Serafini P. (1992), Simulated annealing for multiple objective optimization problems, in: Proceedings of the Tenth International Conference on Multiple Criteria Decision Making, Taipei 19-24.07, vol. 1, 87-96.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....of the methods are well known from the literature. The methods are: the multiple objective genetic local search algorithm (MOGLS) proposed by us [14] 16] Ishibuchi s and Murata s multiple objective genetic local search (IMMOGLS) 12] Serafini s multiple objective simulated annealing (SMOSA) [23], multiple objective simulated annealing proposed by Ulungu et al. MOSA) 28] Pareto simulated annealing (PSA) proposed by us [2] nondominated sorting genetic algorithm (NSGA) 25] controlled elitist non dominated sorting genetic algorithm (CENSGA) 3] strength Pareto evolutionary algorithm ....

....other hybrid genetic algorithms. The method repeatedly draws a scalarizing function and uses a local heuristic to optimize the function starting from a random solution. 4.3. Simulated annealing algorithms The first multiple objective version of simulated annealing has been proposed by Serafini [23]. The method is called Serafini s multiple objective simulated annealing (SMOSA) The algorithm of the method is almost the same as the algorithm of single objective SA. The method uses a single current solution. Serafini considered a number of multiple objective rules for acceptance of new ....

Serafini P. (1992), Simulated annealing for multiple objective optimization problems, in: Proceedings of the Tenth International Conference on Multiple Criteria Decision Making, Taipei 19-24.07, vol. 1, 87-96.

....set. 5.1. Multiple objective acceptance rules Each generating solution x may be modified by accepting a randomly generated solution from the neighborhood of x. The new solution is accepted with some probability. PSA uses the concept of multiple objective acceptance rules proposed by Serafini [147] (see section 3.2.1) In the multiple objective case one of the following three exclusive situations may occur while comparing a new solution y with the current one x (see Figure 1) y dominates or is equal to x, y is dominated by x, y is nondominated with respect to x. In the first ....

Serafini P. (1994), Simulated annealing for multiple objective optimization problems, in: G.H. Tzeng, H.F. Wang, V.P. Wen, P.L. Yu (eds.), Multiple Criteria Decision Making. Expand and Enrich the Domains of Thinking and Application, Springer, Berlin, 283-292.

....of elite solutions added to each generation, while in Zitzler s and Thiele s SPEA algorithm [188] all solutions from this set participate in selection. 3.2. Local search methods 3.2.1. Simulated annealing The first multiple objective version of simulated annealing was proposed by Serafini [146] in 1992. The method uses the standard scheme of simulated annealing with single current solutions. The outcome of the algorithm is the set of potentially Pareto optimal solutions containing all the solutions not dominated by any other solution generated by the algorithm. Serafini considered a ....

....in Pareto ranking based multiple objective genetic evolutionary algorithms (see section 3.1. 2) PAES [100] M PAES [101] and Shelokar s et al. ant colonies algorithm [148] Scalarizing functions are used in Murata et al. multiple objective genetic algorithm [124] Serafini s simulated annealing [146], MOSA [170] Gandibleux et al. tabu search [44] Hansen s tabu search [50] CHESS [8] Iredi s et al. ant colonies algorithm [74] and Ishibuchi s and Murata s genetic local search [74] Some methods use both the ideas, e.g. Suppapitnarm s and Parks s [158] simulated annealing or Ben Abdelaziz s ....

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Serafini P. (1992), Simulated annealing for multiple objective optimization problems, in: Proceedings of the Tenth International Conference on Multiple Criteria Decision Making, Taipei 19-24.07, vol. 1, 87-96.

.... : plusieurs probl emes classiques d optimisation combinatoire ont et e etudi es dans une version multi objectif [13] sac a dos [1] ordonnancement [59] 73] plus court chemin dans un graphe [96] arbre recouvrant (minimum spanning tree) 102] affectation [90] voyageur de commerce [76], routage de v ehicules [63] etc. Prenons comme exemple illustratif le probl eme du sac a dos multi objectif qui peut etre mod elis e de la mani ere suivante [1] 8 : Max(f i (x) P m j=1 c i j x j (i = 1; n) x 2 C C = fx= P m j=1 w j x j w x j 2 f0; 1g, 8j ....

