Parallel Search Algorithm Research Articles

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.

Efficient distributed quantum computing

We provide algorithms for efficiently moving and addressing quantum memory in parallel. These imply that the standard circuit model can be simulated with a low overhead by a more realistic model of a distributed quantum computer. As a result, the circuit model can be used by algorithm designers without worrying whether the underlying architecture supports the connectivity of the circuit. In addition, we apply our results to existing memory-intensive quantum algorithms. We present a parallel quantum search algorithm and improve the time–space trade-off for the element distinctness and collision finding problems.

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.

Improved algorithm for the isogeny problem for ordinary elliptic curves

A low storage algorithm for constructing isogenies between ordinary elliptic curves was proposed by Galbraith, Hess and Smart (GHS). We give an improvement of this algorithm by modifying the pseudorandom walk so that lower-degree isogenies are used more frequently. This is motivated by the fact that high degree isogenies are slower to compute than low degree ones. We analyse the running time of the parallel collision search algorithm when the partitioning is uneven. We also give experimental results. We conclude that our algorithm is around $$14$$ times faster than the GHS algorithm when constructing horizontal isogenies between random isogenous elliptic curves over a $$160$$ -bit prime field. The results apply to generic adding walks and the more general group action inverse problem; a speed-up is obtained whenever the cost of computing edges in the graph varies significantly.

Efficient Parallel Search Algorithm for Determining Optimal R=1/2 Systematic Convolutional Self-Doubly Orthogonal Codes

A novel parallel and implicitly-exhaustive search algorithm for finding, in systematic form, rate R=1/2 optimal-span Convolutional Self-Doubly Orthogonal (CDO) codes and Simplified Convolutional Self-Doubly Orthogonal (S-CDO) codes is presented. In order to obtain high-performance low-latency codecs with these codes, it is important to minimize their constraint length (or "span") for a given J number of generator connections. The proposed exhaustive algorithm uses algorithmic enhancements over the best previously published searching techniques, yielding new and improved codes: we were able to obtain new optimal-span CDO/S-CDO codes (having order J∈{9} and J∈{10,11} respectively), as well as new codes having the shortest spans published to date for higher values of J (J∈{10,12,...,17} and J∈{12,...,20} for CDO and S-CDO codes respectively). The new codes and their error performance are provided. An analysis of the evolution of the CDO/S-CDO code error performance as J increases is presented, and the shortest CDO/S-CDO code span values for each given J are compared.

Search Algorithms for Conceptual Graph Databases

We consider a database composed of a set of conceptual graphs. Using conceptual graphs and graph homomorphism it is possible to build a basic query-answering mechanism based on semantic search. Graph homomorphism defines a partial order over conceptual graphs. Since graph homomorphism checking is an NP-Complete problem, the main requirement for database organizing and managing algorithms is to reduce the number of homomorphism checks. Searching is a basic operation for database manipulating problems. We consider the problem of searching for an element in a partially ordered set. The goal is to minimize the number of queries required to find a target element in the worst case. First we analyse conceptual graph database operations. Then we propose a new algorithm for a subclass of lattices. Finally, we suggest a parallel search algorithm for a general poset.

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.

Dealing with hardware heterogeneity: a new parallel search model

In this article we present Ethane, a parallel heterogeneous metaheuristic model specifically designed for its execution on heterogeneous hardware environments. With Ethane we propose a hybrid parallel search algorithm inspired in the structure of the chemical compound of the same name, implementing a heterogeneous island model based in the structure of the chemical bonds of the ethane compound. Here we also shape a schema for describing a complete family of parallel heterogeneous metaheuristics inspired by the structure of hydrocarbons in nature, HydroCM (HydroCarbon inspired Metaheuristics), establishing a resemblance between atoms and computers, and between chemical bonds and communication links. Our goal is to gracefully match computers of different computing power to algorithms of different behavior (genetic algorithm and simulated annealing in this study), all them collaborating to solve the same problem. In addition to the nice natural metaphor we will show that Ethane, though simple, can solve search problems in a faster and more robust way than well-known panmictic and distributed algorithms very popular in the literature, as well as can achieve a better exploration/exploitation balance during the search process.

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.

Parallel variable neighborhood search for solving fuzzy multi-objective dynamic facility layout problem

In spite of the classic approaches of solution of dynamic facility layout problem, which only material handling and rearrangement costs are considered as objective function, these problems are the multi-objective problems. In this paper, a mixed integer linear programming formulation is presented for multi-objective dynamic facility layout problem concerning flexible bay structure. In addition, three current objectives in dynamic facility layout problems including minimizing material handling and rearrangement costs, maximizing adjacency rate, and minimizing shape ratio difference have been considered. Also, for solving this problem, two methods including the GAMS software and proposed parallel variable neighborhood search (PVNS) algorithm are used. So, it is worth mentioning that four test problems are solved by them, and the results show that the proposed PVNS algorithm is more efficient than the GAMS software.

