An improved migrating birds optimization (Imbo) algorithm is proposed to solve the hybrid flowshop scheduling problem with lot-streaming of random breakdown (RBHLFS) with the aim of minimizing the total flow time. To ...
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ISBN:
(数字)9783030954703
ISBN:
(纸本)9783030954703;9783030954697
An improved migrating birds optimization (Imbo) algorithm is proposed to solve the hybrid flowshop scheduling problem with lot-streaming of random breakdown (RBHLFS) with the aim of minimizing the total flow time. To ensure the diversity of the initial population, a Nawaz-Enscore-Ham (NEH) heuristic algorithm is used. A greedy algorithm is used to construct a combined neighborhood search structure. An effective local search procedure is utilized to explore potential promising neighborhoods. In addition, a reset mechanism is added to avoid falling into local optimum. Extensive experiments and comparisons demonstrate the feasibility and effectiveness of the proposed algorithm.
We present a simplified version of the threshold dynamics algorithm given in Esedoglu and Otto (Commun Pure Appl Math 68(5): 808-864, 2015). The new version still allows specifying (N 2) possibly distinct surface tens...
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We present a simplified version of the threshold dynamics algorithm given in Esedoglu and Otto (Commun Pure Appl Math 68(5): 808-864, 2015). The new version still allows specifying (N 2) possibly distinct surface tensions and (N 2) possibly distinct mobilities for a network with N phases, but achieves this level of generality without the use of retardation functions. Instead, it employs linear combinations of Gaussians in the convolution step of the algorithm. Convolutions with only two distinct Gaussians is enough for the entire network, maintaining the efficiency of the original thresholding scheme. We discuss stability and convergence of the new algorithm, including some counterexamples in which convergence fails. The apparently convergent cases include unequal surface tensions given by the Read and Shockley model and its three dimensional extensions, along with equal mobilities, that are a very common choice in computational materials science.
In this paper we consider two agents that compete on the use of a common processor. Each of the agents has a set of jobs that have to be processed on the same machine without preemption. Each of the agents wants to mi...
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In this paper we consider two agents that compete on the use of a common processor. Each of the agents has a set of jobs that have to be processed on the same machine without preemption. Each of the agents wants to minimize an objective function that depends on the completion time of its own jobs. In addition, each job has different release dates. In the presence of unequal release dates, it is sometimes advantageous to form a non-full batch, while in other situations it is a better strategy to wait for future job arrivals in order to increase the fullness of the batch. The objective is to find a schedule that performs well with respect to the objectives of both agents. To solve this difficulty problem, we construct a branch-and-bound solution scheme incorporating these bounds and some dominance rules for the optimal solution. In view of the advantage of combining local and global searches in the honey-bees optimization algorithm, we attempt to use a marriage in honey-bees optimization algorithm (mbo) to find near-optimal solutions. We conduct extensive computational experiments to evaluate the performance of the algorithms. (C) 2014 Elsevier Inc. All rights reserved.
Exhaustive testing of all possible combinations of input parameter values of a large system is impossible. Here, pairwise testing technique is often chosen owing to its effectiveness for bug detection. For pairwise te...
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ISBN:
(纸本)9781467382274
Exhaustive testing of all possible combinations of input parameter values of a large system is impossible. Here, pairwise testing technique is often chosen owing to its effectiveness for bug detection. For pairwise testing, test cases are designed to cover all possible pair combinations of input parameter values at least once. In this paper, we investigate the adoption of Migrating Birds Optimization (mbo) algorithm as a strategy to find an optimal solution for pairwise test data reduction. Two strategies have been proposed;the first strategy implements the basic mbo algorithm, called Pairwise mbo Strategy (PmboS) and the second strategy implements an improved Pairwise mbo strategy, called iPmboS. The iPmboS enhances the PmboS with multiple neighborhood structures and elitism. Based on the published benchmarking results, these two strategies offers competitive results with most existing strategies in terms of the generated test size. We also noted that iPmboS outperforms PmboS in several parameter configurations, especially when the test size generated is relatively small.
Partitioning and scheduling are two central issues in the design of embedded systems since they can widely influence the characteristics of the system under design. The numerous constraints imposed by the environment ...
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Partitioning and scheduling are two central issues in the design of embedded systems since they can widely influence the characteristics of the system under design. The numerous constraints imposed by the environment and/or the underlying target architecture of mixed systems (containing hardware and software parts) make these two problems hard to solve. This paper introduces an automatic approach that integrates simultaneously partitioning and scheduling. It is inspired by the collective behavior of social insects such as bees in order to find a feasible solution to partitioning, using scheduling to find the shortest execution time to the system under design. (c) 2006 Elsevier Inc. All rights reserved.
The "NP-Complete" class gathers very significant practical problems such as Sat, Max-Sat, partitioning There is not polynomial algorithm for the resolution of these problems. As a result, the interest in heu...
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ISBN:
(纸本)3540263195
The "NP-Complete" class gathers very significant practical problems such as Sat, Max-Sat, partitioning There is not polynomial algorithm for the resolution of these problems. As a result, the interest in heuristics and meta-heuristics is still growing. In this paper, we present a very recent metaheuristic introduced to solve a 3-sat problem. This metaheuristic can be classified as an evolutionary algorithm. It is based on the process of bees' reproduction. We adapted it for the resolution of the Max-Sat problem. We tested it on a medical benchmark obtained from a data-mining problem that we translated into a Max-Sat problem.
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