In order to achieve the wide range of the robotic application it is necessary to provide iterative motions
among points of the goals. For instance, in the industry mobile robots can replace any components between
a storehouse and an assembly department. Ammunition replacement is widely used in military services.
Working place is possible in ports, airports, waste site and etc. Mobile agents can be used for monitoring if
it is necessary to observe control points in the secret place. The paper deals with path planning programme
for mobile robots. The aim of the research paper is to analyse motion-planning algorithms that contain the
design of modelling programme. The programme is needed as environment modelling to obtain the
simulation data. The simulation data give the possibility to conduct the wide analyses for selected
algorithm. Analysis means the simulation data interpretation and comparison with other data obtained
using the motion-planning. The results of the careful analysis were considered for optimal path planning
algorithms. The experimental evidence was proposed to demonstrate the effectiveness of the algorithm for
steady covered space. The results described in this work can be extended in a number of directions, and
applied to other algorithms.
A COMPARISON BETWEEN SWARM INTELLIGENCE ALGORITHMS FOR ROUTING PROBLEMSecij
Travelling salesman problem (TSP) is a most popular combinatorial routing problem, belongs to the class of NP-hard problems. Many approacheshave been proposed for TSP.Among them, swarm intelligence (SI) algorithms can effectively achieve optimal tours with the minimum lengths and attempt to avoid trapping in local minima points. The transcendence of each SI is depended on the nature of the problem. In our studies, there has been yet no any article, which had compared the performance of SI algorithms for TSP perfectly. In this paper,four common SI algorithms are used to solve TSP, in order to compare the performance of SI algorithms for the TSP problem. These algorithms include genetic algorithm, particle swarm optimization, ant colony optimization, and artificial bee colony. For each SI, the various parameters and operators were tested, and the best values were selected for it. Experiments oversome benchmarks fromTSPLIBshow that
artificial bee colony algorithm is the best one among the fourSI-basedmethods to solverouting problems like TSP.
OPTIMAL GLOBAL THRESHOLD ESTIMATION USING STATISTICAL CHANGE-POINT DETECTIONsipij
Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical
method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does
not assume any prior statistical distribution of background and object grey levels. Further, this method is
less influenced by an outlier due to our judicious derivation of a robust criterion function depending on
Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method
compared to other popular methods available for global image thresholding. In this paper we also propose
a performance criterion for comparison of thresholding algorithms. This performance criteria does not
depend on any ground truth image. We have used this performance criterion to compare the results of
proposed thresholding algorithm with most cited global thresholding algorithms in the literature.
A COMPARISON BETWEEN SWARM INTELLIGENCE ALGORITHMS FOR ROUTING PROBLEMSecij
Travelling salesman problem (TSP) is a most popular combinatorial routing problem, belongs to the class of NP-hard problems. Many approacheshave been proposed for TSP.Among them, swarm intelligence (SI) algorithms can effectively achieve optimal tours with the minimum lengths and attempt to avoid trapping in local minima points. The transcendence of each SI is depended on the nature of the problem. In our studies, there has been yet no any article, which had compared the performance of SI algorithms for TSP perfectly. In this paper,four common SI algorithms are used to solve TSP, in order to compare the performance of SI algorithms for the TSP problem. These algorithms include genetic algorithm, particle swarm optimization, ant colony optimization, and artificial bee colony. For each SI, the various parameters and operators were tested, and the best values were selected for it. Experiments oversome benchmarks fromTSPLIBshow that
artificial bee colony algorithm is the best one among the fourSI-basedmethods to solverouting problems like TSP.
OPTIMAL GLOBAL THRESHOLD ESTIMATION USING STATISTICAL CHANGE-POINT DETECTIONsipij
Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical
method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does
not assume any prior statistical distribution of background and object grey levels. Further, this method is
less influenced by an outlier due to our judicious derivation of a robust criterion function depending on
Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method
compared to other popular methods available for global image thresholding. In this paper we also propose
a performance criterion for comparison of thresholding algorithms. This performance criteria does not
depend on any ground truth image. We have used this performance criterion to compare the results of
proposed thresholding algorithm with most cited global thresholding algorithms in the literature.
LOGNORMAL ORDINARY KRIGING METAMODEL IN SIMULATION OPTIMIZATIONorajjournal
This paper presents a lognormal ordinary kriging (LOK) metamodel algorithm and its application to
optimize a stochastic simulation problem. Kriging models have been developed as an interpolation method
in geology. They have been successfully used for the deterministic simulation optimization (SO) problem. In
recent years, kriging metamodeling has attracted a growing interest with stochastic problems. SO
researchers have begun using ordinary kriging through global optimization in stochastic systems. The
goals of this study are to present LOK metamodel algorithm and to analyze the result of the application
step-by-step. The results show that LOK is a powerful alternative metamodel in simulation optimization
when the data are too skewed.
