Parallel Tabu Search Research Articles

A Novel and Effective University Course Scheduler Using Adaptive Parallel Tabu Search and Simulated Annealing

A Novel and Effective University Course Scheduler Using Adaptive Parallel Tabu Search and Simulated Annealing

Joint Parallel Tabu Search Algorithm-Based Look-Up Table Design and Deep Learning-Based Signal Detection for OFDM-AIM

Joint Parallel Tabu Search Algorithm-Based Look-Up Table Design and Deep Learning-Based Signal Detection for OFDM-AIM

A new parallel tabu search algorithm for the optimization of the maximum vertex weight clique problem

SummaryThe efficiency of metaheuristic algorithms depends significantly on the number of fitness value evaluations performed on candidate solutions. In addition to various intelligent techniques used to obtain better results, parallelization of calculations can substantially improve the solutions in cases where the problem is NP‐hard and requires many evaluations. This study proposes a new parallel tabu search method for solving the Maximum Vertex Weight Clique Problem (MVWCP) on the Non‐Uniform Memory Access (NUMA) architectures using the OpenMP parallel programming paradigm. Achieving scalability in the NUMA architectures presents significant challenges due to the high complexity of their memory systems, which can lead to performance loss. However, our proposed Tabu‐NUMA algorithm provides up to speed‐up with 64 cores for ten basic problem instances in DIMACS‐W and BHOSLIB‐W benchmarks. And it improves the performance of the serial Multi Neighborhood Tabu Search (MN/TS) algorithm for 38 problem instances in DIMACS‐W and BHOSLIB‐W benchmarks. We further evaluate our algorithm on larger datasets with thousands of edges and vertices from Network Data Repository benchmark problem instances, and we report significant improvements in terms of speed up. Our results confirm that the Tabu‐NUMA algorithm is among the best recent algorithms for solving MVWCP on the NUMA architectures.

Implicit function characterization of the curvilinear mask to realize parametric optical proximity correction with a neighborhood parallel tabu search.

The curvilinear mask structures provide significant benefits in improving lithographic resolution. Curvilinear masks, as opposed to rectilinear masks, have a wider range of structure types that can be used precisely to correct the contour of diffraction at sharp technological nodes. However, the curvilinear structure also makes the inverse design of mask in optical proximity correction (OPC) flow difficult. The current OPC of curvilinear masks uses pixel-based inverse optimization, which is extremely computationally intensive and takes up a lot of design data storage space. This paper proposes an implicit function to represent a large number of curve types with a small number of parameters to reduce computational complexity and the R&D cycle. Therefore, the ultra-high dimensional pixel-based OPC problem is transformed into a low-dimensional parameter search problem in the critical diffraction area of the mask pattern. The tabu search algorithm and neighborhood parallel computing strategy are then used to quickly search for optimal characterized parameters. The results of the simulation show that the parametric curvilinear OPC method achieves a higher image fidelity than that of rectilinear OPC. At the same time, it addresses the shortcomings of the traditional pixelated curvilinear mask OPC method, including high computational complexity, low manufacturability, and storage space occupancy.

A novel differential evolution algorithm for tone reservation based peak to average power ratio reduction technique in orthogonal frequency division multiplexing systems

A critical problem in the Orthogonal Frequency Division Multiplexing (OFDM) framework for 5G communication is the high Peak to Average Power Ratio (PAPR) of the transmitted signal. The Tone Reservation (TR) scheme is employed to minimize the OFDM signal’s PAPR in time domain. It is comprised of some reserved subcarriers to generate a Peak Reduction Tone (PRT) set. The selection of these PRT set and the optimal target clipping level are the depending parameters of the PAPR reduction performance. A novel Differential Evolution based Tone Reservation (DE-TR) scheme is proposed to find an optimal PRT set and then the PAPR is reduced by employing an Optimized Iterative Clipping and Filtering (OICF) technique. Compared to other related techniques like Clipping Control Tone Reservation (CC-TR), Curve Fitting based Tone Reservation (CF-TR) and Parallel Tabu Search Tone Reservation (PTS-TR), the proposed technique DE-TR(Pro.) contributes to a substantial PAPR reduction output with lower computational complexity and maintaining an adequate bit error rate (BER) and spectral spreading efficiency. In addition, the theoretical relationship between the Error Vector Magnitude (EVM) and PAPR as well as CCDF performance with EVM are analyzed.

