The Experts below are selected from a list of 57582 Experts worldwide ranked by ideXlab platform
Lin-yu Tseng - One of the best experts on this subject based on the ideXlab platform.
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a hybrid genetic Local Search Algorithm for the permutation flowshop scheduling problem
European Journal of Operational Research, 2009Co-Authors: Lin-yu TsengAbstract:Traditionally, the permutation flowshop scheduling problem (PFSP) was with the criterion of minimizing makespan. The permutation flowshop scheduling problem to minimize the total flowtime has attracted more attention from reSearchers in recent years. In this paper, a hybrid genetic Local Search Algorithm is proposed to solve this problem with each of both criteria. The proposed Algorithm hybridizes the genetic Algorithm and a novel Local Search scheme that combines two Local Search methods: the Insertion Search (IS) and the Insertion Search with Cut-and-Repair (ISCR). It employs the genetic Algorithm to do the global Search and two Local Search methods to do the Local Search. Two Local Search methods play different roles in the Search process. The Insertion Search is responsible for Searching a small neighborhood while the Insertion Search with Cut-and-Repair is responsible for Searching a large neighborhood. Furthermore, the orthogonal-array-based crossover operator is designed to enhance the GA's capability of intensification. The experimental results show the advantage of combining the two Local Search methods. The performance of the proposed hybrid genetic Algorithm is very competitive. For the PFSP with the total flowtime criterion, it improved 66 out of the 90 current best solutions reported in the literature in short-term Search and it also improved all the 20 current best solutions reported in the literature in long-term Search. For the PFSP with the makespan criterion, the proposed Algorithm also outperforms the other three methods recently reported in the literature.
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two phase genetic Local Search Algorithm for the multimode resource constrained project scheduling problem
IEEE Transactions on Evolutionary Computation, 2009Co-Authors: Lin-yu Tseng, Shihchieh ChenAbstract:In this paper, the resource-constrained project scheduling problem with multiple execution modes for each activity is explored. This paper aims to find a schedule of activities such that the makespan of the schedule is minimized subject to the precedence and resource constraints. We present a two-phase genetic Local Search Algorithm that combines the genetic Algorithm and the Local Search method to solve this problem. The first phase aims to Search globally for promising areas, and the second phase aims to Search more thoroughly in these promising areas. A set of elite solutions is collected during the first phase, and this set, which acts as the indication of promising areas, is utilized to construct the initial population of the second phase. By suitable applications of the mutation with a large mutation rate, the restart of the genetic Local Search Algorithm, and the collection of good solutions in the elite set, the strength of intensification and diversification can be properly adapted and the Search ability retained in a long term. Computational experiments were conducted on the standard sets of project instances, and the experimental results revealed that the proposed Algorithm was effective for both the short-term (with 5000 schedules being evaluated) and the long-term (with 50000 schedules being evaluated) Search in solving this problem.
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A Two-Phase Genetic Local Search Algorithm for Feedforward Neural Network Training
The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006Co-Authors: Lin-yu Tseng, Wen-ching ChenAbstract:In this work, a two-phase genetic Local Search Algorithm is proposed to train the connection weights of the feedforward neural networks. Various evolutionary Algorithms including evolution strategies, evolutionary programming, and genetic Algorithms had been proposed to train the weights and/or architectures of neural networks. But, most of them did not have an effective crossover operator. In the proposed Algorithm, an effective orthogonal array crossover operator was used. Two classes of architectures were adopted and the classification capability of these two neural network architectures trained by the proposed two-phase genetic Local Search Algorithm was shown by applying them to the n-bit parity problem.
Tadahiko Murata - One of the best experts on this subject based on the ideXlab platform.