This paper presentsa novel data flow architecturethat utilizes data from engineering simulations to
generate a reduced order model within Apache Spark. The reduced order model from Spark is then utilized by
anevolutionary algorithm in the optimization of an industrial system component. This work is presented in the
context of the shape optimization of a heat exchanger fin and demonstrates the ability of theengineering
simulation, the reduced order model and the evolutionary algorithm to exchange data with each other by
utilizing Spark as the common data-processing framework. In order to enable a user to monitor the input design
parameter space,self-organizing maps are generated for visualization. The results of theevolutionary
optimization utilizing this data flow are compared with results from invoking high-fidelity engineering
simulations. This novel data flow architecture decouples the evolutionary algorithm from the reduced order
model and allows improvement of the optimization results by continuously augmenting the reduced order model
with data from the evolutionary algorithm.Additionally, when constraints on the optimization algorithm are
modifiedthe evolutionary algorithm canadapt and evolve good solutions. Themethodology presented in this
articlealso makes it feasible to simultaneously tune evolutionary optimization experiments along with
engineering simulations at a relatively low computational cost.
A MODIFIED VORTEX SEARCH ALGORITHM FOR NUMERICAL FUNCTION OPTIMIZATIONijaia
The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was
inspired from the vortical flow of the stirred fluids. Although the VS algorithm is shown to be a good
candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS
algorithm, candidate solutions are generated around the current best solution by using a Gaussian
distribution at each iteration pass. This provides simplicity to the algorithm but it also leads to some
problems along. Especially, for the functions those have a number of local minimum points, to select a
single point to generate candidate solutions leads the algorithm to being trapped into a local minimum
point. Due to the adaptive step-size adjustment scheme used in the VS algorithm, the locality of the created
candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local
point as quickly as possible, it becomes much more difficult for the algorithm to escape from that point in
the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to overcome
above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions
are generated around a number of points at each iteration pass. Computational results showed that with
the help of this modification the global search ability of the existing VS algorithm is improved and the
MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark
numerical function set.
Parallel sorting algorithms order a set of elements USING MULTIPLE processors in order to enhance the performance of sequential sorting algorithms. In general, the performance of sorting algorithms are EVALUATED IN term of algorithm growth rate according to the input size. In this paper, the running time, parallel speedup and parallel efficiency OF PARALLEL bubble sort is evaluated and measured. Message Passing Interface (MPI) IS USED for implementing the parallel version of bubble sort and IMAN1 supercomputer is used to conduct the results. The evaluation results show that parallel bubble sort has better running time as the number of processors increases. On other hand, regarding parallel efficiency, parallel bubble sort algorithm is more efficient to be applied OVER SMALL number of processors.
GENETIC ALGORITHM FOR FUNCTION APPROXIMATION: AN EXPERIMENTAL INVESTIGATIONijaia
Function Approximation is a popular engineering problems used in system identification or Equation
optimization. Due to the complex search space it requires, AI techniques has been used extensively to spot
the best curves that match the real behavior of the system. Genetic algorithm is known for their fast
convergence and their ability to find an optimal structure of the solution. We propose using a genetic
algorithm as a function approximator. Our attempt will focus on using the polynomial form of the
approximation. After implementing the algorithm, we are going to report our results and compare it with
the real function output.
The Traveling salesman problem (TSP) is proved to be NP-complete in most cases. The genetic algorithm
(GA) is one of the most useful algorithms for solving this problem. In this paper a conventional GA is
compared with an improved hybrid GA in solving TSP. The improved or hybrid GA consist of
conventional GA and two local optimization strategies. The first strategy is extracting all sequential
groups including four cities of samples and changing the two central cities with each other. The second
local optimization strategy is similar to an extra mutation process. In this step with a low probability a
sample is selected. In this sample two random cities are defined and the path between these cities is
reversed. The computation results show that the proposed method also finds better paths than the
conventional GA within an acceptable computation time.
Path Finding Solutions For Grid Based Graphacijjournal
Any path finding will work as long as there are no obstacles on distractions along the way. A genetic A*
algorithm has been used for more advanced environments in graph. Implementation of the path finding
algorithm for grid based graph with or without obstacles.