An Adaptive Penalty based Parallel Tabu Search for Constrained Covering Array Generation

The generation of the optimal constrained covering arrays is a key challenge in the research field of combinatorial testing, where a variety of Constrained Covering Array Generation (CCAG) algorithms have been developed. However, existing algorithms typically reuse constraint solver or forbidden tuple-based techniques to handle constraints, which might restrict their potentials on finding smaller arrays.

Parallel Algorithm with Blocks for a Single-Machine Total Weighted Tardiness Scheduling Problem

In this paper, the weighted tardiness single-machine scheduling problem is considered. To solve it an approximate (tabu search) algorithm, which works by improving the current solution by searching the neighborhood, is used. Methods of eliminating bad solutions from the neighborhood (the so-called block elimination properties) were also presented and implemented in the algorithm. Blocks allow a significant shortening of the process of searching the neighborhood generated by insert type moves. The designed parallel tabu search algorithm was implemented using the MPI (Message Passing Interface) library. The obtained speedups are very large (over 60,000×) and superlinear. This may be a sign that the parallel algorithm is superior to the sequential one as the sequential algorithm is not able to effectively search the solution space for the problem under consideration. Only the introduction of diversification process through parallelization can provide an adequate coverage of the entire search process. The current methods of parallelization of metaheuristics give a speedup which strongly depends on the problem’s instances, rarely greater than number of used parallel processors. The method proposed here allows the obtaining of huge speedup values (over 60,000×), but only when so-called blocks are used. The above-mentioned speedup values can be obtained on high performance computing infrastructures such as clusters with the use of MPI library.

An efficient GPU-based parallel tabu search algorithm for hardware/software co-design

Hardware/software partitioning is an essential step in hardware/software co-design. For large size problems, it is difficult to consider both solution quality and time. This paper presents an efficient GPU-based parallel tabu search algorithm (GPTS) for HW/SW partitioning. A single GPU kernel of compacting neighborhood is proposed to reduce the amount of GPU global memory accesses theoretically. A kernel fusion strategy is further proposed to reduce the amount of GPU global memory accesses of GPTS. To further minimize the transfer overhead of GPTS between CPU and GPU, an optimized transfer strategy for GPU-based tabu evaluation is proposed, which considers that all the candidates do not satisfy the given constraint. Experiments show that GPTS outperforms state-of-the-art work of tabu search and is competitive with other methods for HW/SW partitioning. The proposed parallelization is significant when considering the ordinary GPU platform.

Parallel Multiple Tabu Search for Multiobjective Urban Transit Scheduling Problem

Urban Transit Scheduling Problem (UTSP) is concerned with determining reliable transit schedules for buses and drivers by considering the preferences of both passengers and operators based on the demand and the set of transit routes. This paper considered a UTSP which consisted of frequency setting, timetabling, and simultaneous bus and driver scheduling. A mixed integer multiobjective model was constructed to optimize the frequency of the routes by minimizing the number of buses, passenger’s waiting times and overcrowding. The model was further extended by incorporating timeslots in determining the frequencies during peak and off-peak hours throughout the time period. The timetabling problem studied two different scenarios which reflected the preferences of passengers and operators to assign the bus departure times at the first and last stop of a route. A set covering model was then adopted to minimize the number of buses and drivers simultaneously. A parallel tabu search algorithm was proposed to solve the problem by modifying the initialization process and incorporating intensification and diversification approaches to guide the search effectively from the different feasible domain in finding optimal solutions with lesser computational effort. Computational experiments were conducted on the well-known Mandl’s and Mumford’s benchmark networks to assess the effectiveness of the proposed algorithm. Competitive results are reported based on the performance metrics, as compared to other algorithms from the literature.

