scholarly journals A MODIFIED A* ALGORITHM FOR ALLOCATING TASK IN HETEROGENEOUS DISTIRBUTED COMPUTING SYSTEMS

2014 ◽  
pp. 50-57
Author(s):  
Nirmeen A. Bahnasawy ◽  
Gamal M. Attiya ◽  
Mervat Mosa ◽  
Magdy A. Koutb
Keyword(s):  
Large Scale ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Task Assignment ◽  
Turnaround Time ◽  
Sequential Search ◽  
A Algorithm ◽  
Computing Systems ◽  
Parallel Application ◽  
Assignment Algorithm

Distributed computing can be used to solve large scale scientific and engineering problems. A parallel application could be divided into a number of tasks and executed concurrently on different computers in the system. This paper provides an optimal task assignment algorithm under memory constraints to minimize required time of finishing a parallel application. The proposed algorithm is based on the optimal assignment sequential search (OASS) of the A* algorithm with additional modifications. This modified algorithm yields optimal solution, lower time complexity, reduces the turnaround time of the application and considerably faster compared with the sequential search algorithm.

An Iterated Local Search Algorithm for Task Assignment in Distributed Computing Systems

2012 ◽  
Vol 588-589 ◽  
pp. 1308-1311
Author(s):  
Qin Ma Kang ◽  
Hong He ◽  
Hai Ning Jiang
Keyword(s):  
Distributed Computing ◽  
Local Search ◽  
Search Algorithm ◽  
Task Assignment ◽  
Computation Time ◽  
Iterated Local Search ◽  
Local Search Algorithm ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
Wide Range

This paper considers the problem of task assignment in heterogeneous distributed computing systems with the goal of minimizing the total execution and communication costs. An iterated local search algorithm is proposed for finding the suboptimal task assignment in a reasonable amount of computation time. We study the performance of the proposed algorithm over a wide range of parameters such as the problem scales, the ratio of average communication time to average computation time, and task interaction density of applications. The effectiveness of the algorithm is manifested by comparing it with other competing algorithms in the relevant literature.

scholarly journals Anytime Heuristic Search

2007 ◽  
Vol 28 ◽  
pp. 267-297 ◽  
Author(s):  
E. A. Hansen ◽  
R. Zhou
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Search Time ◽  
Optimal Solution ◽  
Search Problem ◽  
A Algorithm ◽  
Solution Quality ◽  
Multiple Sequence ◽  
Anytime Algorithm ◽  
Heuristic Search Algorithm

We describe how to convert the heuristic search algorithm A* into an anytime algorithm that finds a sequence of improved solutions and eventually converges to an optimal solution. The approach we adopt uses weighted heuristic search to find an approximate solution quickly, and then continues the weighted search to find improved solutions as well as to improve a bound on the suboptimality of the current solution. When the time available to solve a search problem is limited or uncertain, this creates an anytime heuristic search algorithm that allows a flexible tradeoff between search time and solution quality. We analyze the properties of the resulting Anytime A* algorithm, and consider its performance in three domains; sliding-tile puzzles, STRIPS planning, and multiple sequence alignment. To illustrate the generality of this approach, we also describe how to transform the memory-efficient search algorithm Recursive Best-First Search (RBFS) into an anytime algorithm.

Economic Load Dispatch Using Oppositional Backtracking Search Algorithm

2017 ◽  
Vol 6 (2) ◽  
pp. 79-97 ◽  
Author(s):  
Moumita Pradhan ◽  
Provas Kumar Roy ◽  
Tandra Pal
Keyword(s):  
Large Scale ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Economic Load Dispatch ◽  
Invasive Weed ◽  
Backtracking Search ◽  
Backtracking Search Algorithm ◽  
Test Systems ◽  
Generation Cost ◽  
Feasible Solutions

In this paper, an oppositional backtracking search algorithm (OBSA) is proposed to solve the large scale economic load dispatch (ELD) problem. The main drawback of the conventional backtracking search algorithm (BSA) is that it produces a local optimal solution rather than the global optimal solution. The proposed OBSA methodology is a highly-constrained optimization problem has to minimize the total generation cost by satisfying several constraints involving load demand, generation limits, prohibited operating zone, ramp rate limits and valve point loading effect. The proposed method is applied for three test systems and provides the unique and fast solutions. The new heuristic OBSA approach is successfully applied in three test systems consisting of 13 and 140 thermal generators. The test results are judged against various methods. The simulation results show the effectiveness and accuracy of the proposed OBSA algorithm over other methods like conventional BSA, oppositional invasive weed optimization (OIWO), Shuffled differential evolution (SDE) and oppositional real coded chemical reaction optimization (ORCCRO). This clearly suggests that the new OBSA method can achieve effective and feasible solutions of nonlinear ELD problems.