FORECASTING ENERGY CONSUMPTION USING FUZZY TRANSFORM AND LOCAL LINEAR NEURO F...ijsc
This paper proposes a hybrid approach based on local linear neuro fuzzy (LLNF) model and fuzzy transform (F-transform), termed FT-LLNF, for prediction of energy consumption. LLNF models are powerful in modelling and forecasting highly nonlinear and complex time series. Starting from an optimal
linear least square model, they add nonlinear neurons to the initial model as long as the model's accuracy is improved. Trained by local linear model tree learning (LOLIMOT) algorithm, the LLNF models provide maximum generalizability as well as the outstanding performance. Besides, the recently introduced technique of fuzzy transform (F-transform) is employed as a time series pre-processing method. The
technique of F-transform, established based on the concept of fuzzy partitions, eliminates noisy variations of the original time series and results in a well-behaved series which can be predicted with higher accuracy. The proposed hybrid method of FT-LLNF is applied to prediction of energy consumption in the
United States and Canada. The prediction results and comparison to optimized multi-layer perceptron (MLP) models and the LLNF itself, revealed the promising performance of the proposed approach for energy consumption prediction and its potential usage for real world applications.
Rotation Invariant Matching of Partial ShoeprintsIJERA Editor
In this paper, we propose a solution for the problem of rotated partial shoeprints retrieval based on the combined use of local points of interest and SIFT descriptor. Once the generated features are encoded using SIFT descriptor, matching is carried out using RANSAC to estimate a transformation model and establish the number of its inliers which is then multiplied by the sum of point-to-point Euclidean distances below a hard threshold. We demonstrate that such combination can overcome the issue of retrieval of partial prints in the presence of rotation and noise distortions. Conducted experiments have shown that the proposed solution achieves very good matching results and outperforms similar work in the literature both in terms of performances and complexity.
Search for a substring of characters using the theory of non-deterministic fi...journalBEEI
The paper proposed an algorithm which purpose is searching for a substring of characters in a string. Principle of its operation is based on the theory of non-deterministic finite automata and vector-character architecture. It is able to provide the linear computational complexity of searching for a substring depending on the length of the searched string measured in the number of operations with hyperdimensional vectors when repeatedly searching for different strings in a target line. None of the existing algorithms has such a low level of computational complexity. The disadvantages of the proposed algorithm are the fact that the existing hardware implementations of computing systems for performing operations with hyperdimensional vectors require a large number of machine instructions, which reduces the gain from this algorithm. Despite this, in the future, it is possible to create a hardware implementation that can ensure the execution of operations with hyperdimensional vectors in one cycle, which will allow the proposed algorithm to be applied in practice.
Hybrid iterated local search algorithm for optimization route of airplane tr...IJECEIAES
The traveling salesman problem (TSP) is a very popular combinatorics problem. This problem has been widely applied to various real problems. The TSP problem has been classified as a Non-deterministic Polynomial Hard (NP-Hard), so a non-deterministic algorithm is needed to solve this problem. However, a non-deterministic algorithm can only produce a fairly good solution but does not guarantee an optimal solution. Therefore, there are still opportunities to develop new algorithms with better optimization results. This research develops a new algorithm by hybridizing three local search algorithms, namely, iterated local search (ILS) with simulated annealing (SA) and hill climbing (HC), to get a better optimization result. This algorithm aimed to solve TSP problems in the transportation sector, using a case study from the Traveling Salesman Challenge 2.0 (TSC 2.0). The test results show that the developed algorithm can optimize better by 15.7% on average and 11.4% based on the best results compared to previous studies using the TabuSA algorithm.
Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...IJERA Editor
An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The
parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile
robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for
mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding
local minima. It gives the optimum paths which are always consisting on free trajectories. But the
proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to
destination due to the sharing of population. The total population is partitioned into a number subgroups to
perform the parallel GA. The master thread is the center of information exchange and making selection with
fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges
quickly with in a less number of iteration.
A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuratio...CSCJournals
This paper presents the implementation of a novel technique for sensor based path planning of autonomous mobile robots. The proposed method is based on finding free-configuration eigen spaces (FCE) in the robot actuation area. Using the FCE technique to find optimal paths for autonomous mobile robots, the underlying hypothesis is that in the low-dimensional manifolds of laser scanning data, there lies an eigenvector which corresponds to the free-configuration space of the higher order geometric representation of the environment. The vectorial combination of all these eigenvectors at discrete time scan frames manifests a trajectory, whose sum can be treated as a robot path or trajectory. The proposed algorithm was tested on two different test bed data, real data obtained from Navlab SLAMMOT and data obtained from the real-time robotics simulation program Player/Stage. Performance analysis of FCE technique was done with existing four path planning algorithms under certain working parameters, namely computation time needed to find a solution, the distance travelled and the amount of turning required by the autonomous mobile robot. This study will enable readers to identify the suitability of path planning algorithm under the working parameters, which needed to be optimized. All the techniques were tested in the real-time robotic software Player/Stage. Further analysis was done using MATLAB mathematical computation software.