Application of parallel tabu search in solving Ride-sharing problem with HOV Lanes

Ride-sharing can be defined as a system where travelers can share their vehicles and travel cost with others who have a similar destination and time schedule. Ride-sharing problem with High Occupancy Vehicle (HOV) Lanes is a problem of finding an optimal route to serve costumer demand where each demand and vehicle consists of origin and destination point, and some of the route are restricted-use freeways lanes reserved for vehicles with more than a predetermined number of occupants, which called HOV lanes. With the use of ride-sharing and HOV Lanes, it is expected to reduce the number of the vehicles thus will mitigate the congestion. The optimal solution is the one with minimum number of total distance, passenger’s ride time, and cost. This study discusses the application of insertion heuristic and parallel tabu search to optimize the use of Ride-sharing with HOV Lanes. Insertion heuristic is applied to obtain an initial solution and parallel tabu search algorithm is applied to improve the initial solution and obtain a better solution. For the case of eight vehicles and eight requests, from insertion heuristic we obtained an initial solution with an objective function value of 425 and with the used of parallel tabu search method we obtained a better solution with an objective function value of 420.

PAPR Reduction Based on Parallel Tabu Search for Tone Reservation in OFDM Systems

In this letter, we focus on the high peak to average power ratio (PAPR) reduction problem in tone reservation-based orthogonal frequency division multiplexing systems. We first propose a parallel Tabu search (PTS)-based scheme to find a sub-optimal peak reduction tone (PRT) set. After finding the sub-optimal PRT set, we apply it in an adaptive iterative clipping and filtering (AICF) method for PAPR reduction. Furthermore, PAPR reduction and bit error rate (BER) performances are compared among the AICF method, the adaptive scaling, the adaptive amplitude clipping and the fast iterative shrinkage-thresholding algorithm schemes. Simulation results verify that the PTS-based PRT scheme can obtain better secondary peaks with lower computational complexity, and the AICF scheme effectively reduces PAPR with a faster convergence speed while its BER performance is only slightly worse than for the existing methods.

Efficient parallel tabu search for the blocking job shop scheduling problem

The Blocking Job Shop Scheduling (BJSS) is an NP-hard scheduling problem. It is obtained from the classical job shop scheduling problem by replacing the infinite buffer capacity constraint by a zero buffer capacity which introduces the blocking constraint. This constraint affects deeply the ability of meta-heuristics to find good solutions due to the low ratio of feasible to explored solutions. In this paper, we discuss the parallelization of the Tabu Search algorithm (TS) which represents one of the most widely used heuristics. Applying the classical TS neighborhood to the BJSS problem produces infeasible solutions in 98% of cases which leads to waste a valuable time in exploring infeasible solutions. For this reason, the use of a feasibility recovery strategy is unavoidable; however, the recovery step slows down considerably the TS algorithm. Therefore, incurring a huge time to explore a small area in the search space. To overcome this drawback and to accelerate the TS algorithm, we propose in this paper parallel multi-start TS approaches where several processes explore simultaneously the search space. Our parallelization exploits a cluster-based architecture with 512 CPU-cores. The obtained results show the positive impact of our proposed parallelization on the solution quality. Moreover, combining both the parallelism and the recovery strategy allowed us to improve the best result in the literature for a large number of known benchmarks.

