Parallel Search Algorithm Research Articles

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 per- formance, 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.

A Parallel Search Algorithm and Its Implementation for Digital k-Winners-Take-All Circuit

The k-Winners-Take-All (kWTA) is an operation to find the largest k (>1) inputs among N inputs. Parallel search algorithm of kWTA for digital inputs is not invented yet, so most of digital kWTA architectures have O(N) time complexity. A parallel search algorithm for digital kWTA operation and the circuits for its VLSI implementation are presented in this paper. The proposed kWTA architecture can compare all inputs simultaneously in parallel. The time complexity of the new architecture is O(logN), so that it is scalable to a large number of digital data. The high-speed kWTA operation and its O(logN) dependency of the new architecture are verified by simulations. It takes 290 ns in searching for 5 winners among 1024 of 32 bit data, which is more than thousands of times faster than existing digital kWTA circuits, as well as existing analog kWTA circuits.

A parallel neighborhood search for order acceptance and scheduling in flow shop environment

We consider the order acceptance and scheduling problem in a flow shop where the objective is to simultaneously minimize makespan and maximize total net revenue. We formulate the problem as a mixed integer linear programming model and develop an effective parallel neighborhood search algorithm. Two-string representation and three neighborhood structures are applied to generate new solutions. Parallelization is implemented by applying two independent searches and directly exchanging information between them. We assess the performance of the proposed method via computational experiments using an extensive set of instances. The experimental results show that the proposed method is highly effective and competitive when compared to other algorithms.

Solving dynamic memory allocation problems in embedded systems with parallel variable neighborhood search strategies

Embedded systems have become an essential part of our lives, thanks to their evolution in the recent years, but the main drawback is their power consumption. This paper is focused on improving the memory allocation of embedded systems to reduce their power consumption. We propose a parallel variable neighborhood search algorithm for the dynamic memory allocation problem, and compare it with the state of the art. Computational results and statistical tests applied show that the proposed algorithm produces significantly better outcomes than the previous algorithm in shorter computing time.

Symmetric Hadamard matrices of order 116 and 172 exist

Abstract We construct new symmetric Hadamard matrices of orders 92, 116, and 172. While the existence of those of order 92 was known since 1978, the orders 116 and 172 are new. Our construction is based on a recent new combinatorial array (GP array) discovered by N. A. Balonin and J. Seberry. For order 116 we used an adaptation of an algorithm for parallel collision search. The adaptation pertains to the modification of some aspects of the algorithm to make it suitable to solve a 3-way matching problem. We also point out that a new infinite series of symmetric Hadamard matrices arises by plugging into the GP array the matrices constructed by Xia, Xia, Seberry, and Wu in 2005.

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.

Sequential and parallel large neighborhood search algorithms for the periodic location routing problem

• We develop different sequential and parallel metaheuristics for the periodic location routing problem. • Computational results show that our algorithms outperform previous methods. • We also suggest a simple and effective parallelization strategy. We propose a large neighborhood search (LNS) algorithm to solve the periodic location routing problem (PLRP). The PLRP combines location and routing decisions over a planning horizon in which customers require visits according to a given frequency and the specific visit days can be chosen. We use parallelization strategies that can exploit the availability of multiple processors. The computational results show that the algorithms obtain better results than previous solution methods on a set of standard benchmark instances from the literature.