LOGNORMAL ORDINARY KRIGING METAMODEL IN SIMULATION OPTIMIZATIONorajjournal
This paper presents a lognormal ordinary kriging (LOK) metamodel algorithm and its application to
optimize a stochastic simulation problem. Kriging models have been developed as an interpolation method
in geology. They have been successfully used for the deterministic simulation optimization (SO) problem. In
recent years, kriging metamodeling has attracted a growing interest with stochastic problems. SO
researchers have begun using ordinary kriging through global optimization in stochastic systems. The
goals of this study are to present LOK metamodel algorithm and to analyze the result of the application
step-by-step. The results show that LOK is a powerful alternative metamodel in simulation optimization
when the data are too skewed.
This paper presentsa novel data flow architecturethat utilizes data from engineering simulations to
generate a reduced order model within Apache Spark. The reduced order model from Spark is then utilized by
anevolutionary algorithm in the optimization of an industrial system component. This work is presented in the
context of the shape optimization of a heat exchanger fin and demonstrates the ability of theengineering
simulation, the reduced order model and the evolutionary algorithm to exchange data with each other by
utilizing Spark as the common data-processing framework. In order to enable a user to monitor the input design
parameter space,self-organizing maps are generated for visualization. The results of theevolutionary
optimization utilizing this data flow are compared with results from invoking high-fidelity engineering
simulations. This novel data flow architecture decouples the evolutionary algorithm from the reduced order
model and allows improvement of the optimization results by continuously augmenting the reduced order model
with data from the evolutionary algorithm.Additionally, when constraints on the optimization algorithm are
modifiedthe evolutionary algorithm canadapt and evolve good solutions. Themethodology presented in this
articlealso makes it feasible to simultaneously tune evolutionary optimization experiments along with
engineering simulations at a relatively low computational cost.
A MODIFIED VORTEX SEARCH ALGORITHM FOR NUMERICAL FUNCTION OPTIMIZATIONijaia
The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was
inspired from the vortical flow of the stirred fluids. Although the VS algorithm is shown to be a good
candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS
algorithm, candidate solutions are generated around the current best solution by using a Gaussian
distribution at each iteration pass. This provides simplicity to the algorithm but it also leads to some
problems along. Especially, for the functions those have a number of local minimum points, to select a
single point to generate candidate solutions leads the algorithm to being trapped into a local minimum
point. Due to the adaptive step-size adjustment scheme used in the VS algorithm, the locality of the created
candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local
point as quickly as possible, it becomes much more difficult for the algorithm to escape from that point in
the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to overcome
above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions
are generated around a number of points at each iteration pass. Computational results showed that with
the help of this modification the global search ability of the existing VS algorithm is improved and the
MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark
numerical function set.
Parallel sorting algorithms order a set of elements USING MULTIPLE processors in order to enhance the performance of sequential sorting algorithms. In general, the performance of sorting algorithms are EVALUATED IN term of algorithm growth rate according to the input size. In this paper, the running time, parallel speedup and parallel efficiency OF PARALLEL bubble sort is evaluated and measured. Message Passing Interface (MPI) IS USED for implementing the parallel version of bubble sort and IMAN1 supercomputer is used to conduct the results. The evaluation results show that parallel bubble sort has better running time as the number of processors increases. On other hand, regarding parallel efficiency, parallel bubble sort algorithm is more efficient to be applied OVER SMALL number of processors.
GENETIC ALGORITHM FOR FUNCTION APPROXIMATION: AN EXPERIMENTAL INVESTIGATIONijaia
Function Approximation is a popular engineering problems used in system identification or Equation
optimization. Due to the complex search space it requires, AI techniques has been used extensively to spot
the best curves that match the real behavior of the system. Genetic algorithm is known for their fast
convergence and their ability to find an optimal structure of the solution. We propose using a genetic
algorithm as a function approximator. Our attempt will focus on using the polynomial form of the
approximation. After implementing the algorithm, we are going to report our results and compare it with
the real function output.
The Traveling salesman problem (TSP) is proved to be NP-complete in most cases. The genetic algorithm
(GA) is one of the most useful algorithms for solving this problem. In this paper a conventional GA is
compared with an improved hybrid GA in solving TSP. The improved or hybrid GA consist of
conventional GA and two local optimization strategies. The first strategy is extracting all sequential
groups including four cities of samples and changing the two central cities with each other. The second
local optimization strategy is similar to an extra mutation process. In this step with a low probability a
sample is selected. In this sample two random cities are defined and the path between these cities is
reversed. The computation results show that the proposed method also finds better paths than the
conventional GA within an acceptable computation time.
Path Finding Solutions For Grid Based Graphacijjournal
Any path finding will work as long as there are no obstacles on distractions along the way. A genetic A*
algorithm has been used for more advanced environments in graph. Implementation of the path finding
algorithm for grid based graph with or without obstacles.