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Local Search procedures in a multi-objective genetic Local Search Algorithm for scheduling problems
IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems Man and Cybernetics (Cat. No.99CH37028), 1999Co-Authors: Hisao Ishibuchi, Tadahiko MurataAbstract:We have already proposed a multi-objective genetic Local Search Algorithm for finding non-dominated solutions of multi-objective optimization problems (Ishibuchi and Murata 1998). In our hybrid Algorithm, a Local Search procedure is applied to each solution generated by genetic operations (i.e., selection, crossover, and mutation). Since our optimization problem involves multiple objectives, the application of the Local Search is not straightforward. We examine various methods for implementing Local Search procedures in our multi-objective genetic Local Search Algorithm. One method uses a weighted sum of multiple objectives as a scalar fitness function where weight values are randomly updated whenever a pair of parent solutions is selected. Such a fitness function is used in the Local Search as well as the selection of parent solutions. In a variant of this method, weight values for a solution in the Local Search are specified according to its location in the objective space. Another method uses an inequality relation between solutions based on multiple objectives when a Local Search procedure determines whether the current solution is to be replaced with a new solution. The performance of multi-objective genetic Local Search Algorithms with various Local Search procedures is examined by computer simulations on two-objective flowshop scheduling problems.
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a multi objective genetic Local Search Algorithm and its application to flowshop scheduling
Systems Man and Cybernetics, 1998Co-Authors: Hisao Ishibuchi, Tadahiko MurataAbstract:We propose a hybrid Algorithm for finding a set of nondominated solutions of a multi objective optimization problem. In the proposed Algorithm, a Local Search procedure is applied to each solution (i.e., each individual) generated by genetic operations. Our Algorithm uses a weighted sum of multiple objectives as a fitness function. The fitness function is utilized when a pair of parent solutions are selected for generating a new solution by crossover and mutation operations. A Local Search procedure is applied to the new solution to maximize its fitness value. One characteristic feature of our Algorithm is to randomly specify weight values whenever a pair of parent solutions are selected. That is, each selection (i.e., the selection of two parent solutions) is performed by a different weight vector. Another characteristic feature of our Algorithm is not to examine all neighborhood solutions of a current solution in the Local Search procedure. Only a small number of neighborhood solutions are examined to prevent the Local Search procedure from spending almost all available computation time in our Algorithm. High performance of our Algorithm is demonstrated by applying it to multi objective flowshop scheduling problems.
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multi objective genetic Local Search Algorithm
IEEE International Conference on Evolutionary Computation, 1996Co-Authors: Hisao Ishibuchi, Tadahiko MurataAbstract:Proposes a hybrid Algorithm for finding a set of non-dominated solutions of a multi-objective optimization problem. In the proposed Algorithm, a Local Search procedure is applied to each solution (i.e. to each individual) generated by genetic operations. The aim of the proposed Algorithm is not to determine a single final solution but to try to find all the non-dominated solutions of a multi-objective optimization problem. The choice of the final solution is left to the decision maker's preference. The high Searching ability of the proposed Algorithm is demonstrated by computer simulations on flowshop scheduling problems.
José Brandão - One of the best experts on this subject based on the ideXlab platform.
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a memory based iterated Local Search Algorithm for the multi depot open vehicle routing problem
European Journal of Operational Research, 2020Co-Authors: José BrandãoAbstract:Abstract The problem studied in this paper is the multi-depot open vehicle routing problem, which has the following two differences in relation to the classical vehicle routing problem: there are several depots; the vehicles do not return to the depot after delivering the goods to the customers, i.e., the end of the route is not the starting point. There are many practical applications for this problem, however the great majority of the studies have only addressed the open vehicle routing problem with a single depot. In this paper, we present an iterated Local Search Algorithm, in which the moves performed during the Local Search are recalled and this historical Search information is then used to define the moves executed inside the perturbation procedures. Therefore, it is recorded the number of times that each customer is moved during the Local Search. Since this information is continuously updated and changes in each iteration, the Search is driven to potentially better regions of the solution space, and increases the chance of avoiding cycling, even when using deterministic perturbations. The performance of this Algorithm was tested using a large set of benchmark problems and was compared with other Algorithms, and the results show that it is very competitive.