....: F = n X i=1 i f i f i o u f i est le param etre de normalisation de l objectif f i , n est le nombre d objectifs, et i sont les poids associ es a chaque objectif f i . ffl Recuit simul e : l algorithme du recuit simul e a et e utilis e pour le voyageur de commerce multi objectif [76], pour le design de r eseaux de transport [27] et pour le probl eme du sac a dos multi objectif [92] o u la fonction d acceptation d une solution voisine est de la forme : P xy (T ) min(1; e P n i=1 i (f i (x) Gammaf i (y) T ) o u x est la solution courante, y est la solution voisine ....

[Article contains additional citation context not shown here]

P. Serafini. Simulated annealing for multiple objective optimization problems. In Tenth Int. Conf. on Multiple Criteria Decision Making, pages 87--96, Taipei, July 1992.

....approximations of the whole Pareto set. The methods are usually based on classical single objective metaheuristics. For example, the methods of Schaffer [21] Fonseca and Fleming [4] Horn, Nafpliotis and Goldberg [9] Srinivas and Deb [24] are based on genetic algorithms, the methods of Serafini [22], Czyzak and Jaszkiewicz [2] Ulungu et al. 29] are based on simulated annealing, and the methods of Gandibleux et al. 6] and Hansen [8] are based on tabu search. Hwang et al. 11] proposed a classification of MOO methods taking into account the moment of collecting the preference information ....

Serafini P. (1994). Simulated annealing for multiple objective optimization problems. In: Tzeng G.H., Wang H.F., Wen V.P., Yu P.L. (eds), Hy#vfyr 8...v#r...vh 9rpv+v' Hhxvt# @`fhq hq @...vpu #ur 9'hv+ 's Uuvxvt hq 6ffyvph#v', Springer Verlag, 283-292.

....Smith (1996) This suggests that multiobjective optimizers that employ local search strategies would be promising to investigate and compare with population based methods. Good results have been obtained with such methods (Czyzak and Jaszkiewicz, 1998; Gandibleux et al. 1996; Hansen, 1997, 1998; Serafini, 1994; Ulungu et al. 1995) and, recently, some theoretical work has been done which yields convergence proofs for simple variants (Rudolph, 1998a, 1998b) However, c fl2000 by the Massachusetts Institute of Technology Evolutionary Computation 8(2) 149 172 J. Knowles and D. Corne it is currently ....

Serafini, P. (1994). Simulated annealing for multiple objective optimization problems. In Tzeng, G. H., Wang, H. F., Wen, V. P. and Yu, P. L., editors, Multiple Criteria Decision Making. Expand and Enrich the Domains of Thinking and Application, pages 289--292, Springer Verlag, Berlin, Germany.

....Smith (1996) This suggests that multiobjective optimizers which employ local search strategies would be promising to investigate and compare with population based methods. Good results have been obtained with such methods (Czyzak and Jaszkiewicz, 1998; Gandibleux et al. 1996; Hansen, 1996, 1997; Serafini, 1994; Ulungu et al. 1995) and recently some theoretical work has been done which yields convergence proofs for simple variants (Rudolph, 1998, 1998a) However, it is currently quite unclear how c fl1999 by the Massachusetts Institute of Technology Evolutionary Computation 7(3) 1 26 JOSHUA KNOWLES ....

Serafini, P. (1994). Simulated annealing for multiple objective optimization problems. In: Tzeng, G. H., Wang, H. F., Wen, V. P. and Yu, P. L., editors, Multiple Criteria Decision Making. Expand and Enrich the Domains of Thinking and Application, pages 289--292. Springer Verlag.

....the effective generation of approximations of the non dominated set. The methods are based on ideas of genetic algorithms (Schaffer, 1985, Fonseca and Fleming, 1993, Horn, Nafpliotis and Goldberg, 1994, Srinivas and Deb, 1995; see also Fonseca and Fleming, 1995, for a review) simulated annealing (Serafini, 1994, Ulungu et al. 1994 and Czyzak and Jaszkiewicz, 1995) or tabu search (Gandibleux et al. 1996, and Hansen, 1997) Authors of such methods usually state that the methods should generate good approximations of the non dominated set. The term good approximation , however, is often only defined ....

Serafini P. (1994). Simulated annealing for multiple objective optimization problems. In: Tzeng G.H., Wang H.F., Wen V.P., Yu P.L. (eds), Multiple Criteria Decision Making. Expand and Enrich the Domains of Thinking and Application, Springer Verlag, 283-292.

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