Prediction of relations between leaching rate of rare earth mineral and the density of its mother liquid based on HGEP-PTS algorithm

Aiming at the problem of insufficient computation efficiency of GEP algorithm in function funding, this study improves it based on parallel tabu search algorithm and applies it in rare earth column leaching experiment and discusses the influences of ammonium ion density, sulfite density and rare earth ion density on the leaching rate of weathered crust strain amass-type rare earth. It could be found from example analysis that the prediction error of improved gene expression programming model is increased 3 to 5 times of that of traditional gene expression programming and high-precision model of relations between rare earth leaching rate and the density parameters is obtained.

A robust and cooperative parallel tabu search algorithm for the maximum vertex weight clique problem

Abstract The maximum vertex weight clique problem (MVWCP) is a challenging NP-Hard combinatorial optimization problem that searches for a clique with maximum total sum of vertices’ weights. In this study, we propose a robust and cooperative parallel tabu search algorithm (PTC) for the MVWCP. Our proposed algorithm uses a dedicated tabu search algorithm with a multistart strategy for the diversification of search space on a parallel computation environment. An effective seeding mechanism is developed with respect to the rank of the processors to choose diversified starting points for a better exploration of the search space. Classical add, swap and drop operators of tabu search are improved with parallel computation and a combined neighborhood approach. The PTC algorithm is evaluated on a set of 120 problem instances from DIMACS-W and BHOSLIB-W benchmarks. Computational results show that the PTC algorithm competes with state-of-the-art heuristic algorithms by reporting average best (optimal) result hit ratios up to 99.0%.

A Tabu search based clustering algorithm and its parallel implementation on Spark

The well-known K-means clustering algorithm has been employed widely in different application domains ranging from data analytics to logistics applications. However, the K-means algorithm can be affected by factors such as the initial choice of centroids and can readily become trapped in a local optimum. In this paper, we propose an improved K-means clustering algorithm that is augmented by a Tabu Search strategy, and which is better adapted to meet the needs of big data applications. Our design focuses on enhancements to take advantage of parallel processing based on the Spark framework. Computational experiments demonstrate the superiority of our parallel Tabu Search based clustering algorithm over a widely used version of the K-means approach embodied in the parallel Spark MLlib system, comparing the algorithms in terms of scalability, accuracy, and effectiveness.

Parallel tabu search for the cyclic job shop scheduling problem

Abstract In this paper, we consider a cyclic job shop problem, consisting of production of a certain set of elements at fixed intervals. Optimization of the process is reduced to a minimization of a cycle time, i.e. the time, after which the next batch of the same elements may be produced. We introduce a new parallel method for the cost function calculation. The parallelization is not trivial and cannot be done automatically by the existing compilers due to the recurrent character of formulas. Since the problem is strongly NP-hard, a heuristic algorithm was designed to solve it. Computational experiments were done in a multiprocessor environment, namely – in Intel Xeon Phi.

Real-time route diversion control in a model predictive control framework with multiple objectives: Traffic efficiency, emission reduction and fuel economy

In this paper, the route recommendation provided by the traffic management authority, rather than the uncontrollable bifurcation splitting rate, is directly considered as the control variable in the route guidance system; a real-time en-route diversion control strategy with multiple objectives is designed in a Model Predictive Control (MPC) framework with regard to system uncertainties and disturbances. The objectives include not only traffic efficiency, but also emission reduction and fuel economy, which respectively correspond to minimizing the total time spent (TTS), total amount of emissions and fuel consumption for all vehicles moving through a network. In the MPC framework, the routing control problem is transformed to be a constrained combinational optimization, which is solved by the parallel Tabu Search algorithm. Two representative traffic scenarios are tested, and the simulation results show: (1) The room for improvement in each objective by means of route diversion control is not consistent with each other and varies with the utilized traffic scenario. In the peak hour, the routing control can lead to significant improvements in TTS and fuel economy, while a relatively small improvement in emission reduction is achieved; in the off-peak hour, however, it is opposite, which indicates that routing is possibly dispensable from the aspect of improving traffic efficiency, but is required from the aspect of emission reduction. (2) The conflict among the multiple objectives varies with the utilized traffic scenario in route diversion control. Improving traffic efficiency often conflicts with emission reduction in both scenarios. For the objectives of traffic efficiency and fuel economy, they are not conflicting in peak hour, while in the off-peak hour, the two objectives are likely conflicting, and the improvement in one objective can lead to the degradation in the other objective. (3) Regardless of the scenarios of peak hour or off-peak hour, the proposed control strategy can result in a proper trade-off among the three chosen objectives.

