The Resource Constrained Project Scheduling Problem, which is considered to be difficult to tackle even for small instances, is a well-known scheduling problem in the operations research domain. To solve the problem we have proposed a parallel Tabu Search algorithm to find high quality solutions in a reasonable time. We show that our parallel Tabu Search algorithm for graphics cards (GPUs) outperforms other existing Tabu Search approaches in terms of quality of solutions and the number of evaluated schedules per second. Moreover, the algorithm for graphics cards is about 10.5 42.7 times faster (J90 benchmark instances) than the optimized parallel sequential algorithm for the Central Processing Unit (CPU). The same quality of solutions is achieved up to 5.4 22 times faster in comparison to the parallel sequential CPU algorithm respectively. The advantages of the GPU version arise from the sophisticated data-structures and their suitable placement in the device memory, tailor-made methods, and last but not least the effective communication scheme. The parallel Tabu Search for the Resource Constrained Project Scheduling Problem.To accelerate the algorithm the CUDA optimized version was implemented.A new resources evaluation algorithm was proposed.Promising results in terms of performance and quality of solutions.
From the Tabu Search parallelization point of view @cite_20 proposed a sophisticated solution. The authors use a circular buffer where the size of the buffer is equivalent to the number of the started threads (often the number of CPUs cores). Every location (i.e. an index of a thread) has different parameters (a tabu tenure, stopping criteria). At the beginning of the search each thread initializes its own location by a short TS operator, i.e. a modified version of the Taillard's robust tabu search. Then the asynchronous parallel tabu search is started. Every thread independently reads a solution and parameters from the location, possibly makes a diversification, runs the TS operator on the solution, and writes back and sets an UPDATE flag if an improving solution is found. After that the thread location index is circularly incremented. Diversification takes place if the read solution does not have the UPDATE flag set. Every best global solution is copied to half of the locations of the circular buffer to propagate elite solutions. Since the circular buffer is shared by many threads, the access has to be as short as possible and the locations have to be protected by critical sections.
@cite_20
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Tabu search techniques are used for moving step by step towards the minimum value of a function. A tabu list of forbidden movements is updated during the iterations to avoid cycling and being trapped in local minima. Such techniques are adapted to graph coloring problems. We show that they provide almost optimal colorings of graphs having up to 1000 nodes and their efficiency is shown to be significantly superior to the famous simulated annealing. The introduction of NVidia's powerful Tesla GPU hardware and Compute Unified Device Architecture (CUDA) platform enable many-core parallel programming. As a result, existing algorithms implemented on a GPU can run many times faster than on modern CPUs. Relatively little research has been done so far on GPU implementations of discrete optimisation algorithms. In this paper, two approaches to parallel GPU evaluation of the Permutation Flowshop Scheduling Problem, with makespan and total flowtime criteria, are proposed. These methods can be employed in most population-based algorithms, e.g. genetic algorithms, Ant Colony Optimisation, Particle Swarm Optimisation, and Tabu Search. Extensive computational experiments, on Tabu Search for Flowshop with both criteria, followed by statistical analysis, confirm great computational capabilities of GPU hardware. A GPU implementation of Tabu Search runs up to 89 times faster than its CPU counterpart. Reducing the dimensionality of datasets is a fundamental step in the task of building a classification model. Feature selection is the process of selecting a smaller subset of features from the original one in order to enhance the performance of the classification model. The problem is known to be NP-hard, and despite the existence of several algorithms there is not one that outperforms the others in all scenarios. Due to the complexity of the problem usually feature selection algorithms have to compromise the quality of their solutions in order to execute in a practicable amount of time. Parallel computing techniques emerge as a potential solution to tackle this problem. There are several approaches that already execute feature selection in parallel resorting to synchronous models. These are preferred due to their simplicity and capability to use with any feature selection algorithm. However, synchronous models implement pausing points during the execution flow, which decrease the parallel performance. In this paper, we discuss the challenges of executing feature selection algorithms in parallel using asynchronous models, and present a feature selection algorithm that favours these models. Furthermore, we present two strategies for an asynchronous parallel execution not only of our algorithm but of any other feature selection approach. The first strategy solves the problem using the distributed memory paradigm, while the second exploits the use of shared memory. We evaluate the parallel performance of our strategies using up to 32 cores. The results show near linear speedups for both strategies, with the shared memory strategy outperforming the distributed one. Additionally, we provide an example of adapting our strategies to execute the Sequential forward Search asynchronously. We further test this version versus a synchronous one. Our results revealed that, by using an asynchronous strategy, we are able to save an average of 7.5 of the execution time. The cell planning problem with capacity expansion is examined in wireless communications. The problem decides the location and capacity of each new base station to cover expanded and increased traffic demand. The objective is to minimize the cost of new base stations. The coverage by the new and existing base stations is constrained to satisfy a proper portion of traffic demands. The received signal power at the base station also has to meet the receiver sensitivity. The cell planning is formulated as an integer linear programming problem and solved by a tabu search algorithm. In the tabu search intensification by add and drop move is implemented by short-term memory embodied by two tabu lists. Diversification is designed to investigate proper capacities of new base stations and to restart the tabu search from new base station locations. Computational results show that the proposed tabu search is highly effective. A 10 cost reduction is obtained by the diversification strategies. The gap from the optimal solutions is approximately 1 spl sim 5 in problems that can be handled in appropriate time limits. The proposed tabu search also outperforms the parallel genetic algorithm. The cost reduction by the tabu search approaches 10 spl sim 20 in problems: with 2500 traffic demand areas (TDAs) in code division multiple access (CDMA).