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iterated Local Search Algorithm with ejection chains for the open vehicle routing problem with time windows
Computers & Industrial Engineering, 2018Co-Authors: José BrandãoAbstract:Abstract The problem studied in this paper is the open vehicle routing problem with time windows. This problem is different from the better known vehicle routing problem with time windows because in the former the vehicles do not return to the distribution depot after delivering the goods to the customers. For solving this problem an iterated Local Search Algorithm was used, whose good results are mainly due to the kind of perturbations applied, in particular, ejection chains, and also to the use of elite solutions. The performance of this Algorithm is tested using a large set of benchmark problems, containing 418 instances in total. The solutions obtained show that it is competitive with the best Algorithms existing in the literature.
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A deterministic iterated Local Search Algorithm for the vehicle routing problem with backhauls
TOP, 2015Co-Authors: José BrandãoAbstract:The vehicle routing problem with backhauls is a variant of the classical capacitated vehicle routing problem. The difference is that it contains two distinct sets of customers: those who receive goods from the depot, who are called linehauls, and those who send goods to the depot, who are referred to as backhauls. In this paper, we describe a new deterministic iterated Local Search Algorithm, which is tested using a large number of benchmark problems chosen from the literature. These computational tests have proven that this Algorithm competes with the best known Algorithms in terms of the quality of the solutions and at the same time, it is simpler and faster.
Thomas Stutzle - One of the best experts on this subject based on the ideXlab platform.
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a non adaptive stochastic Local Search Algorithm for the chesc 2011 competition
Learning and Intelligent Optimization, 2012Co-Authors: Franco Mascia, Thomas StutzleAbstract:In this work, we present our submission for the Cross-domain Heuristic Search Challenge 2011. We implemented a stochastic Local Search Algorithm that consists of several Algorithm schemata that have been offline-tuned on four sample problem domains. The schemata are based on all families of low-level heuristics available in the framework used in the competition with the exception of crossover heuristics. Our Algorithm goes through an initial phase that filters dominated low-level heuristics, followed by an Algorithm schemata selection implemented in a race. The winning schema is run for the remaining computation time. Our Algorithm ranked seventh in the competition results. In this paper, we present the results obtained after a more careful tuning, and a different combination of Algorithm schemata included in the final Algorithm design. This improved version would rank fourth in the competition.
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adaptive sample size and importance sampling in estimation based Local Search for the probabilistic traveling salesman problem
European Journal of Operational Research, 2009Co-Authors: Prasanna Balaprakash, Thomas Stutzle, Mauro Birattari, Marco DorigoAbstract:The probabilistic traveling salesman problem is a paradigmatic example of a stochastic combinatorial optimization problem. For this problem, recently an estimation-based Local Search Algorithm using delta evaluation has been proposed. In this paper, we adopt two well-known variance reduction procedures in the estimation-based Local Search Algorithm: the first is an adaptive sampling procedure that selects the appropriate size of the sample to be used in Monte Carlo evaluation; the second is a procedure that adopts importance sampling to reduce the variance involved in the cost estimation. We investigate several possible strategies for applying these procedures to the given problem and we identify the most effective one. Experimental results show that a particular heuristic customization of the two procedures increases significantly the effectiveness of the estimation-based Local Search.
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hierarchical iterated Local Search for the quadratic assignment problem
Lecture Notes in Computer Science, 2009Co-Authors: Mohamed Saifullah Hussin, Thomas StutzleAbstract:Iterated Local Search is a stochastic Local Search (SLS) method that combines a perturbation step with an embedded Local Search Algorithm. In this article, we propose a new way of hybridizing iterated Local Search. It consists in using an iterated Local Search as the embedded Local Search Algorithm inside another iterated Local Search. This nesting of Local Searches and iterated Local Searches can be further iterated, leading to a hierarchy of iterated Local Searches. In this paper, we experimentally examine this idea applying it to the quadratic assignment problem. Experimental results on large, structured instances show that the hierarchical iterated Local Search can offer advantages over using a "flat" iterated Local Search and make it a promising technique to be further considered for other applications.