Improved packing of protein side chains with parallel ant colonies

IntroductionThe accurate packing of protein side chains is important for many computational biology problems, such as ab initio protein structure prediction, homology modelling, and protein design and ligand docking applications. Many of existing solutions are modelled as a computational optimisation problem. As well as the design of search algorithms, most solutions suffer from an inaccurate energy function for judging whether a prediction is good or bad. Even if the search has found the lowest energy, there is no certainty of obtaining the protein structures with correct side chains.MethodsWe present a side-chain modelling method, pacoPacker, which uses a parallel ant colony optimisation strategy based on sharing a single pheromone matrix. This parallel approach combines different sources of energy functions and generates protein side-chain conformations with the lowest energies jointly determined by the various energy functions. We further optimised the selected rotamers to construct subrotamer by rotamer minimisation, which reasonably improved the discreteness of the rotamer library.ResultsWe focused on improving the accuracy of side-chain conformation prediction. For a testing set of 442 proteins, 87.19% of and 77.11% of angles were predicted correctly within 40° of the X-ray positions. We compared the accuracy of pacoPacker with state-of-the-art methods, such as CIS-RR and SCWRL4. We analysed the results from different perspectives, in terms of protein chain and individual residues. In this comprehensive benchmark testing, 51.5% of proteins within a length of 400 amino acids predicted by pacoPacker were superior to the results of CIS-RR and SCWRL4 simultaneously. Finally, we also showed the advantage of using the subrotamers strategy. All results confirmed that our parallel approach is competitive to state-of-the-art solutions for packing side chains.ConclusionsThis parallel approach combines various sources of searching intelligence and energy functions to pack protein side chains. It provides a frame-work for combining different inaccuracy/usefulness objective functions by designing parallel heuristic search algorithms.

Sparse Detection With Integer Constraint Using Multipath Matching Pursuit

In this paper, we consider a detection problem of the underdetermined system when the input vector is sparse and its elements are chosen from a set of finite alphabets. This scenario is popular and embraces many of current and future wireless communication systems. We show that a simple modification of multipath matching pursuit (MMP), recently proposed parallel greedy search algorithm, is effective in recovering the discrete and sparse input signals. We also show that the addition of cross validation (CV) to the MMP algorithm is effective in identifying the sparsity level of input vector.

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.

Restoration of Motion Blurred Image Based on PSO Combine Wiener Filter in Ship Imaging System

Ship internal equipment vibration will cause the imaging system platform vibration, resulting in blurred images. Wiener Filter is often used to restore the motion blurred image. The principle of the method expects to minimize the mean square error between the restore image and original image. However, this method has some constrains, if parameter selection improper, it generates ringing effect easily. Usually, most users select parameter by rule of thumb, so they frequently fail to generate the optimal solution. In order to get high quality restore image, eliminate the ringing effect, a new approach based on particle swarm optimization (PSO) Wiener Filter was proposed, which automatically adjusts the parameter for Wiener Filter, this method seek the optimal solution by transferring information between individuals and information sharing, which is a highly efficient parallel search algorithm, insuring the accuracy of parameter selection, effectively reducing the ringing effect after image restoration, improve image quality of restoration.

Improvement of Lagrangian relaxation performance for open pit mines constrained long-term production scheduling problem

Constrained long-term production scheduling problem (CLTPSP) of open pit mines has been extensively studied in the past few decades due to its wide application in mining projects and the computational challenges it poses become an NP-hard problem. This problem has major practical significance because the effectiveness of the schedules obtained has strong economical impact for any mining project. Despite of the rapid theoretical and technical advances in this field, heuristics is still the only viable approach for large scale industrial applications. This work presents an approach combining genetic algorithms (GAs) and Lagrangian relaxation (LR) to optimally determine the CLTPSP of open pit mines. GAs are stochastic, parallel search algorithms based on the natural selection and the process of evolution. LR method is known for handling large-scale separable problems; however, the convergence to the optimal solution can be slow. The proposed Lagrangian relaxation and genetic algorithms (LR-GAs) combines genetic algorithms into Lagrangian relaxation method to update the Lagrangian multipliers. This approach leads to improve the performance of Lagrangian relaxation method in solving CLTPSP. Numerical results demonstrate that the LR method using GAs to improve its performance speeding up the convergence. Subsequently, highly near-optimal solution to the CLTPSP can be achieved by the LR-GAs.

