, Ruiz R, Ciavotta M (2008) A review and evaluation of multiobjective algorithms for the flowshop scheduling problem. INFORMS J. Comput. 20(3):451–471.Link, Google Scholar
• Molina J, Laguna M, Martí R, Caballero R (2007) Sspmo: A scatter tabu search procedure for non-linear multiobjective optimization. INFORMS J. Comput. 19(1):91–100.Link, Google Scholar
• Nam D, Park CH (2000) Multiobjective simulated annealing: A comparative study to evolutionary algorithms. Internat. J. Fuzzy Systems 2(2):87–97.Google Scholar
• Pal M, Bandyopadhyay S (2016) Reliability of convergence metric and hypervolume indicator for many-objective optimization. 2016 2nd Internat. Conf. Control, Instrumentation, Energy & Comm. (CIEC) (IEEE, Piscataway, NJ), 511–515.Google Scholar
• Qasem SN, Shamsuddin SM (2011) Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis. Appl. Soft Comput. 11(1):1427–1438.Crossref, Google Scholar
• Rubinstein RY (2001) Combinatorial optimization, cross-entropy, ants and rare events. Uryasev S, Pardalos PM, eds. Stochastic Optimization: Algorithms and Applications (Springer, New York), 303–363.Google Scholar
• Santiago A, Huacuja HJF, Dorronsoro B, Pecero JE, Santillan CG, Barbosa JJG, Monterrubio JCS (2014) A survey of decomposition methods for multi-objective optimization. Castillo O, Melin P, Pedrycz W, Kacprzyk J, eds. Recent Advances on Hybrid Approaches for Designing Intelligent Systems (Springer, New York), 453–465.Google Scholar
• Toffolo A, Lazzaretto A (2002) Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design. Energy 27(6):549–567.Crossref, Google Scholar
• Unveren A, Acan A (2007) Multi-objective optimization with cross entropy method: stochastic learning with clustered pareto fronts. 2007 IEEE Congress on Evolutionary Comput. (IEEE Press, Piscataway, NJ), 3065–3071.Google Scholar
• Zabinsky ZB (2013) Stochastic Adaptive Search for Global Optimization, vol. 72. (Springer, New York).Google Scholar
• Zhou E, Hu J (2014) Gradient-based adaptive stochastic search for non-differentiable optimization. IEEE Trans. Automat. Control. 59(7):1818–1832.Crossref, Google Scholar
• Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms—a comparative case study. Eiben AE, Bäck T, Schoenauer M, Schwefel H-P, eds. Internat. Conf. Parallel Problem Solving Nature (Springer, New York), 292–301.Crossref, Google Scholar
• Zitzler E, Brockhoff D, Thiele L (2007) The hypervolume indicator revisited: On the design of Pareto-compliant indicators via weighted integration. Trautmann H, Rudolph G, Klamroth K, Schütze O, Wiecek M, Jin Y, Grimme C, eds. Evolutionary Multi-Criterion Optimization (Springer, New York), 862–876.Google Scholar
• Zitzler E, Thiele L, Laumanns M, Fonseca CM, Da Fonseca VG (2003) Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evolutionary Comput. 7(2):117–132.Crossref, Google Scholar