**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**3339

# Search results for: greedy algorithm

##### 3339 A “Greedy“ Czech Manufacturing Case

**Authors:**
George Cristian Gruia,
Michal Kavan

**Abstract:**

**Keywords:**
Czech,
greedy algorithm,
minimize,
total costs.

##### 3338 Choosing Search Algorithms in Bayesian Optimization Algorithm

**Authors:**
Hao Wu,
Jonathan L. Shapiro

**Abstract:**

The Bayesian Optimization Algorithm (BOA) is an algorithm based on the estimation of distributions. It uses techniques from modeling data by Bayesian networks to estimating the joint distribution of promising solutions. To obtain the structure of Bayesian network, different search algorithms can be used. The key point that BOA addresses is whether the constructed Bayesian network could generate new and useful solutions (strings), which could lead the algorithm in the right direction to solve the problem. Undoubtedly, this ability is a crucial factor of the efficiency of BOA. Varied search algorithms can be used in BOA, but their performances are different. For choosing better ones, certain suitable method to present their ability difference is needed. In this paper, a greedy search algorithm and a stochastic search algorithm are used in BOA to solve certain optimization problem. A method using Kullback-Leibler (KL) Divergence to reflect their difference is described.

**Keywords:**
Bayesian optimization algorithm,
greedy search,
KL divergence,
stochastic search.

##### 3337 Model-Based Software Regression Test Suite Reduction

**Authors:**
Shiwei Deng,
Yang Bao

**Abstract:**

**Keywords:**
Dependence analysis,
EFSM model,
greedy
algorithm,
regression test.

##### 3336 Block Based Imperial Competitive Algorithm with Greedy Search for Traveling Salesman Problem

**Authors:**
Meng-Hui Chen,
Chiao-Wei Yu,
Pei-Chann Chang

**Abstract:**

Imperial competitive algorithm (ICA) simulates a multi-agent algorithm. Each agent is like a kingdom has its country, and the strongest country in each agent is called imperialist, others are colony. Countries are competitive with imperialist which in the same kingdom by evolving. So this country will move in the search space to find better solutions with higher fitness to be a new imperialist. The main idea in this paper is using the peculiarity of ICA to explore the search space to solve the kinds of combinational problems. Otherwise, we also study to use the greed search to increase the local search ability. To verify the proposed algorithm in this paper, the experimental results of traveling salesman problem (TSP) is according to the traveling salesman problem library (TSPLIB). The results show that the proposed algorithm has higher performance than the other known methods.

**Keywords:**
Traveling Salesman Problem,
Artificial Chromosomes,
Greedy Search,
Imperial Competitive Algorithm.

##### 3335 Design and Implementation of Optimal Winner Determination Algorithm in Combinatorial e- Auctions

**Authors:**
S. Khanpour,
A. Movaghar

**Abstract:**

**Keywords:**
Bids,
genetic algorithm,
heuristic,
metaheuristic,
simulated annealing greedy.

##### 3334 Optimal Management of Internal Capital of Company

**Authors:**
S. Sadallah

**Abstract:**

**Keywords:**
Management,
software,
optimal,
greedy algorithm,
graph-diagram.

##### 3333 An Improved Greedy Routing Algorithm for Grid using Pheromone-Based Landmarks

**Authors:**
Lada-On Lertsuwanakul,
Herwig Unger

**Abstract:**

This paper objects to extend Jon Kleinberg-s research. He introduced the structure of small-world in a grid and shows with a greedy algorithm using only local information able to find route between source and target in delivery time O(log2n). His fundamental model for distributed system uses a two-dimensional grid with longrange random links added between any two node u and v with a probability proportional to distance d(u,v)-2. We propose with an additional information of the long link nearby, we can find the shorter path. We apply the ant colony system as a messenger distributed their pheromone, the long-link details, in surrounding area. The subsequence forwarding decision has more option to move to, select among local neighbors or send to node has long link closer to its target. Our experiment results sustain our approach, the average routing time by Color Pheromone faster than greedy method.

**Keywords:**
Routing algorithm,
Small-World network,
Ant Colony Optimization,
and Peer-to-peer System.

##### 3332 Greedy Geographical Void Routing for Wireless Sensor Networks

**Authors:**
Chiang Tzu-Chiang,
Chang Jia-Lin,
Tsai Yue-Fu,
Li Sha-Pai

**Abstract:**

**Keywords:**
Wireless sensor network,
internet routing,
wireless network,
greedy void avoiding algorithm,
bypassing void.