A parallel optimisation approach for the realisation problem in intensity modulated radiotherapy treatment planning

We propose a parallel algorithm for computing exact solutions to the problem of minimizing the number of multileaf collimator apertures needed in step-and-shoot intensity modulated radiotherapy. These problems are very challenging particularly as the problem size increases. Here, we investigate how advanced parallel computing methods can be applied to these problems with a focus on the issues that are peculiar to parallel search algorithms and do not arise in their serial counterparts. A previous paper by the authors presented the MU---RD method for solving such problems using a serial constraint programming based search method. This method is being used as the starting point for a parallel implementation. The key challenges in creating a parallel implementation are ensuring that the CPUs are not starved of work and avoiding unnecessary computation due to the rearrangement of the search order in the parallel version. We show that efficient parallel optimisation is possible by dynamically changing the way work is split with potentially multiple tree search processes as well as parallel search of nodes. A weakly sorted queueing system is used to ensure appropriate prioritisation of tasks. Numerical results are presented to demonstrate the effectiveness of our algorithms in scaling from 8 to 64 CPUs.

Effective ACPS-Based Rescheduling of Parallel Batch Processing Machines with MapReduce

MapReduce is a highly efficient distributed and parallel computing framework, allowing users to readily manage large clusters in parallel computing. For Big data search problem in the distributed computing environment based on MapReduce architecture, in this paper we propose an Ant colony parallel search algorithm (ACPSMR) for Big data. It take advantage of the group intelligence of ant colony algorithm for global parallel search heuristic scheduling capabilities to solve problem of multi-task parallel batch scheduling with low efficiency in the MapReduce. And we extended HDFS design in MapReduce architecture, which make it to achieve effective integration with MapReduce. Then the algorithm can make the best of the scalability, high parallelism of MapReduce. The simulation experiment result shows that, the new algorithm can take advantages of cloud computing to get good efficiency when mining Big data.

Efficient sequential and parallel algorithms for planted motif search.

BackgroundMotif searching is an important step in the detection of rare events occurring in a set of DNA or protein sequences. One formulation of the problem is known as (l,d)-motif search or Planted Motif Search (PMS). In PMS we are given two integers l and d and n biological sequences. We want to find all sequences of length l that appear in each of the input sequences with at most d mismatches. The PMS problem is NP-complete. PMS algorithms are typically evaluated on certain instances considered challenging. Despite ample research in the area, a considerable performance gap exists because many state of the art algorithms have large runtimes even for moderately challenging instances.ResultsThis paper presents a fast exact parallel PMS algorithm called PMS8. PMS8 is the first algorithm to solve the challenging (l,d) instances (25,10) and (26,11). PMS8 is also efficient on instances with larger l and d such as (50,21). We include a comparison of PMS8 with several state of the art algorithms on multiple problem instances. This paper also presents necessary and sufficient conditions for 3 l-mers to have a common d-neighbor. The program is freely available at http://engr.uconn.edu/~man09004/PMS8/.ConclusionsWe present PMS8, an efficient exact algorithm for Planted Motif Search. PMS8 introduces novel ideas for generating common neighborhoods. We have also implemented a parallel version for this algorithm. PMS8 can solve instances not solved by any previous algorithms.

Amoeba-inspired algorithm for cognitive medium access

tug-of-war (TOW) is a unique parallel search algorithm for solving the multi-armed bandit problem (BP), which was inspired by the photoavoidance behavior of a single-celled amoeboid organism, the true slime mold Physarum polycephalum (1-4). The cognitive medium access (CMA) problem, which refers to multiuser channel allocations of the cognitive radio, can be interpreted as a multi-armed bandit problem (CBP) (5, 6). Unlike the normal BP, the CBP considers a competitive situation in which more than one user selects a channel whose reward probability (probability of which channel is free) varies depending on the number and combination of the selecting users as indicated in a payoff matrix. Depending on the payoff matrix, the CBP provides a hard problem instance in which the users should not be attracted to the Nash equilibrium to achieve the maximum, which is the most desirable state to obtain the maximum total score (throughput) for all the users. In this study, we propose two variants of the TOW model (solid type and liquid type) for the CBP toward developing a CMA protocol using a distributed control in uncertain environments. Using the minimum CBP cases where both the users choose a channel from the two considered channels, we show that the performance of our solid-type TOW model is better than that of the well-known upper confidence bound 1 (UCB1)-tuned algorithm, particularly for the hard problem instances. aim of this study is to explore how the users can achieve the social maximum in a decentralized manner. We also show that our liquid-type TOW model, which introduces direct interactions among the users for avoiding mutual collisions, makes it possible to achieve the social maximum for general CBP instances.

