Ishibuchi, H., Murata, T., 1996. Multi-objective genetic local search algorithm. Proceedings of 1996 IEEE International Conference on Evolutionary Computation (ICEC '96), May 20--22, Nagoya University, Japan, Piscataway, NJ IEEE Service Center, pp. 119--124.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....W E consider the following multiobjective programming problem: Minimize (1) where is a variable vector in a real and dimensional space, is the feasible solution space, and there are objective functions . Many real world decision problems can be formulated as the above problem (e.g. see [1] [3] Very often, the objective functions are noncommensurable and they cannot be optimized simultaneously, and the decision maker has to find a compromise solution. The notion of Pareto optimality is one of the major approaches to multiobjective programming [1] 7] For any two points and in ....

....as the above problem (e.g. see [1] 3] Very often, the objective functions are noncommensurable and they cannot be optimized simultaneously, and the decision maker has to find a compromise solution. The notion of Pareto optimality is one of the major approaches to multiobjective programming [1] [7] For any two points and in , if the following conditions hold: for all for some (2) Manuscript received June 18, 1999; revised August 7, 2000. This work was supported by the HKBU FRG under Research Grant FRG 98 99 II 62. Y. W. Leung is with the Department of Computer Science, Hong Kong ....

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H. Ishibuchi and T. Murata, "A multi-objective genetic local search algorithm and its application to flowshop scheduling," IEEE Trans. Syst., Man, Cybern. C, vol. 28, pp. 392--403, Aug. 1998.

....a child and is computationally expensive. Weighted aggregation method was an obvious extension to the classical single objective optimization formulation to handle multiobjective problems. The principle of weighted aggregation of objectives is reflected in the works of Ishibuchi and Murata[17] and Hajela and Lin[13] Ishibuchi and Murata[17] used a combination of a weighted sum based evolutionary algorithm [EA] and a local search algorithm while Hajela and Lin[13] employed a variable set of weights for objective aggregation along with sharing and mating restrictions. In both of these ....

....Weighted aggregation method was an obvious extension to the classical single objective optimization formulation to handle multiobjective problems. The principle of weighted aggregation of objectives is reflected in the works of Ishibuchi and Murata[17] and Hajela and Lin[13] Ishibuchi and Murata[17] used a combination of a weighted sum based evolutionary algorithm [EA] and a local search algorithm while Hajela and Lin[13] employed a variable set of weights for objective aggregation along with sharing and mating restrictions. In both of these methods, multiple objectives are transformed to a ....

Ishibuchi, H. and Murata, T.: Multiobjective genetic local search algorithm. Proceedings of

....(e.g. see [1] Different algorithms may give different sets of nondominated solutions to the same problem. To evaluate the quality of these algorithms, several quality measures have been used or proposed in the literature: 1. Number of function evaluations or CPU time required (e.g. see [2, 5, 6]) It measures the time complexity of a multiobjective programming algorithm. 2. Number of solutions found (e.g. see [2, 5, 6] It measures the number of solutions that can be found by a multiobjective programming algorithm. Minimize x W f 1 (x) f 2 (x) f M (x) 1) f 1 (x) f 2 ....

....the quality of these algorithms, several quality measures have been used or proposed in the literature: 1. Number of function evaluations or CPU time required (e.g. see [2, 5, 6] It measures the time complexity of a multiobjective programming algorithm. 2. Number of solutions found (e.g. see [2, 5, 6]) It measures the number of solutions that can be found by a multiobjective programming algorithm. Minimize x W f 1 (x) f 2 (x) f M (x) 1) f 1 (x) f 2 (x) f M (x) x W x 1 x 2 W f i (x 1 ) f i (x 2 ) for all i 1 , 2 , M f j (x 1 ) f j (x 2 ) for some j 1 , ....

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H. Ishibuchi, and T. Murata, "A multi-objective genetic local search algorithm and its application to flowshop scheduling," IEEE Trans. Syst. Man Cyber. C, vol. 28, no. 3, pp. 392-403, 1998.

....metaheuristics on the bi objective set covering problem. Nine 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 ....

....of HGAs is one of the most promising directions of research. Pareto memetic algorithm introduced in this paper has been developed on the basis of previously proposed multiple objective genetic local search (MOGLS) algorithm [14] 16] The general scheme of PMA, MOGLS, as well as of IMMOGLS [12], may be summarized by the algorithm presented in Figure 1. The three algorithms differ by the way in which solutions are drawn for recombination. In the case of MOGLS, a relatively small temporary elite population TE composed of a number (e.g. 10) of solutions being the best on the current ....