FORECASTING ENERGY CONSUMPTION USING FUZZY TRANSFORM AND LOCAL LINEAR NEURO F...ijsc
This paper proposes a hybrid approach based on local linear neuro fuzzy (LLNF) model and fuzzy transform (F-transform), termed FT-LLNF, for prediction of energy consumption. LLNF models are powerful in modelling and forecasting highly nonlinear and complex time series. Starting from an optimal
linear least square model, they add nonlinear neurons to the initial model as long as the model's accuracy is improved. Trained by local linear model tree learning (LOLIMOT) algorithm, the LLNF models provide maximum generalizability as well as the outstanding performance. Besides, the recently introduced technique of fuzzy transform (F-transform) is employed as a time series pre-processing method. The
technique of F-transform, established based on the concept of fuzzy partitions, eliminates noisy variations of the original time series and results in a well-behaved series which can be predicted with higher accuracy. The proposed hybrid method of FT-LLNF is applied to prediction of energy consumption in the
United States and Canada. The prediction results and comparison to optimized multi-layer perceptron (MLP) models and the LLNF itself, revealed the promising performance of the proposed approach for energy consumption prediction and its potential usage for real world applications.
Rotation Invariant Matching of Partial ShoeprintsIJERA Editor
In this paper, we propose a solution for the problem of rotated partial shoeprints retrieval based on the combined use of local points of interest and SIFT descriptor. Once the generated features are encoded using SIFT descriptor, matching is carried out using RANSAC to estimate a transformation model and establish the number of its inliers which is then multiplied by the sum of point-to-point Euclidean distances below a hard threshold. We demonstrate that such combination can overcome the issue of retrieval of partial prints in the presence of rotation and noise distortions. Conducted experiments have shown that the proposed solution achieves very good matching results and outperforms similar work in the literature both in terms of performances and complexity.
Search for a substring of characters using the theory of non-deterministic fi...journalBEEI
The paper proposed an algorithm which purpose is searching for a substring of characters in a string. Principle of its operation is based on the theory of non-deterministic finite automata and vector-character architecture. It is able to provide the linear computational complexity of searching for a substring depending on the length of the searched string measured in the number of operations with hyperdimensional vectors when repeatedly searching for different strings in a target line. None of the existing algorithms has such a low level of computational complexity. The disadvantages of the proposed algorithm are the fact that the existing hardware implementations of computing systems for performing operations with hyperdimensional vectors require a large number of machine instructions, which reduces the gain from this algorithm. Despite this, in the future, it is possible to create a hardware implementation that can ensure the execution of operations with hyperdimensional vectors in one cycle, which will allow the proposed algorithm to be applied in practice.
Hybrid iterated local search algorithm for optimization route of airplane tr...IJECEIAES
The traveling salesman problem (TSP) is a very popular combinatorics problem. This problem has been widely applied to various real problems. The TSP problem has been classified as a Non-deterministic Polynomial Hard (NP-Hard), so a non-deterministic algorithm is needed to solve this problem. However, a non-deterministic algorithm can only produce a fairly good solution but does not guarantee an optimal solution. Therefore, there are still opportunities to develop new algorithms with better optimization results. This research develops a new algorithm by hybridizing three local search algorithms, namely, iterated local search (ILS) with simulated annealing (SA) and hill climbing (HC), to get a better optimization result. This algorithm aimed to solve TSP problems in the transportation sector, using a case study from the Traveling Salesman Challenge 2.0 (TSC 2.0). The test results show that the developed algorithm can optimize better by 15.7% on average and 11.4% based on the best results compared to previous studies using the TabuSA algorithm.
Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...IJERA Editor
An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The
parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile
robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for
mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding
local minima. It gives the optimum paths which are always consisting on free trajectories. But the
proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to
destination due to the sharing of population. The total population is partitioned into a number subgroups to
perform the parallel GA. The master thread is the center of information exchange and making selection with
fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges
quickly with in a less number of iteration.
A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuratio...CSCJournals
This paper presents the implementation of a novel technique for sensor based path planning of autonomous mobile robots. The proposed method is based on finding free-configuration eigen spaces (FCE) in the robot actuation area. Using the FCE technique to find optimal paths for autonomous mobile robots, the underlying hypothesis is that in the low-dimensional manifolds of laser scanning data, there lies an eigenvector which corresponds to the free-configuration space of the higher order geometric representation of the environment. The vectorial combination of all these eigenvectors at discrete time scan frames manifests a trajectory, whose sum can be treated as a robot path or trajectory. The proposed algorithm was tested on two different test bed data, real data obtained from Navlab SLAMMOT and data obtained from the real-time robotics simulation program Player/Stage. Performance analysis of FCE technique was done with existing four path planning algorithms under certain working parameters, namely computation time needed to find a solution, the distance travelled and the amount of turning required by the autonomous mobile robot. This study will enable readers to identify the suitability of path planning algorithm under the working parameters, which needed to be optimized. All the techniques were tested in the real-time robotic software Player/Stage. Further analysis was done using MATLAB mathematical computation software.