PMS6MC: A Multicore Algorithm for Motif Discovery.

We develop an efficient multicore algorithm, PMS6MC, for the (l, d)-motif discovery problem in which we are to find all strings of length l that appear in every string of a given set of strings with at most d mismatches. PMS6MC is based on PMS6, which is currently the fastest single-core algorithm for motif discovery in large instances. The speedup, relative to PMS6, attained by our multicore algorithm ranges from a high of 6.62 for the (17,6) challenging instances to a low of 2.75 for the (13,4) challenging instances on an Intel 6-core system. We estimate that PMS6MC is 2 to 4 times faster than other parallel algorithms for motif search on large instances.

Network motif detection: Algorithms, parallel and cloud computing, and related tools

Network motif is defined as a frequent and unique subgraph pattern in a network, and the search involves counting all the possible instances or listing all patterns, testing isomorphism known as NP-hard and large amounts of repeated processes for statistical evaluation. Although many efficient algorithms have been introduced, exhaustive search methods are still infeasible and feasible approximation methods are yet implausible. Additionally, the fast and continual growth of biological networks makes the problem more challenging. As a consequence, parallel algorithms have been developed and distributed computing has been tested in the cloud computing environment as well. In this paper, we survey current algorithms for network motif detection and existing software tools. Then, we show that some methods have been utilized for parallel network motif search algorithms with static or dynamic load balancing techniques. With the advent of cloud computing services, network motif search has been implemented with MapReduce in Hadoop Distributed File System (HDFS), and with Storm, but without statistical testing. In this paper, we survey network motif search algorithms in general, including existing parallel methods as well as cloud computing based search, and show the promising potentials for the cloud computing based motif search methods.

Research on Improved Code Phase Acquisition Algorithm for BOC(pn,n) Signal

Auto correlation function of BOC signal has the character of milti-values, so acquistion of BOC signal can not simply transplant the algorithm of GPS signal. For this reason, parallel code phase search algorithm and cross correlation characteristics of BOC signal are analyzed. And then, the validity of parallel code phase search algorithm on BOC signal are simulated. After that, point to the insufficiency of parallel code phase search algorithm on the acquisiton of BOC signal at low SNR(signal to noise ratio), an improved acquisition algorithm is presented and used to analyze BOC signal. The results show that the improved algorithm is applicable to BOC(pn, n) signal where p is an integer, which expands the application aera of parallel code phase search algorithm; and that the improved algorithm can improve SNR of acquisition about 15dB compared with that of parallel code phase search algorithm, that is to say, the improved algorithm can enhance acquisiton sensitivity of receivers obviously, which is very important to the design of acquisiton algorithm in receivers.

Heuristic Search for Large Problems with Real Costs

Heuristic search is a fundamental technique for solving problems in artificial intelligence. However, many heuristic search algorithms, such as A* are limited by the amount of main memory available. External memory search overcomes the memory limitation of A* by taking advantage of cheap secondary storage, such as disk. Previous work in this area assumes that edge costs fall within a narrow range of integer values and relies on uniformed search order. The goal of this dissertation research is to develop novel techniques that enable heuristic search algorithms to solve problems with real values using a best-first search order while exploiting external memory and multiple processors. This work will be organized into four components. The first component will discuss external memory search and present a novel technique for incorporating real-valued edge costs. The second component will present a novel algorithm for solving problems with large branching factors with application to the challenging problem of Multiple Sequence Alignment (MSA). The third component will cover bounded suboptimal external search for practical MSA applications. The final component of this research will be the development of a novel distributed search framework; allowing parallel and external memory heuristic search algorithms to run cooperatively on a commodity computing cluster. Together these four components will enable heuristic search to scale to large problems in practical settings while exploiting modern hardware.