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Ishibuchi H. Murata T. (1998), Multi-Objective Genetic Local Search Algorithm and Its Application to Flowshop Scheduling, IEEE Transactions on Systems, Man and Cybernetics - Part C.' Applications and Reviews, 28, 3,392-403.

....find out the global solution. Therefore, we expect that a global optimization method can overcome some of the disadvantages of the conventional methods. In this paper, we will address this problem with a global multi objective optimization methods, Multi objective Genetic Local Search Algorithm [7]. This algorithm has good global and local search performance. We first introduce several objective functions, which reflect different goals of our reconstruction. In the process of optimization, the multiple objectives are simultaneously optimized. We can eventually get al..l the non dominated ....

....Although the genetic algorithm (GA) can be applied to the multi objective optimization problem because it keeps a population of solutions in the computation [1, 2, 3] but the classic GA [2, 3] has some disadvantages on local search performance. We will adopt a kind of hybrid algorithm proposed in [7] and tailor it to our problem. This algorithm has excellent global and local search performance. 3 Multi objective Decision Model For Image Reconstruction From Projections In this section, we propose several objective functions which are suitable for the image reconstruction and we cast the ....

H. Ishibuchi, T. Murata. A Multi-Objective Genetic Local Search Algorithm and Its Application to Flowshop Scheduling, IEEE Trans. Systems, Man, and Cybernetics, Part c: Applications and Reviews, vol.28, no.3, 1998.

....optimization. In particular, methods of this type were able to improve best known results on a number of well studied combinatorial optimization problems, e.g. Traveling Salesperson Problem [119] Graph Coloring Problem [41] and Quadratic Assignment Problem [160] Ishibuchi and Murata [75] were the first authors to propose a multiple objective genetic local search algorithm. The main idea of their method is to randomly generate a weight vector for each iteration. The weight vector is used in a scalarizing function. Originally they suggested the use of linear functions, but the ....

....and MOGLS. 9.1. The performance of multiple objective genetic local search on traveling salesperson problem The experiment described in this section is an updated version of the experiments reported in [81] and [85] Our MOGLS algorithm was compared with Ishibuchi s and Murata s MOGLS (IMMOGLS) [75] (see also section 3.3) Fonseca s and Fieming s Pareto ranking based multiple objective genetic algorithm [35] Pareto GA) and introduced in [81] new MOGLS algorithm based on the idea of MOSA method [170] MOSA like MOGLS) 9.1.1. Multiple objective symmetric traveling salesperson problem ....

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Ishibuchi H., Murata T. (1998), Multi-Objective Genetic Local Search Algorithm and Its Application to Flowshop Scheduling, IEEE Transactions on Systems, Man and Cybernetics, 28, 3, 392-403.

....most promising directions of research. In this paper, we use multiple objective genetic local search (MOGLS) algorithm proposed by the first author in [7] In [7] the algorithm was tested on multiple objective Travelling Salesperson Problem and outperformed MOGLS algorithm of Ishibuchi and Murata [6], MOGLS based on the idea of MOSA method proposed by Ulungu et al. 12] and Pareto ranking based evolutionary algorithm [3] proposed by Fonseca and Fleming. In [8] the algorithm outperformed Strength Pareto Approach (SPEA) algorithm [15] and Memetic Pareto Archive Evolution Strategy (M PAES) 9] ....

Ishibuchi H. Murata T. Multi-Objective Genetic Local Search Algorithm and Its Application to Flowship Scheduling. IEEE Transactions on Systems, Man and Cybernetics, 28, 3, 392-403, 1998.

....as well as Nagar et al. 1995] point out that reports of research into multiobjective industrial scheduling problems are relatively rare and that this avenue of research is promising. Since that time, a number of studies of multiobjective scheduling have been published. Murata Ishibuchi [1996] Ishibuchi Murata [1998], Cavalieri et Gaiardelli [1998] Fanti et al. 1998] Brandimarte [1999] et Santos et Dourado [1999] have worked with multiobjective genetic algorithms. Min et al. 1998] and Scheduling jobs in an Alcan aluminium foundry using ant colony ; M.Gravel, C.Gagn, W. Price 2 Kim et al. 1998] used ....

Ishibuchi H., Murata T. [1998], A multi-objective genetic local search algorithm and its application to flowshop scheduling, IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and reviews, 28, 3, 392-403.

....current best objective function value is inserted into the next generation. In multiobjective applications some fraction of the solutions along the current nondominated front must be passed on to the next generation (see Reed et al. 2001, Zitzler Thiele 1999, Parks Miller 1998, Bck 1996, and Ishibuchi Murata 1996 for a description of alternative multiobjective elitist strategies) Elitism has been shown to improve the performance and convergence of the GA in both single and multiple objective applications (Thierens et al. 1998, Zitzler et al. 2000, Reed et al. 2001) Population sizing using trial runs. ....