##### 3331 An Algorithm for the Map Labeling Problem with Two Kinds of Priorities

**Authors:**
Noboru Abe,
Yoshinori Amai,
Toshinori Nakatake,
Sumio Masuda,
Kazuaki Yamaguchi

**Abstract:**

We consider the problem of placing labels of the points on a plane. For each point, its position, the size of its label and a priority are given. Moreover, several candidates of its label positions are prespecified, and each of such label positions is assigned a priority. The objective of our problem is to maximize the total sum of priorities of placed labels and their points. By refining a labeling algorithm that can use these priorities, we propose a new heuristic algorithm which is more suitable for treating the assigned priorities.

**Keywords:**
Map labeling,
greedy algorithm,
heuristic algorithm,
priority.

##### 3330 Particle Filter Supported with the Neural Network for Aircraft Tracking Based on Kernel and Active Contour

**Authors:**
Mohammad Izadkhah,
Mojtaba Hoseini,
Alireza Khalili Tehrani

**Abstract:**

**Keywords:**
Video tracking,
particle filter,
greedy snake,
neural
network.

##### 3329 Multi Objective Micro Genetic Algorithm for Combine and Reroute Problem

**Authors:**
Soottipoom Yaowiwat,
Manoj Lohatepanont,
Proadpran Punyabukkana

**Abstract:**

**Keywords:**
Irregular Airline Operation,
Combine and RerouteRoutine,
Genetic Algorithm,
Micro Genetic Algorithm,
Multi ObjectiveOptimization,
Evolutionary Algorithm.

##### 3328 Resource Allocation and Task Scheduling with Skill Level and Time Bound Constraints

**Authors:**
Salam Saudagar,
Ankit Kamboj,
Niraj Mohan,
Satgounda Patil,
Nilesh Powar

**Abstract:**

Task Assignment and Scheduling is a challenging Operations Research problem when there is a limited number of resources and comparatively higher number of tasks. The Cost Management team at Cummins needs to assign tasks based on a deadline and must prioritize some of the tasks as per business requirements. Moreover, there is a constraint on the resources that assignment of tasks should be done based on an individual skill level, that may vary for different tasks. Another constraint is for scheduling the tasks that should be evenly distributed in terms of number of working hours, which adds further complexity to this problem. The proposed greedy approach to solve assignment and scheduling problem first assigns the task based on management priority and then by the closest deadline. This is followed by an iterative selection of an available resource with the least allocated total working hours for a task, i.e. finding the local optimal choice for each task with the goal of determining the global optimum. The greedy approach task allocation is compared with a variant of Hungarian Algorithm, and it is observed that the proposed approach gives an equal allocation of working hours among the resources. The comparative study of the proposed approach is also done with manual task allocation and it is noted that the visibility of the task timeline has increased from 2 months to 6 months. An interactive dashboard app is created for the greedy assignment and scheduling approach and the tasks with more than 2 months horizon that were waiting in a queue without a delivery date initially are now analyzed effectively by the business with expected timelines for completion.

**Keywords:**
Assignment,
deadline,
greedy approach,
hungarian algorithm,
operations research,
scheduling.

##### 3327 Bitrate Reduction Using FMO for Video Streaming over Packet Networks

**Authors:**
Le Thanh Ha,
Hye-Soo Kim,
Chun-Su Park,
Seung-Won Jung,
Sung-Jea Ko

**Abstract:**

**Keywords:**
Data Partition,
Entropy Coding,
Greedy Algorithm,
H.264/AVC,
Slice Group.

##### 3326 Solving the Teacher Assignment-Course Scheduling Problem by a Hybrid Algorithm

**Authors:**
Aldy Gunawan,
Kien Ming Ng,
Kim Leng Poh

**Abstract:**

This paper presents a hybrid algorithm for solving a timetabling problem, which is commonly encountered in many universities. The problem combines both teacher assignment and course scheduling problems simultaneously, and is presented as a mathematical programming model. However, this problem becomes intractable and it is unlikely that a proven optimal solution can be obtained by an integer programming approach, especially for large problem instances. A hybrid algorithm that combines an integer programming approach, a greedy heuristic and a modified simulated annealing algorithm collaboratively is proposed to solve the problem. Several randomly generated data sets of sizes comparable to that of an institution in Indonesia are solved using the proposed algorithm. Computational results indicate that the algorithm can overcome difficulties of large problem sizes encountered in previous related works.