GAA-Based Decision Approach for Hospital Building Renovation Management

2011 ◽  
Vol 403-408 ◽  
pp. 5265-5272
Author(s):  
Yi Kai Juan ◽  
Yu Ching Cheng ◽  
Yeng Horng Perng ◽  
Guang Bin Wang
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Energy Use ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Developed Countries ◽  
Sustainable Technologies ◽  
Trade Offs ◽  
Hospital Building

More and more attention has been paid to hospital facilities since modern pandemics have emerged such as SARS and avian influenza. Energy consumption by buildings accounts for 20-40% of energy use in developed countries, so many global organizations make efforts to develop sustainable technologies or materials to create a sustainable environment, and to reduce energy consumption when renovating building. Therefore, maintaining high standards of hygiene and reducing energy consumption has become the major task for hospital buildings. This study develops an integrated decision support system to assess existing hospital building conditions and to recommend an optimal scheme of sustainable renovation actions, considering trade-offs between renovation cost, improved building quality, and environmental impacts. A hybrid approach that combines the A* graph search algorithm with genetic algorithms (GA) is used to analyze all possible renovation actions and their trade-offs to develop the optimal solution. A simulated hospital renovation project is established to demonstrate the system. The result reveals the system can solve complicated and large-scale combinational, discrete and determinate problems such as the hospital renovation project, and also improve traditional building condition assessment to be more effective and efficient.

scholarly journals A Two-Layer Task Assignment Algorithm for UAV Swarm Based on Feature Weight Clustering

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaowei Fu ◽  
Peng Feng ◽  
Bin Li ◽  
Xiaoguang Gao
Keyword(s):  
Large Scale ◽  
Task Assignment ◽  
Small Scale ◽  
Resource Assignment ◽  
Assignment Algorithm ◽  
Uav Swarm ◽  
Feature Weight ◽  
Bundle Algorithm ◽  
Aerial Vehicle ◽  
Simulation Results

For the large-scale operations of unmanned aerial vehicle (UAV) swarm and the large number of UAVs, this paper proposes a two-layer task and resource assignment algorithm based on feature weight clustering. According to the numbers and types of task resources of each UAV and the distances between different UAVs, the UAV swarm is divided into multiple UAV clusters, and the large-scale allocation problem is transformed into several related small-scale problems. A two-layer task assignment algorithm based on the consensus-based bundle algorithm (CBBA) is proposed, and this algorithm uses different consensus rules between clusters and within clusters, which ensures that the UAV swarm gets a conflict-free task assignment solution in real time. The simulation results show that the algorithm can assign tasks effectively and efficiently when the number of UAVs and targets is large.

IMPROVED TABU SEARCH RECURSIVE FUZZY METHOD FOR CRUDE OIL INDUSTRY

2012 ◽  
Vol 03 (01) ◽  
pp. 1150002 ◽  
Author(s):  
P. VASANT ◽  
T. GANESAN ◽  
I. ELAMVAZUTHI
Keyword(s):  
Tabu Search ◽  
Crude Oil ◽  
Large Scale ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Oil Field ◽  
Fuzzy Linear Programming ◽  
Oil Refinery ◽  
Profit Function ◽  
Decision Variables

The minimization of the profit function with respect to the decision variables is very important for the decision makers in the oil field industry. In this paper, a novel approach of the improved tabu search algorithm has been employed to solve a large scale problem in the crude oil refinery industry. This problem involves 44 variables, 36 constraints, and four decision variables which represent four types of crude oil types. The decision variables have been modeled in the form of fuzzy linear programming problem. The vagueness factor in the decision variables is captured by the nonlinear modified S-curve membership function. A recursive improved tabu search has been used to solve this fuzzy optimization problem. Tremendously improved results are obtained for the optimal profit function and optimal solution for four crude oil. The accuracy of constraints satisfaction and the quality of the solutions are achieved successfully.

scholarly journals A Framing Link Based Tabu Search Algorithm for Large-Scale Multidepot Vehicle Routing Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Xuhao Zhang ◽  
Shiquan Zhong ◽  
Yiliu Liu ◽  
Xuelian Wang
Keyword(s):  
Tabu Search ◽  
Vehicle Routing ◽  
Large Scale ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Tabu Search Algorithm ◽  
Routing Problem ◽  
Selection Principles ◽  
Speed Up ◽  
Computation Speed

