نتایج جستجو برای: simulated annealing and tabu search
تعداد نتایج: 16869672 فیلتر نتایج به سال:
In this paper simulated annealing and genetic algorithms are applied to the graph partitioning problem. These techniques mimic processes in statistical mechanics and biology, respectively, and are the most popular meta-heuristics or general-purpose optimization strategies. A hybrid algorithm for circuit partitioning, which uses tabu search to improve the simulated annealing meta-heuristics, is ...
Improved upper bounds are presented for K(n, r), the minimum cardinality of a binary code of length n and coveting radius r. The new bounds are obtained by both new and old constructions; in many of these, computer search using simulated annealing and tabu search plays a central role. Some new linear coveting codes are also presented. An updated table of upper bounds on K(n,r), n~<64, r~<12, is...
Job Shop Scheduling Problem (JSSP) is an optimization problem in which ideal jobs are assigned to resources at particular times. In recent years many attempts have been made at the solution of JSSP using a various range of tools and techniques such as Branch and Bound and Heuristics algorithms. This paper proposed a new algorithm based on Genetic Algorithm (GA), Tabu Search (TS) and Simulated A...
The objective of this study is to develop a tabu search (TS) procedure for the Extended Maximal Availability Location Problem (EMALP) and compare the results obtained with this procedure against those obtained with the Simulated Annealing (SA) procedure developed by Galvão et al. (2005) for the same problem. It is shown that in terms of quality the solutions SA outperforms TS for the smaller ne...
In this paper, we present a complex distribution network design problem in supply chain system which includes location and inventory decisions. Customers demand is generated randomly and each distribution center maintains a certain amount of safety stock in order to achieve a certain service level for the customers it services. Unlike most of past research, our model allows for multiple levels...
This research is focused on solving problems in the area of software project management using metaheuristic search algorithmsand as such is research in the field of search based software engineering. The main aim of this research is to evaluate the performance of different metaheuristic search techniques in resource allocation and scheduling problemsthat would be typical of software development...
In recent years, there has been a great deal of interest in metaheuristics in the optimization community. Tabu Search (TS) represents a popular class of metaheuristics. However, compared with other metaheuristics like genetic algorithm and simulated annealing, contributions of TS that deals with continuous problems are still very limited. In this paper, we introduce a continuous TS called Direc...
Three heuristic algorithms: simulated annealing, genetic algorithm, and Tabu search were compared to molecular docking procedure using 3 protein-ligand systems. Statistical analysis of the results indicated that the Tabu search showed the best performance in terms of locating solutions close to the crystallographic ligand conformation. From the comparisons, a hybrid search algorithm was propose...
In this paper we present the results of an investigation of the possibilities offered by three wellknown metaheuristic algorithms to solve the timetable problem, a multi-constrained, NP-hard, combinatorial optimization problem with real-world applications. First, we present our model of the problem, including the definition of a hierarchical structure for the objective function, and of the neig...
The purpose of this paper is to improve the simulated annealing method with a variable neighborhood search to solve the resource-constrained scheduling problem. We also compare numerically this method with other neighborhood search (local search) techniques: threshold accepting methods and tabu search. Furthermore, we combine these techniques with multistart diversification strategies and with ...
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