Ishibuchi, H. & Murata, T. (1996) Multi-objective genetic local search algorithm. In Proceedings of 1996 IEEE International Conference on Evolutionary Computation, pp. 119-124, IEEE: Piscataway, NJ.

....the rst issue, the main emphasis has been on using an external le that stores nondominated vectors found during the evolutionary process. These vectors are put back into the population at later generations (this can be seen as a form of elitism in the context of multiobjective optimization [6, 18]) Regarding the second issue, the main emphasis has been on using clustering techniques [2] or approaches based on geographical positioning of individuals in an adaptive grid [10] Also, some researchers have suggested the use of a distributed GA in which Pareto dominance is applied only to ....

Hisao Ishibuchi and Tadahiko Murata. MultiObjective Genetic Local Search Algorithm. In Toshio Fukuda and Takeshi Furuhashi, editors, Proceedings of the 1996 International Conference on Evolutionary Computation, pages 119{ 124, Nagoya, Japan, 1996. IEEE.

.... an EMOO technique (see for example [14, 6] The main emphasis has been on using an external le that stores nondominated vectors found during the evolutionary process which are reinserted later in the population (this can be seen as a form of elitism in the context of multiobjective optimization [10, 17, 22]) Following the same line of thought of this current research, we decided to develop an approach in which we would use a GA with a very small population size and a reinitialization process (the so called micro GA) combined with an external le to store nondominated vectors previously found. ....

Hisao Ishibuchi and Tadahiko Murata. Multi-Objective Genetic Local Search Algorithm. In Toshio Fukuda and Takeshi Furuhashi, editors, Proceedings of the 1996 International Conference on Evolutionary Computation, pages 119-124, Nagoya, Japan, 1996. IEEE.

....de facon pr ematur ee avant que des informations suffisantes soient disponibles. L autre probl eme avec cette approche est la d etermination des poids, sans avoir de connaissances sur le probl eme trait e. Plusieurs strat egies aveugles peuvent etre utilis ees pour g en erer les poids. Dans [43], les poids sont g en er es de facon al eatoire : w i = random i random 1 : randomn ; i = 1; 2; n o u les variables random i sont des entiers positifs. L ex ecution multiple de l algorithme avec des poids diff erents permet d obtenir plusieurs solutions efficaces. Cependant, elle ....

....jeu de poids qui est fix e suivant les priorit es des objectifs. ffl M etaheuristiques hybrides : r ecemment, des m etaheuristiques hybrides ont et e propos ees. Dans [88] l algorithme utilis e est le recuit simul e, mais les solutions initiales sont g en er ees par un algorithme glouton. Dans [43], un algorithme hybridant un AG et une recherche locale est utilis e. Dans l AG, chaque s election d une paire d individus se fait avec des poids diff erents. Une recherche locale est effectu ee a partir de l individu produit par l AG. La direction de la recherche est d etermin ee par les poids ....

[Article contains additional citation context not shown here]

H. Ishibuchi and T. Murata. A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, 28(3):392--403, Aug 1998.

....of GLS is one of the most promising directions of research. In this paper, we use multiple objective genetic local search (MOGLS) algorithm proposed by us in [11] The algorithm was tested on multiple objective Travelling Salesperson Problem and outperformed MOGLS algorithm of Ishibuchi and Murata [10], 3 MOGLS based on the idea of MOSA method proposed by Ulungu et al. 19] and Pareto ranking based evolutionary algorithm [3] proposed by Fonseca and Fleming. Below we describe main ideas of the method. The goal of multiple objective metaheuristics is to generate good approximations to the ....

Ishibuchi H. Murata T. Multi-Objective Genetic Local Search Algorithm and Its Application to Flowship Scheduling. # ############### ##### ##### # ########, , 3, 392-403, 1998.

.... an EMOO technique (see for example [11, 5] The main emphasis has been on using an external le that stores nondominated vectors found during the evolutionary process which are reinserted later in the population (this can be seen as a form of elitism in the context of multiobjective optimization [8, 13, 18]) Following the same line of thought of this current research, we decided to develop an approach in which we would use a GA with a very small population size and a reinitialization process (the so called micro GA) combined with an external le to store nondominated vectors previously found. ....

Hisao Ishibuchi and Tadahiko Murata. Multi-Objective Genetic Local Search Algorithm. In Toshio Fukuda and Takeshi Furuhashi, editors, Proceedings of the 1996 International Conference on Evolutionary Computation, pages 119-124, Nagoya, Japan, 1996. IEEE.

No context found.