A parallel tabu search for solving the primal buffer allocation problem in serial production systems

This paper presents a novel parallel tabu search (PTS) algorithm equipped with a proper adaptive neighborhood generation mechanism to solve the primal buffer allocation problem, which consists of minimizing the total buffer capacity of a serial production system under a minimum throughput rate constraint. An evaluative method based on a specific algorithm has been implemented to simulate the system behavior. In order to validate the effectiveness of the proposed PTS a mixed integer linear programming-based simulation/optimization approach and several metaheuristics from the relevant literature have been implemented. Since most metaheuristics are sensitive to the parameter setting, a proper calibration analysis based on a non-parametric test has been performed. Then, a comprehensive comparison analysis, concerning with both quality of solutions and computational efficiency, has been carried out. Finally, through the numerical results obtained from PTS, a multi-factorial experimental analysis has been developed to analyze the influencing factors of the problem under investigation.

Jointly RVM Based Channel Estimation and PAPR Reduction Using Modified Tabu Search Algorithm in Power Line Communication Systems

Power line communication (PLC) channel as a transmitting medium for high speed data communication based on orthogonal frequency division multiplexing (OFDM) is demonstrated. High peak to average power ratio (PAPR) in OFDM, frequency selectivity and impulsive noise in PLC channel are the most important challenges that should be taken into consideration. In this paper, a new SFTS algorithm with low searching complexity is applied to popular PTS method to reduce the PAPR parameter in which there is no need to send any side information. Also, the robust relevance vector machine based channel estimation is jointly used with the PAPR reduction. Our proposed channel estimation algorithm is based on sparse Bayesian learning with a sigmoid function as a new multi-kernel with proper initial hyper-parameters. It improves BER and MSE performances in PLC channels contaminated by impulsive noises. Proposed jointly PAPR reduction and channel estimation without any side information achieves good results comparing to parallel Tabu search PAPR reduction and Huang channel estimation algorithms. It is shown that in our proposed PAPR reduction algorithm about 1.5 and 4.5 dB improvements are obtained with considerable searching complexity reduction comparing to the parallel Tabu search method and conventional OFDM, respectively. Also, about 2 dB improvement achieved in SNIR for BER = 10?3 of proposed channel estimation algorithm comparing to LS method and 5 dB enhancement in MSE comparing to Huang method.

A novel fill-time window minimisation problem and adaptive parallel tabu search algorithm in mail-order pharmacy automation system

This paper presents a novel fill-time window (FTW) problem in a mail-order pharmacy automation (MOPA) system. The MOPA system uses a batch process to fulfil and distribute tens of thousands of highly customised prescription orders. It has been utilised to accommodate an increasing prescription volume and pharmacy dispensing productivity. Since the majority of prescription orders consist of multiple medications, the long medications’ waiting time in the collation process will increase the makespan or even cause a production deadlock in extreme cases. To minimise the collation time of multiple medication orders, the FTW is defined as the time difference between the first and last dispensed medications within a prescription order and the FTW problem is introduced as a flexible order scheduling problem by considering makespan as a constraint. To minimise the FTW, an integer mathematical model has been developed to find an optimal production schedule. To solve this NP-hard order scheduling problem efficiently, an adaptive parallel tabu search (APTS) algorithm is proposed. The performance of the proposed algorithm has been experimented with different system parameters. Based on the experimental results, the APTS algorithm yields less FTW than LPT, and less FTW than TS.