Parallel Tabu Search Algorithm Research Articles

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.

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.

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%.

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.

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.

Solving the Resource Constrained Project Scheduling Problem using the parallel Tabu Search designed for the CUDA platform

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.

Optimization of integrated polarization filters.

This study reports on the design of small footprint, integrated polarization filters based on engineered photonic lattices. Using a rods-in-air lattice as a basis for a TE filter and a holes-in-slab lattice for the analogous TM filter, we are able to maximize the degree of polarization of the output beams up to 98% with a transmission efficiency greater than 75%. The proposed designs allow not only for logical polarization filtering, but can also be tailored to output an arbitrary transverse beam profile. The lattice configurations are found using a recently proposed parallel tabu search algorithm for combinatorial optimization problems in integrated photonics.

Multiobjective optimization in integrated photonics design

We propose the use of the parallel tabu search algorithm (PTS) to solve combinatorial inverse design problems in integrated photonics. To assess the potential of this algorithm, we consider the problem of beam shaping using a two-dimensional arrangement of dielectric scatterers. The performance of PTS is compared to one of the most widely used optimization algorithms in photonics design, the genetic algorithm (GA). We find that PTS can produce comparable or better solutions than the GA, while requiring less computation time and fewer adjustable parameters. For the coherent beam shaping problem as a case study, we demonstrate how PTS can tackle multiobjective optimization problems and represent a robust and efficient alternative to GA.

Parallel tabu search algorithm for the hybrid flow shop problem

The paper deals with the parallel variant of the scheduling algorithm dedicated to the hybrid flow shop problem. The problem derives from practice of automated manufacturing lines, e.g. for printed packages. The overall goal is to design a new algorithm which merges the performance of the best known sequential approach with the efficient exploitation of parallel calculation environments. In order to fulfill the above aim, there are two methods proposed in this paper: the original fast method of parallel calculation of the criterion function and the local neighborhood parallel search method embedded in the tabu search approach. The theoretical analysis, as well as the original implementation, with the use of vector processing instructions SSE2 supported by suitable data organization, are presented below. Numerical properties of the proposed algorithm are empirically verified on the multi-core processor.

Solving Multi-criteria Vehicle Routing Problem by Parallel Tabu Search on GPU

Transportation plays a crucial role in both production and service industry. In modern times, the importance of supplying the goods on time to warehouses, production units and finally to the customers is not lost on logistic companies. Vehicle Routing Problems (VRP) models evolved to be more advanced, which led to the growth of computational complexity. Optimizing transportation routes for companies means performing complex computations and doing so in the shortest possible amounts of time. Graphics Processing Units (GPUs) provide massive computation when the needed operations are properly parallelized. nVidia GPUs are equipped with Compute Unified Device Architecture (CUDA), so applying parallel algorithms is not lim- ited to complex workstations or specialized computers. This work emphasizes the value of using parallel Tabu Search (TS) algorithm over sequential TS algorithm and its application to multicriteria discrete optimization of Distance-constrained VRP.

Flexible job shop problem – parallel tabu search algorithm for multi-GPU

In the paper we propose a new framework for the distributed tabu search algorithm designed to be executed with the use of a multi-GPU cluster, in which cluster of nodes are equipped with multicore GPU computing units. The proposed methodology is designed specially to solve difficult discrete optimization problems, such as a flexible job shop scheduling problem, which we introduce as a case study used to analyze the efficiency of the designed synchronous algorithm.

A parallel multi-neighborhood cooperative tabu search for capacitated vehicle routing problems

This paper presents a parallel tabu search algorithm that utilizes several different neighborhood structures for solving the capacitated vehicle routing problem. Single neighborhood or neighborhood combinations are encapsulated in tabu search threads and they cooperate through a solution pool for the purpose of exploiting their joint power. The computational experiments on 32 large scale benchmark instances show that the proposed method is highly effective and competitive, providing new best solutions to four instances while the average deviation of all best solutions found from the collective best results reported in the literature is about 0.22%. We are also able to associate the beneficial use of special neighborhoods with some test instance characteristics and uncover some sources of the collective power of multi-neighborhood cooperation.

Partial Transmit Sequences for PAPR Reduction Using Parallel Tabu Search Algorithm in OFDM Systems

In this letter, partial transmit sequences (PTS) based on parallel tabu search (Parallel TS-PTS) scheme is proposed to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals. PTS is a distortionless PAPR reduction technique, but its high search complexity for finding optimal phase factors must be reduced for usable applications. Parallel TS-PTS is compared to different PTS schemes for PAPR reduction and search complexity performances. The simulation results show that the proposed parallel TS-PTS method provides good PAPR reduction and bit-error-rate (BER) performances.

A cooperative parallel tabu search algorithm for the quadratic assignment problem

In this study, we introduce a cooperative parallel tabu search algorithm (CPTS) for the quadratic assignment problem (QAP). The QAP is an NP-hard combinatorial optimization problem that is widely acknowledged to be computationally demanding. These characteristics make the QAP an ideal candidate for parallel solution techniques. CPTS is a cooperative parallel algorithm in which the processors exchange information throughout the run of the algorithm as opposed to independent concurrent search strategies that aggregate data only at the end of execution. CPTS accomplishes this cooperation by maintaining a global reference set which uses the information exchange to promote both intensification and strategic diversification in a parallel environment. This study demonstrates the benefits that may be obtained from parallel computing in terms of solution quality, computational time and algorithmic flexibility. A set of 41 test problems obtained from QAPLIB were used to analyze the quality of the CPTS algorithm. Additionally, we report results for 60 difficult new test instances. The CPTS algorithm is shown to provide good solution quality for all problems in acceptable computational times. Out of the 41 test instances obtained from QAPLIB, CPTS is shown to meet or exceed the average solution quality of many of the best sequential and parallel approaches from the literature on all but six problems, whereas no other leading method exhibits a performance that is superior to this.

A parallel tabu search algorithm for digital filter design

Purpose – The purpose of the paper is to present a novel design method for the optimal finite word length (FWL) finite impulse response (FIR) filters.Design/methodology/approach – The design method is based on a parallel tabu search (TS) algorithm which uses the crossover operator of the genetic algorithm.Findings – Three design examples have been presented to show that the proposed method can provide a good solution to the design problem of a FWL FIR filter. In order to show the validity of the proposed method, the performance of the suggested method has been compared to those of widely‐used other methods. From the comparison results, it was concluded that the proposed method can be efficiently used for the optimal FWL FIR filter design.Research limitations/implications – The number of examples can be increased and also the performance of the proposed method might be compared to other design methods, apart from those presented in this work, developed for the design of optimal FWL FIR filters.Practical im...