Hardware implementation and validation of the fast variable block size motion estimation architecture for H.264/AVC

Block matching motion estimation is the heart of video coding system. It leads to a high compression ratio, whereas it is time consuming and calculation intensive. Many fast search block matching motion estimation algorithms have been developed in order to minimize search positions and speed up computation but they do not take into account how they can be effectively implemented by hardware. In this paper, we propose an efficient hardware architecture of the fast line diamond parallel search (LDPS) algorithm with variable block size motion estimation (VBSME) for H.264/AVC video coding system. The design is described in VHDL language, synthesized to Altera Stratix III FPGA and to TSMC 0.18μm standard-cells. The throughput of the hardware architecture reaches a processing rate up to 78 millions of pixels per second at 83.5MHz frequency clock and uses only 28kgates when mapped to standard-cells. Finally, a system on a programmable chip (SoPC) implementation and validation of the proposed design as an IP core is presented using the embedded video system.

Efficient Search Algorithm for Determining Optimal R=1/2 Systematic Convolutional Self-Doubly Orthogonal Codes

A novel parallel and implicitly-exhaustive search algorithm for finding, in systematic form, rate R=\frac{1}{2} optimal-span Convolutional Self-Doubly Orthogonal (CDO) codes and Simplified Convolutional Self-Doubly Orthogonal (S-CDO) codes is presented. In order to obtain high-performance low-latency codecs with these codes, it is important to minimize their constraint length (or span) for a given J number of generator connections. The proposed exhaustive algorithm uses algorithmic enhancements over the best previously published searching techniques, yielding new and improved codes: we were able to obtain new optimal-span CDO/S-CDO codes (having order J∈{9} and J∈{10,11} respectively), as well as new codes having the shortest spans published to date for higher values of J (J∈{10,12,...,17} and J∈{12,...,20} for CDO and S-CDO codes respectively). The new codes and their error performance are provided. An analysis of the evolution of the CDO/S-CDO code error performance as J increases is presented, and the shortest CDO/S-CDO code span values for each given J are compared.

Parallel search for maximum satisfiability

The predominance of multicore processors has increased the interest in developing parallel Boolean Satisfiability (SAT) solvers. As a result, more parallel SAT solvers are emerging. Even though parallel approaches are known to boost performance, parallel approaches developed for Boolean optimization are scarce. This paper proposes parallel search algorithms for Maximum Satisfiability (MaxSAT) and introduces PWBO, a new parallel solver for partial MaxSAT. Using two threads, an unsatisfiability-based algorithm is used to search on the lower bound of the optimal solution, while at the same time a linear search is performed on the upper bound of the optimal solution. Moreover, learned clauses are shared between threads during the search. For a larger number of threads two different strategies are proposed. The first strategy performs a portfolio approach by searching on the lower and upper bound values of the optimal solution using different encodings of cardinality constraints for each thread. The second strategy splits the search space considering different upper bound values of the optimal solution for each thread. Experimental results show that the techniques proposed in the paper enable PWBO to improve when increasing the number of threads.

Exploiting run time distributions to compare sequential and parallel stochastic local search algorithms

Run time distributions or time-to-target plots are very useful tools to characterize the running times of stochastic algorithms for combinatorial optimization. We further explore run time distributions and describe a new tool to compare two algorithms based on stochastic local search. For the case where the running times of both algorithms fit exponential distributions, we derive a closed form index that gives the probability that one of them finds a solution at least as good as a given target value in a smaller computation time than the other. This result is extended to the case of general run time distributions and a numerical iterative procedure is described for the computation of the above probability value. Numerical examples illustrate the application of this tool in the comparison of different sequential and parallel algorithms for a number of distinct problems.

Parallel Scatter Search Algorithms for Exam Timetabling

University exam timetabling refers to scheduling exams into predefined days, time periods and rooms, given a set of constraints. Exam timetabling is a computationally intractable optimization problem, which requires heuristic techniques for producing adequate solutions within reasonable execution time. For large numbers of exams and students, sequential algorithms are likely to be time consuming. This paper presents parallel scatter search meta-heuristic algorithms for producing good sub-optimal exam timetables in a reasonable time. Scatter search is a population-based approach that generates solutions over a number of iterations and aims to combine diversification and search intensification. The authors propose parallel scatter search algorithms that are based on distributing the population of candidate solutions over a number of processors in a PC cluster environment. The main components of scatter search are computed in parallel and efficient communication techniques are employed. Empirical results show that the proposed parallel scatter search algorithms yield good speed-up. Also, they show that parallel scatter search algorithms improve solution quality because they explore larger parts of the search space within reasonable time, in contrast with the sequential algorithm.