Ishibuchi, H., and Murata, T.: A Multi-Objective Genetic Local Search Algorithm and Its Application to Flowshop Scheduling, IEEE Trans. on Systems, Man, and Cybernetics - Part C: Applications and Reviews 28 (1998) 392-403.

No context found.

H. Ishibuchi and T. Murata, "A multi-objective genetic local search algorithm and its application to flowshop scheduling," IEEE Trans. on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 28, no. 3, pp. 392-403, 1998.

No context found.

H. Ishibuchi and T. Murata, "Multi-objective genetic local search algorithm," Proc. of 3rd IEEE International Conference on Evolutionary Computation, pp. 119-124, 1996.

....in many studies (e.g. 14] 15] Such a hybrid algorithm is often referred to as a memetic algorithm. See Moscato [16] for an introduction to this field and [17] 19] for recent developments. The hybridization with local search for multiobjective optimization was first implemented in [20] [21] as a multiobjective genetic local search (MOGLS) algorithm where a scalar fitness function with random weights was used for the selection of parents and the local search for their offspring. Jaszkiewicz [22] improved the performance of the MOGLS by modifying its selection mechanism of parents. ....

....three steps (i.e. selection, crossover and mutation) for generating new solutions while local search uses a single step. 3 4 5 1 2 10 6 8 9 7 3 4 5 1 2 10 6 8 9 7 Fig. 1 An example of a new tour generated by a local search operation. 4We use some variants of the MOGLS in [20] [21] for multiobjective permutation flowshop scheduling. Flowshop is one of the most frequently studied scheduling problems in the literature (see [30] for an introduction to this field) Permutation flowshop scheduling is to find an optimal permutation of n jobs processed on m machines. Thus the size ....

[Article contains additional citation context not shown here]

H. Ishibuchi and T. Murata, "A multi-objective genetic local search algorithm and its -52application to flowshop scheduling," IEEE Trans. on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 28, no. 3, pp. 392-403, August 1998.

....problems in many studies (e.g. 14] 15] Such a hybrid algorithm is often referred to as a memetic algorithm. See Moscato [16] for an introduction to this field and [17] 19] for recent developments. The hybridization with local search for multiobjective optimization was first implemented in [20], 21] as a multiobjective genetic local search (MOGLS) algorithm where a scalar fitness function with random weights was used for the selection of parents and the local search for their offspring. Jaszkiewicz [22] improved the performance of the MOGLS by modifying its selection mechanism of ....

....problems and [25] for degree constrained multiobjective MST (minimum weight spanning tree) problems. In those studies, the M PAES was compared with the PAES, the MOGLS of Jaszkiewicz [22] and an EMO algorithm. In the above mentioned hybrid EMO algorithms (i.e. multiobjective memetic algorithms [20] [25] local search was applied to 3individuals in every generation. In some studies [26] 27] local search was applied to individuals only in the final generation. While Deb and Goel [26] used local search for decreasing the number of non dominated solutions (i.e. for decreasing the ....

[Article contains additional citation context not shown here]

H. Ishibuchi and T. Murata, "Multi-objective genetic local search algorithm," Proc. of 3rd IEEE International Conference on Evolutionary Computation, pp. 119-124, Nagoya, Japan, May 20-22, 1996.

No context found.

Ishibuchi, H., Murata, T., 1996. Multi-objective genetic local search algorithm. Proceedings of 1996 IEEE International Conference on Evolutionary Computation (ICEC '96), May 20--22, Nagoya University, Japan, Piscataway, NJ IEEE Service Center, pp. 119--124.

No context found.

H. Ishibuchi and T. Murata. Multi-objective genetic local search algorithm. In Proceedings of IEEE International Conference on Evolutionary Computation, pages 119--124, 1996.

No context found.

Hisao Ishibuchi and Tadahiko Murata. Multi-objective genetic local search algorithm. In Toshio Fukuda and Takeshi Furuhashi, editors, Proceedings of the 1996 International Conference on Evolutionary Computation, pages 119-124, Nagoya, Japan, 1996. IEEE.

No context found.

H. Ishibuchi and T. Murata, "Multi-objective genetic local search algorithm, " in Proc. IEEE Conf. Evolutionary Computation (ICEC '96), 1996, pp. 119--124.

No context found.

H. Ishibuchi and T. Murata, "Multi-objective genetic local search algorithm, " in Proc. 3rd ICEC, 1996, pp. 119--124.

No context found.

Hisao Ishibuchi and Tadahiko Murata. Multi-objective genetic local search algorithm. In Proceedings of 1996 IEEE International Conference on Evolutionary Computation (ICEC'96), pages 119--124, Piscataway, NJ. IEEE Press, (1996).

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