A framing link (FL) based tabu search algorithm is proposed in this paper for a large-scale multidepot vehicle routing problem (LSMDVRP). Framing links are generated during continuous great optimization of current solutions and then taken as skeletons so as to improve optimal seeking ability, speed up the process of optimization, and obtain better results. Based on the comparison between pre- and postmutation routes in the current solution, different parts are extracted. In the current optimization period, links involved in the optimal solution are regarded as candidates to the FL base. Multiple optimization periods exist in the whole algorithm, and there are several potential FLs in each period. If the update condition is satisfied, the FL base is updated, new FLs are added into the current route, and the next period starts. Through adjusting the borderline of multidepot sharing area with dynamic parameters, the authors define candidate selection principles for three kinds of customer connections, respectively. Link split and the roulette approach are employed to choose FLs. 18 LSMDVRP instances in three groups are studied and new optimal solution values for nine of them are obtained, with higher computation speed and reliability.

scholarly journals Novel optimization using hierarchical Path finding A* (HPA*) algorithm for strategic gaming setup

2018 ◽  
Vol 7 (2.6) ◽  
pp. 54
Author(s):  
Aqsa Zafar ◽  
Krishna Kant Agrawal
Keyword(s):  
Shortest Path ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Research Area ◽  
Optimization Techniques ◽  
Path Finding ◽  
A Algorithm ◽  
Strategic Gaming ◽  
Game Industry ◽  
Future Work

In game Industry, the most trending research area is shortest path finding. There are many video games are present who are facing the problem of path finding and there is various algorithms are present to solve this problem. In this paper brief introduction is given in the most using algorithm for path finding and A* algorithm has been proved the best algorithm for resolving the problem of shortest path finding in games. It provides the optimal solution for path finding as compare to other search algorithm. At the start of the paper, brief introduction about the path finding is given. Then the reviews of different search algorithm are presented on the basis of path finding. After that information of A* algorithm and optimization techniques are described. In the last, application and examples how the path finding techniques are used in the game is addressed and future work and conclusion are drawn.

scholarly journals A Metaheuristic Algorithm for VLSI Floorplanning Problem

2019 ◽  
Vol 8 (2S5) ◽  
pp. 249-254
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Search Algorithm ◽  
Design Method ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Chip Area ◽  
Optimum Solution ◽  
Vlsi Floorplanning

Floorplanning plays an important role within the physical design method of very large Scale Integrated (VLSI) chips. It’s a necessary design step to estimate the chip area before the optimized placement of digital blocks and their interconnections. Since VLSI floorplanning is an NP-hard problem, several improvement techniques were adopted to find optimal solution. In this paper, a hybrid algorithm which is genetic algorithm combined with music-inspired Harmony Search (HS) algorithm is employed for the fixed die outline constrained floorplanning, with the ultimate aim of reducing the full chip area. Initially, B*-tree is employed to come up with the first floorplan for the given rectangular hard modules and so Harmony Search algorithm is applied in any stages in genetic algorithm to get an optimum solution for the economical floorplan. The experimental results of the HGA algorithm are obtained for the MCNC benchmark circuits

Evaluation Of Algorithm Performance For Simulated Square And Non-Square Logistic Assignment Problems

2021 ◽  
Author(s):  
Maximilian Selmair ◽  
Sascha Hamzehi ◽  
Klaus-Juergen Meier
Keyword(s):  
Approximation Method ◽  
Assignment Problem ◽  
Large Scale ◽  
Optimal Allocation ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Computational Time ◽  
Quadratic Assignment ◽  
Assignment Problems ◽  
Objective Value

The optimal allocation of transportation tasks to a fleet of vehicles, especially for large-scale systems of more than 20 Autonomous Mobile Robots (AMRs), remains a major challenge in logistics. Optimal in this context refers to two criteria: how close a result is to the best achievable objective value and the shortest possible computational time. Operations research has provided different methods that can be applied to solve this assignment problem. Our literature review has revealed six commonly applied methods to solve this problem. In this paper, we compared three optimal methods (Integer Linear Programming, Hungarian Method and the Jonker Volgenant Castanon algorithm) to three three heuristic methods (Greedy Search algorithm, Vogel’s Approximation Method and Vogel’s Approximation Method for non-quadratic Matrices). The latter group generally yield results faster, but were not developed to provide optimal results in terms of the optimal objective value. Every method was applied to 20.000 randomised samples of matrices, which differed in scale and configuration, in simulation experiments in order to determine the results’ proximity to the optimal solution as well as their computational time. The simulation results demonstrate that all methods vary in their time needed to solve the assignment problem scenarios as well as in the respective quality of the solution. Based on these results yielded by computing quadratic and non-quadratic matrices of different scales, we have concluded that the Jonker Volgenant Castanon algorithm is deemed to be the best method for solving quadratic and non-quadratic assignment problems with optimal precision. However, if performance in terms of computational time is prioritised for large non-quadratic matrices (50×300 and larger), the Vogel’s Approximation Method for non-quadratic Matrices generates faster approximated solutions.

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