**Keywords:**
Timetabling problem,
mathematical programming
model,
hybrid algorithm,
simulated annealing.

##### 3325 Approximation Algorithm for the Shortest Approximate Common Superstring Problem

**Authors:**
A.S. Rebaï,
M. Elloumi

**Abstract:**

**Keywords:**
Shortest approximate common superstring,
approximation algorithms,
strings overlaps,
complexities.

##### 3324 Optimizing Network Latency with Fast Path Assignment for Incoming Flows

**Abstract:**

**Keywords:**
Latency,
Fast path assignment,
Bottleneck link.

##### 3323 A Subjective Scheduler Based on Backpropagation Neural Network for Formulating a Real-life Scheduling Situation

**Authors:**
K. G. Anilkumar,
T. Tanprasert

**Abstract:**

**Keywords:**
Backpropagation algorithm,
Critical value,
Greedy
alignment procedure,
Neural network,
Subjective criteria,
Satisfiability.

##### 3322 Simulation Modeling of Manufacturing Systems for the Serial Route and the Parallel One

**Authors:**
Tadeusz Witkowski,
Paweł Antczak,
Arkadiusz Antczak

**Abstract:**

**Keywords:**
Makespan,
open shop,
route flexibility,
serial and
parallel route,
simulation modeling,
type of production.

##### 3321 Upgraded Cuckoo Search Algorithm to Solve Optimisation Problems Using Gaussian Selection Operator and Neighbour Strategy Approach

**Authors:**
Mukesh Kumar Shah,
Tushar Gupta

**Abstract:**

An Upgraded Cuckoo Search Algorithm is proposed here to solve optimization problems based on the improvements made in the earlier versions of Cuckoo Search Algorithm. Short comings of the earlier versions like slow convergence, trap in local optima improved in the proposed version by random initialization of solution by suggesting an Improved Lambda Iteration Relaxation method, Random Gaussian Distribution Walk to improve local search and further proposing Greedy Selection to accelerate to optimized solution quickly and by “Study Nearby Strategy” to improve global search performance by avoiding trapping to local optima. It is further proposed to generate better solution by Crossover Operation. The proposed strategy used in algorithm shows superiority in terms of high convergence speed over several classical algorithms. Three standard algorithms were tested on a 6-generator standard test system and the results are presented which clearly demonstrate its superiority over other established algorithms. The algorithm is also capable of handling higher unit systems.

**Keywords:**
Economic dispatch,
Gaussian selection operator,
prohibited operating zones,
ramp rate limits,
upgraded cuckoo search.

##### 3320 Partially Knowing of Least Support Orthogonal Matching Pursuit (PKLS-OMP) for Recovering Signal

**Authors:**
Israa Sh. Tawfic,
Sema Koc Kayhan

**Abstract:**

Given a large sparse signal, great wishes are to reconstruct the signal precisely and accurately from lease number of measurements as possible as it could. Although this seems possible by theory, the difficulty is in built an algorithm to perform the accuracy and efficiency of reconstructing. This paper proposes a new proved method to reconstruct sparse signal depend on using new method called Least Support Matching Pursuit (LS-OMP) merge it with the theory of Partial Knowing Support (PSK) given new method called Partially Knowing of Least Support Orthogonal Matching Pursuit (PKLS-OMP). The new methods depend on the greedy algorithm to compute the support which depends on the number of iterations. So to make it faster, the PKLS-OMP adds the idea of partial knowing support of its algorithm. It shows the efficiency, simplicity, and accuracy to get back the original signal if the sampling matrix satisfies the Restricted Isometry Property (RIP). Simulation results also show that it outperforms many algorithms especially for compressible signals.

**Keywords:**
Compressed sensing,
Lest Support Orthogonal
Matching Pursuit,
Partial Knowing Support,
Restricted isometry
property,
signal reconstruction.