Parallel algorithms for finding cliques in a graph

A clique is a subgraph in a graph that is complete in the sense that each two of its nodes are connected by an edge. Finding cliques in a given graph is an important procedure in discrete mathematical modeling. The paper will show how concepts such as splitting partitions, quasi coloring, node and edge dominance are related to clique search problems. In particular we will discuss the connection with parallel clique search algorithms. These concepts also suggest practical guide lines to inspect a given graph before starting a large scale search.

A heterogeneous cooperative parallel search of branch-and-bound method and tabu search algorithm

In this study, we present a heterogeneous cooperative parallel search that integrates branch-and-bound method and tabu search algorithm. These two algorithms perform searches in parallel and cooperate by asynchronously exchanging information about the best solutions found and new initial solutions for tabu search. The rapid production of a good solution from the tabu search process provides the branch-and-bound process with a better feasible solution to accelerate the elimination of subproblems that do not contain an optimal solution. The new initial solution produced from the subproblem with a least-cost lower bound of the branch-and-bound method suggests the best potential area for tabu search to explore. We use a master-slave model to reduce the complexity of communication and enhance the performance of data exchange. A branch-and-bound process is used as the master process to control the exchange of information and the termination of computation. Several tabu search processes are executed simultaneously as the slave processes and cooperate by asynchronously exchanging information on the best solutions found and the new initial solutions by the master process of branch-and-bound. Based on the computation experiments of solving traveling salesman problems (TSP), the proposed heterogeneous parallel search algorithm outperforms a conventional parallel branch-and-bound method and a conventional parallel tabu search. We also present the computational results showing the efficiency of heterogeneous cooperative parallel search when we use more processors to accelerate search time. Thus, the proposed heterogeneous parallel search algorithm achieves linear accelerations.

Performance Improvement in Large Graph Algorithms on GPU using CUDA: an Overview

The basic operations on the graphs with millions of vertices are common in various applications. To have faster execution of such operations is very essential to reduce overall computation time. Today’s Graphics processing units (GPUs) have high computation power and low price. This device can be treated as an array of Single Instruction Multiple Data (SIMD) processors using CUDA software interface by Nvidia. Massively Multithreaded architecture of a CUDA device makes various threads to run in parallel and hence making optimum use of available computation power of GPU. In case of graph algorithms, vertices of the graphs are processed in parallel by mapping them to various threads on device. By making thousands of threads to run in parallel, computation time required for these algorithms is drastically decreased as compared to their CPU implementation. We studied different parallel algorithms for Breadth first search, all pairs shortest path that are carried out on GPU using CUDA and make their comparative study with respect to execution time, data structure used, input data etc. In the paper, we presented overview of various parallel methods carried out on GPU using its multithreaded architecture for BFS, APSP by various authors.

Stressing Search with Scenarios for Flexible Solutions to Real-Time Task Allocation Problems

One of the most important properties of a good software engineering process and of the design of the software it produces is robustness to changing requirements. Scenario-based analysis is a popular method for improving the flexibility of software architectures. This paper demonstrates a search-based technique for automating scenario-based analysis in the software architecture deployment view. Specifically, a novel parallel simulated annealing search algorithm is applied to the real-time task allocation problem to find baseline solutions which require a minimal number of changes in order to meet the requirements of potential upgrade scenarios. Another simulated annealing-based search is used for finding a solution that is similar to an existing baseline when new requirements arise. Solutions generated using a variety of scenarios are judged by how well they respond to different system requirements changes. The evaluation is performed on a set of problems with a controlled set of different characteristics.

Parametric Optimization Design of Aircraft Based on Hybrid Parallel Multi-objective Tabu Search Algorithm

For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search (HPMOTS) algorithm is used. First, a new multi-objective tabu search (MOTS) algorithm is proposed. Comparing with the traditional MOTS algorithm, this proposed algorithm adds some new methods such as the combination of MOTS algorithm and “Pareto solution”, the strategy of “searching from many directions” and the reservation of good solutions. Second, this article also proposes the improved parallel multi-objective tabu search (PMOTS) algorithm. Finally, a new hybrid algorithm—HPMOTS algorithm which combines the PMOTS algorithm with the non-dominated sorting-based multi-objective genetic algorithm (NSGA) is presented. The computing results of these algorithms are compared with each other and it is shown that the optimal result can be obtained by the HPMOTS algorithm and the computing result of the PMOTS algorithm is better than that of MOTS algorithm.