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reactive stochastic Local Search Algorithms for the genomic median problem
European conference on Evolutionary Computation in Combinatorial Optimization, 2008Co-Authors: Renaud Lenne, Thomas Stutzle, Christine Solnon, Eric Tannier, Mauro BirattariAbstract:The genomic median problem is an optimization problem inspired by a biological issue: it aims to find the chromosome organization of the common ancestor to multiple living species. It is formulated as the Search for a genome that minimizes a rearrangement distance measure among given genomes. Several attempts have been reported for solving this NP-hard problem. These range from simple heuristic methods to a stochastic Local Search Algorithm inspired by WalkSAT, a well-known Local Search Algorithm for the satisfiability problem in propositional logic. The main objective of this reSearch is to develop improved Algorithmic techniques for tackling the genomic median problem and to provide new state-of-the-art solutions. In particular, we have developed an Algorithm that is based on tabu Search and iterated Local Search and that shows high performance. To alleviate the dependence of the Algorithm performance on a single fixed parameter setting, we have included a reactive scheme that automatically adapts the tabu list length of the tabu Search part and the perturbation strength of the iterated Local Search part. In fact, computational results show that we have developed a new very high-performing stochastic Local Search Algorithm for the genomic median problem and we also have found a new best solution for a realworld case.
Hisao Ishibuchi - One of the best experts on this subject based on the ideXlab platform.
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Local Search procedures in a multi-objective genetic Local Search Algorithm for scheduling problems
IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems Man and Cybernetics (Cat. No.99CH37028), 1999Co-Authors: Hisao Ishibuchi, Tadahiko MurataAbstract:We have already proposed a multi-objective genetic Local Search Algorithm for finding non-dominated solutions of multi-objective optimization problems (Ishibuchi and Murata 1998). In our hybrid Algorithm, a Local Search procedure is applied to each solution generated by genetic operations (i.e., selection, crossover, and mutation). Since our optimization problem involves multiple objectives, the application of the Local Search is not straightforward. We examine various methods for implementing Local Search procedures in our multi-objective genetic Local Search Algorithm. One method uses a weighted sum of multiple objectives as a scalar fitness function where weight values are randomly updated whenever a pair of parent solutions is selected. Such a fitness function is used in the Local Search as well as the selection of parent solutions. In a variant of this method, weight values for a solution in the Local Search are specified according to its location in the objective space. Another method uses an inequality relation between solutions based on multiple objectives when a Local Search procedure determines whether the current solution is to be replaced with a new solution. The performance of multi-objective genetic Local Search Algorithms with various Local Search procedures is examined by computer simulations on two-objective flowshop scheduling problems.
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a multi objective genetic Local Search Algorithm and its application to flowshop scheduling
Systems Man and Cybernetics, 1998Co-Authors: Hisao Ishibuchi, Tadahiko MurataAbstract:We propose a hybrid Algorithm for finding a set of nondominated solutions of a multi objective optimization problem. In the proposed Algorithm, a Local Search procedure is applied to each solution (i.e., each individual) generated by genetic operations. Our Algorithm uses a weighted sum of multiple objectives as a fitness function. The fitness function is utilized when a pair of parent solutions are selected for generating a new solution by crossover and mutation operations. A Local Search procedure is applied to the new solution to maximize its fitness value. One characteristic feature of our Algorithm is to randomly specify weight values whenever a pair of parent solutions are selected. That is, each selection (i.e., the selection of two parent solutions) is performed by a different weight vector. Another characteristic feature of our Algorithm is not to examine all neighborhood solutions of a current solution in the Local Search procedure. Only a small number of neighborhood solutions are examined to prevent the Local Search procedure from spending almost all available computation time in our Algorithm. High performance of our Algorithm is demonstrated by applying it to multi objective flowshop scheduling problems.
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multi objective genetic Local Search Algorithm
IEEE International Conference on Evolutionary Computation, 1996Co-Authors: Hisao Ishibuchi, Tadahiko MurataAbstract:Proposes a hybrid Algorithm for finding a set of non-dominated solutions of a multi-objective optimization problem. In the proposed Algorithm, a Local Search procedure is applied to each solution (i.e. to each individual) generated by genetic operations. The aim of the proposed Algorithm is not to determine a single final solution but to try to find all the non-dominated solutions of a multi-objective optimization problem. The choice of the final solution is left to the decision maker's preference. The high Searching ability of the proposed Algorithm is demonstrated by computer simulations on flowshop scheduling problems.