EMBC'13 Poster Presentation on "A Bio-Inspired Cooperative Algorithm for Dist...Md Kafiul Islam
In this paper we propose an algorithm for distributed
optimization in mobile nodes. Compared with many
published works, an important consideration here is that the
nodes do not know the cost function beforehand. Instead of
decision-making based on linear combination of the neighbor
estimates, the proposed algorithm relies on information-rich
nodes that are iteratively identified. To quickly identify the
information rich node, the algorithm adopts a larger step size
during the initial iterations. The proposed algorithm can be used
in many different applications, such as distributed odor source
localization and mobile robots. We present simulation results to
show the performance of our proposed algorithm
With the widespread of smart mobile devices and the
availability of many applications that provide maps, many programs
have spread to find the closest and fastest routes between
two points on the map. While the exactness and effectiveness of
best path depend on the traffic circumstances, the system needs to
add more parameters such as real traffic density and velocity in
road. In addition, because of the restricted resources of phone devices,
it is not reasonable to be used to calculate the exact optimal
solutions by some familiar deterministic algorithms, which are
usually used to find the shortest path with a map of reasonable
node number. To resolve this issue, this paper put forward to use
the genetic algorithm to reduce the computational time. The proposed
system use the genetic algorithm to find the shortest path
time with miscellaneous situations of real traffic conditions. The
genetic algorithm is clearly demonstrate excellent result when applied
on many types of map, especially when the number of nodes
increased.
Proposing a New Job Scheduling Algorithm in Grid Environment Using a Combinat...Editor IJCATR
Grid computing is a hardware and software infrastructure and provides affordable, sustainable, and reliable access. Its aim is
to create a supercomputer using free resources. One of the challenges to the Grid computing is scheduling problem which is regarded
as a tough issue. Since scheduling problem is a non-deterministic issue in the Grid, deterministic algorithms cannot be used to improve
scheduling. In this paper, a combination of imperialist competition algorithm (ICA) and gravitational attraction is used for to address the
problem of independent task scheduling in a grid environment, with the aim of reducing the makespan and energy. Experimental results
compare ICA with other algorithms and illustrate that ICA finds a shorter makespan and energy relative to the others. Moreover, it
converges quickly, finding its optimum solution in less time than the other algorithms.
Information Spread in the Context of Evacuation OptimizationDr. Mirko Kämpf
Abstract: Our evacuation simulation tool utilizes established algorithms for the emotional and intelligence driven motion of human beings in addition to a simple lattice gas simulation. We analyze the spread of information inside a restricted geometry of a real building and compare these results with the data from a simulation in the free space. We apply the DFA and the RIS statistic to our simulation dataset to detect phases or phase transitions of the whole system. We study the impact of communication technology by comparison of different update algorithms and exit strategies. These results help us to define basic functional requirements to the underlying communication technology and network topology as well as to the needed sensors.
The objective of path planning algorithms is to find the optimal path from a source position to a target position. This paper proposes a real-time path planner for UAVs based on the genetic algorithm. The proposed approach does not identify any specific points outside or between obstacles to solve the problems of the invisible path. In addition, this approach uses no additional steps in the genetic algorithm to handle the problems resulting from generating points inside the obstacles, or the intersection between path segments with obstacles. For these reasons, this paper introduces a simple evaluation method that takes into account the intersections between the path segments and obstacles to find a collision-free and near to optimal path. This evaluation method take into account overlapped and intersected obstacles. The sequential implementation for all of the genetic algorithm steps is detailed. This paper explores the Parallel Genetic Algorithms (PGA) models and introduces the parallel implementation of the proposed path planner on multi-core processors using OpenMP. The execution time of the proposed parallel implementation is reduced compared to sequential execution.
UAV PATH PLANNING USING GENETIC ALGORITHMWITH PARALLEL IMPLEMENTATIONijcsity
The objective of path planning algorithms is to find the optimal path from a source position to a target
position. This paper proposes a real-time path planner for UAVs based on the genetic algorithm. The
proposed approach does not identify any specific points outside or between obstacles to solve the problems
of the invisible path. In addition, this approach uses no additional steps in the genetic algorithm to handle
the problems resulting from generating points inside the obstacles, or the intersection between path
segments with obstacles. For these reasons, this paper introduces a simple evaluation method that takes
into account the intersections between the path segments and obstacles to find a collision-free and near to
optimal path. This evaluation method take into account overlapped and intersected obstacles. The sequential
implementation for all of the genetic algorithm steps is detailed. This paper explores the Parallel Genetic
Algorithms (PGA) models and introduces the parallel implementation of the proposed path planner on
multi-core processors using OpenMP. The execution time of the proposed parallel implementation is
reduced compared to sequential execution.