##### 3319 A Hybrid Multi-Objective Firefly-Sine Cosine Algorithm for Multi-Objective Optimization Problem

**Authors:**
Gaohuizi Guo,
Ning Zhang

**Abstract:**

**Keywords:**
Firefly algorithm,
hybrid algorithm,
multi-objective optimization,
Sine Cosine algorithm.

##### 3318 Approximating Fixed Points by a Two-Step Iterative Algorithm

**Authors:**
Safeer Hussain Khan

**Abstract:**

In this paper, we introduce a two-step iterative algorithm to prove a strong convergence result for approximating common fixed points of three contractive-like operators. Our algorithm basically generalizes an existing algorithm..Our iterative algorithm also contains two famous iterative algorithms: Mann iterative algorithm and Ishikawa iterative algorithm. Thus our result generalizes the corresponding results proved for the above three iterative algorithms to a class of more general operators. At the end, we remark that nothing prevents us to extend our result to the case of the iterative algorithm with error terms.

**Keywords:**
Contractive-like operator,
iterative algorithm,
fixed point,
strong convergence.

##### 3317 Some Improvements on Kumlander-s Maximum Weight Clique Extraction Algorithm

**Authors:**
Satoshi Shimizu,
Kazuaki Yamaguchi,
Toshiki Saitoh,
Sumio Masuda

**Abstract:**

Some fast exact algorithms for the maximum weight clique problem have been proposed. Östergard’s algorithm is one of them. Kumlander says his algorithm is faster than it. But we confirmed that the straightforwardly implemented Kumlander’s algorithm is slower than O¨ sterga˚rd’s algorithm. We propose some improvements on Kumlander’s algorithm.

**Keywords:**
Maximum weight clique,
exact algorithm,
branch-andbound,
NP-hard.

##### 3316 A Simple Adaptive Atomic Decomposition Voice Activity Detector Implemented by Matching Pursuit

**Authors:**
Thomas Bryan,
Veton Kepuska,
Ivica Kostanic

**Abstract:**

**Keywords:**
Atomic Decomposition,
Gabor,
Gammatone,
Matching Pursuit,
Voice Activity Detection.

##### 3315 Position Based Routing Protocol with More Reliability in Mobile Ad Hoc Network

**Authors:**
Mahboobeh Abdoos,
Karim Faez,
Masoud Sabaei

**Abstract:**

**Keywords:**
Mobile Ad Hoc Network,
Position Based,
Reliability,
Routing.

##### 3314 Application of Adaptive Genetic Algorithm in Function Optimization

**Authors:**
Panpan Xu,
Shulin Sui

**Abstract:**

The crossover probability and mutation probability are the two important factors in genetic algorithm. The adaptive genetic algorithm can improve the convergence performance of genetic algorithm, in which the crossover probability and mutation probability are adaptively designed with the changes of fitness value. We apply adaptive genetic algorithm into a function optimization problem. The numerical experiment represents that adaptive genetic algorithm improves the convergence speed and avoids local convergence.

**Keywords:**
Genetic algorithm,
Adaptive genetic algorithm,
Function optimization.

##### 3313 Optimal External Merge Sorting Algorithm with Smart Block Merging

**Authors:**
Mir Hadi Seyedafsari,
Iraj Hasanzadeh

**Abstract:**

**Keywords:**
External sorting algorithm,
internal sortingalgorithm,
fast sorting,
robust algorithm.

##### 3312 Template-Based Object Detection through Partial Shape Matching and Boundary Verification

**Authors:**
Feng Ge,
Tiecheng Liu,
Song Wang,
Joachim Stahl

**Abstract:**

**Keywords:**
Object detection,
shape matching,
contour grouping.

##### 3311 Analog Circuit Design using Genetic Algorithm: Modified

**Authors:**
Amod P. Vaze

**Abstract:**

Genetic Algorithm has been used to solve wide range of optimization problems. Some researches conduct on applying Genetic Algorithm to analog circuit design automation. These researches show a better performance due to the nature of Genetic Algorithm. In this paper a modified Genetic Algorithm is applied for analog circuit design automation. The modifications are made to the topology of the circuit. These modifications will lead to a more computationally efficient algorithm.

**Keywords:**
Genetic algorithm,
analog circuits,
design.

##### 3310 Application of Hybrid Genetic Algorithm Based on Simulated Annealing in Function Optimization

**Authors:**
Panpan Xu,
Shulin Sui,
Zongjie Du

**Abstract:**

**Keywords:**
Genetic algorithm,
Simulated annealing,
Hybrid
genetic algorithm,
Function optimization.