Hypervolume-based multi-objective local search

Neural Computing and Applications

Volumn 21, Issue 8, 2012, Pages 1917-1929

  Basseur, Matthieu ^a   Zeng, Rong Qiang ^a   Hao, Jin Kao ^a  

^a UNIVERSITÉ D'ANGERS   (France)

Author keywords

Flow shop problem; Hypervolume contribution; Local search; Multi objective; Quadratic assignment problem

Indexed keywords

BINS; COMBINATORIAL OPTIMIZATION; EVOLUTIONARY ALGORITHMS; LOCAL SEARCH (OPTIMIZATION); MACHINE SHOP PRACTICE;

FLOW SHOP PROBLEMS; HYPERVOLUME CONTRIBUTIONS; LOCAL SEARCH; MULTI OBJECTIVE; QUADRATIC ASSIGNMENT PROBLEMS;

OPTIMIZATION;

EID: 85027944526     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-011-0588-4     Document Type: Article

References (28)

1. Arroyo J, Armentano V (2005) Genetic local search for multi-objective flowshop scheduling problems. Eur J Oper Res 167(3): 717-738.
2. Bader J, Deb K, Zitzler E (2008) Faster hypervolume-based search using monte carlo sampling. In: Conference on multiple criteria decision making (MCDM 2008). Springer, New York, pp 313-326.
3. Basseur M, Burke EK (2007) Indicator-based multiobjective local search. In: Proceedings of the IEEE congress on evolutionary computation (CEC 2007). Singapore, September, pp 3100-3107.
4. Basseur M, Seynhaeve F, Talbi E-G (2002) Design of multi-objective evolutionary algorithms: application to the flow-shop scheduling problem. In: Congress on evolutionary computation, vol 2. Honolulu, USA, pp 1151-1156.
5. Beume N (2009) S-metric calculation by considering dominated hypervolume as klee's measure problem. Evol Comput 17(4): 477-492.
6. Beume N, Naujoks B, Emmerich M (2007) SMS-EMOA: multiobjective selection based on dominated hypervolume. Eur J Oper Res 181: 1653-1669.
7. Bradstreet L, While L, Barone L (2008) A fast incremental hypervolume algorithm. IEEE Trans Evol Comput 12(6): 714-723.
8. Bringmann K, Friedrich T (2008) Approximating the volume of unions and intersections of high-dimensional geometric objects. In: ISAAC'08: proceedings of the 19th international symposium on algorithms and computation, Berlin, Heidelberg. Springer, pp 436-447.
9. Bringmann K, Friedrich T (2009) Don't be greedy when calculating hypervolume contributions. In: FOGA'09: proceedings of the tenth ACM SIGEVO workshop on foundations of genetic algorithms. New York, NY, USA, ACM, pp 103-112.
10. Coello Coello CA, Lamont GB, van Veldhuizen DA (2006) Evolutionary algorithms for solving multi-objective problems (genetic and evolutionary computation). Springer, New York, Inc., Secaucus, NJ, USA.
11. Day O, Lamont B (2005) Multiobjective quadratic assignment problem solved by an explicit building block search algorithm-momga-iia. In: EvoCOP, volume 3448 of lecture notes in computer science. Springer, New York, pp 91-100.
12. Dhaenens C, Lemesre J, Talbi E (2010) K-ppm: A new exact method to solve multiobjective combinatorial optimization problems. Eur J Oper Res 1: 45-53.
13. Du J, Leung JY-T (1990) Minimizing total tardiness on one machine is NP-hard. Math Oper Res 15: 483-495.
14. Figueira J, Liefooghe A, Talbi E-G, Wierzbicki A (2010) A parallel multiple reference point approach for multi-objective optimization. Eur J Oper Res 205(2): 390-400.
15. Knowles JD, Corne DW (2000) Approximating the nondominated front using the pareto archived evolution strategy. Evol Comput 8(2): 149-172.
16. Knowles JD, Thiele L, Zitzler E (2005) A tutorial on the performance assessment of stochastive multiobjective optimizers. Technical report TIK-Report No. 214, Computer Engineering and Networks Laboratory, ETH Zurich, July.
17. Landa-Silva D, Burke EK, Petrovic S (2004) Metaheuristic for multiobjective optimisation, chapter an introduction to multiobjective metaheuristics for scheduling and timetabling. Springer, New York, pp 91-129.
18. Lenstra JK, Rinnooy Kan AHG, Brucker P (1977) Complexity of machine scheduling problems. Ann Discrete Math 1: 343-362.
19. Liefooghe A, Mesmoudi S, Humeau J, Jourdan L, Talbi E-G (2009) Designing, implementing and analyzing effective heuristics. In: Engineering stochastic local search algorithms, volume 5752 of lecture notes in computer science. Springer, New York, pp 120-124.
20. Nagar A, Haddock J, Heragu S (1995) Multiple and bicriteria scheduling: a litterature survey. Eur J Oper Res 81: 88-104.
21. Paquete L, Stuetzle T (2006) A study of local search algorithms for the biobjective QAP with correlated flow matrices. Eur J Oper Res 169(3): 943-959.
22. Pardalos P, Rendl F, Wolkowicz H (1994) The quadratic assignment problem: a survey and recent developments. In: Proceedings of the DIMACS workshop on quadratic assignment problems, volume 16 of DIMACS series in discrete mathematics and theoretical computer science, pp 1-42.
23. Sahni S, Gonzalez T (1976) P-complete approximation problems. J ACM 23(3): 555-565.
24. Taillard E (1993) Benchmarks for basic scheduling problems. Eur J Oper Res 64: 278-285.
25. Zhang Q, Li H (2006) A multiobjective evolutionary algorithm based on decomposition. Technical report TIK-report CSM-450, Department of Computer Science, University of Essex.
26. Zitzler E, Künzli S (2004) Indicator-based selection in multiobjective search. In 8th international conference on parallel problem solving from nature (PPSN VIII), pp 832-842, Birmingham, UK, September.
27. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. Evol Comput 3: 257-271.
28. Zitzler E, Thiele L, Laumanns M, Foneseca CM, Grunert da Fonseca V (2003) Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans Evol Comput 7(2): 117-132.