Robot navigation in unknown environment with obstacle recognition using laser...IJECEIAES
Robot navigation in unknown and dynamic environments may result in aimless wandering, corner traps and repetitive path loops. To address these issues, this paper presents the solution by comparing the standard deviation of the distance ranges of the obstacles appeared in the robot navigation path. For the similar obstacles, The standard deviations of distance range vectors, obtained from the laser range finder sensor of the robot at similar pose, are very close to each other. Therefore, the measurements of odometer sensor are also combined with the standard deviation to recognize the location of the obstacles. A novel algorithm, with obstacle detection feature, is presented for robot navigation in unknown and dynamic environments. The algorithm checks the similarity of the distance range vectors of the obstacles in the path and uses this information in combination with the odometer measurements to identify the obstacles and their locations. The experimental work is carried out using Gazebo simulator.
The optimization of running queries in relational databases using ant colony ...ijdms
The issue of optimizing queries is a cost-sensitive
process and with respect to the number of associat
ed
tables in a query, its number of permutations grows
exponentially. On one hand, in comparison with oth
er
operators in relational database, join operator is
the most difficult and complicated one in terms of
optimization for reducing its runtime. Accordingly,
various algorithms have so far been proposed to so
lve
this problem. On the other hand, the success of any
database management system (DBMS) means
exploiting the query model. In the current paper, t
he heuristic ant algorithm has been proposed to sol
ve this
problem and improve the runtime of join operation.
Experiments and observed results reveal the efficie
ncy
of this algorithm compared to its similar algorithm
s.
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NON - EUCLIDEAN METRIC AND PATH PLANNING
1. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.1, February 2016
DOI:10.5121/ijcses.2016.7102 13
NON - EUCLIDEAN METRIC AND PATH PLANNING
Edvards Valbahs1
and Peter Grabusts2
Rezekne Academy of Technologies, Rezekne, Latvia
ABSTRACT
In order to achieve the wide range of the robotic application it is necessary to provide iterative motions
among points of the goals. For instance, in the industry mobile robots can replace any components between
a storehouse and an assembly department. Ammunition replacement is widely used in military services.
Working place is possible in ports, airports, waste site and etc. Mobile agents can be used for monitoring if
it is necessary to observe control points in the secret place. The paper deals with path planning programme
for mobile robots. The aim of the research paper is to analyse motion-planning algorithms that contain the
design of modelling programme. The programme is needed as environment modelling to obtain the
simulation data. The simulation data give the possibility to conduct the wide analyses for selected
algorithm. Analysis means the simulation data interpretation and comparison with other data obtained
using the motion-planning. The results of the careful analysis were considered for optimal path planning
algorithms. The experimental evidence was proposed to demonstrate the effectiveness of the algorithm for
steady covered space. The results described in this work can be extended in a number of directions, and
applied to other algorithms.
KEYWORDS
Path Planning, Robotic, Simulated Annealing.
1. INTRODUCTION
The article is connected to the travelling salesman problem (TSP), but with some exceptions and
conditions. In the case when the TSP is envisaged the following approximate path planning
algorithms are used [1, 2, 3]:
• The closest neighbour algorithm;
• Simulated Annealing (SA);
• Genetic Algorithm (GA);
• Ant colony optimization.
The closest neighbour approach is the simplest and straightforward TSP one [4]. The way to this
approach to always visit the closest city. The polynomial complexity of the approach is O(n2
).
The algorithm is relatively simple:
• choose a random city;
• find out the nearest city unvisited and visit it;
• are there any unvisited cities left? If yes, repeat step 2;
• return to the first city.
SA is successfully used and adapted to get an approximate solutions for the TSP [4]. SA is
basically a randomized local search algorithm similar to Tabu Search but do not allow path
exchange that deteriorates the solution. The polynomial complexity of the approach is O(n2
)
accordingly.
2. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.1, February 2016
14
Input: ProblemSize, iterationsmax, tempmax
Output:Sbest
1. Scurrent←CreateInitialSolution(ProblemSize)
2. Sbest←Scurrent
3. for i=1 to iterationsmax do
4. Si←CreateNeighborSolution(Scurrent)
5. tempcurr←CalculateTemperature(i, tempmax)
6. if Cost(Si)≤Cost(Scurrent) then
7. Scurrent←Si
8. if Cost(Si) ≤ Cost(Sbest) then
9. Sbest←Si
10. end
11. else if Exp((Cost(Scurrent)-Cost(Si))/tempcurr)>Rand() then
12. Scurrent←Si
13. end
14. end
15. return Sbest
Figure 1. Pseudocode for SA
The SA method [5, 6] is widely used in applied science (see Figure 1). The well-known traveling
salesman problem has effectively solved by means of this method. For instance, the arrangement
of many circuit elements on a silicon substrate is considerably improved to reduce interference
among the elements [7, 8].
GA conducts in a way similar to the nature [2]. A basic GA starts working with a randomly
generated population of potential solution. The candidates are then mated to produce offspring
and only some of them go through a mutating process. Each candidate has an optimal value
demonstrating us how go it is. Choosing the most optimal candidates for mating and mutation the
overall optimality of the candidate solutions increases. Using GA to the TSP involves
implementing a crossover procedure, a measure of optimality and mutation as well. Optimality of
the solution is a length of the solution.
Ant colony optimization is the algorithm that is inspired by the nature [9]. It is based on ant
colony moving behaviour. Good results can be achieved by means of the algorithm but not for
complex problems.
We managed to use SA algorithm rather successfully in our previous work [10] taking into
account the specific side of this work (it will be discussed in detail further). Therefore, it is
necessary to discuss some principles of SA realization in detail. In order to calculate the total path
it is necessary to know the shortest route among all the cities. As we do not know the distance, we
must apply one of the algorithms to define the shortest route among all the cities. It is Dijkstra's
algorithm [11] that gives the possibility to get the shortest path tree. The polynomial complexity
of the Dijkstra's algorithm is O(n2
).
2. GOALS
The aim of the research paper is to analyze motion-planning algorithms that contain the design of
modelling programme. The programme is needed as environment modelling to obtain the
simulation data. The simulation data give the possibility to conduct the wide analyses for selected
algorithm. Analysis means the simulation data interpretation and comparison with other data
obtained using the motion-planning.
The use in practice and the necessity of it is greatly connected to optimal algorithm and
methodological work out for autonomous agents that move in the space and are able to plan route
3. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.1, February 2016
15
on their own [12, 13, 14, 15, 16, 17]. One of such agent-samples exiting in our everyday life is
autonomous vacuum cleaner. Autonomous vacuum cleaners do not usually use the motion-
planning algorithm. They are based on some simple algorithms, for example cleaning in a spiral,
crossing the premises avoiding the walls and their moving is casual after touching the walls. The
philosophy of this design was offered by the scientists of Massachusetts Institute of Technology.
Agents must behave as insects having primitive controlling devices in accordance to the
environment. As a result, though an autonomous vacuum cleaner is very effective in cleaning
premises, it is required much more time as compared with work made by a human. There is a
drawback, the autonomous vacuum cleans some spaces many times but other spaces only once.
The use of motion-planning algorithms can raise the effectiveness of an autonomous vacuum
cleaner.
3. ASSUMPTIONS
In order to fulfill the aim of the research paper the following conditions are introduced for:
• premises where an object moves;
• robot (or object) moves around the premises;
• path the robot moves on in the premises.
The premises are presented as two-dimensional plane. The plane of premises is equally divided
into the cells. The cell dimensions are equal to agent dimension that moves in the premises. The
space can be represented as a graph with two kinds of edges (see Figure 2). Horizontal and
vertical edges are marked with unbroken lines they are of similar length, but others are longer and
marked with dash lines. It is linked with agent movement possibilities.
Figure 2. The example of the graph and 3 x 3 space
The object moves only one cell forward and back i.e. during one motion the object can move to
the one cell from empty eight ones (eight cells around one cell) paying attention to that cell is not
occupied by the obstacle but if it is occupied, the graph will not have the relevant vertex (see
Figure 3).
Figure 3. The example of agent’s motion (where vi,j is relevant vertex)
4. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.1, February 2016
16
As opposed to classical TSP we take a number of additional rules and it means that the agent can
cross the one the same cell several times in succession (it must cross any cell one time
obligatory). Thus, the agent’s initial vertex does not coincide with its final vertex of total route.
In this research paper both algorithms were compared practically using and combining different
placement of obstacles in the unchangeable two-dimensional space. All the results were obtained
on one and the same computer (2.66 GHz processor and 2GB RAM), operating systems (Ubuntu
15.04 Linux were used). The following information was collected about:
• the number of covering for each cell;
• the time which was necessary for both algorithms to plan the route.
The given illustrations (see Figure 5) show coverage density (it is an example that was obtained
in our previous work [10]).
Figure 4. Density scale (white - uncovered; black - covered the most)
The density scale (see Figure 4) is the same for all coverage densities. Coverage density shows
how often the robot covers each cell.
Figure 5. Coverage density for the space without obstacles for SA
4. MATHEMATICAL ANALYSIS
Let’s discuss Euclidian (or hypotenuse of right-angled triangle) and non-Euclidian (calculated
with offered/developed approach) distance between two graph points that are mutually moved
along vertical and longitude (see Figure 6).
5. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.1, February 2016
17
Figure 6. Example for agents moving from junction v0,3 to junction v5,0
Let’s label the distance calculated by means of non-Euclidian geometry with L, then L = K +
20.5
*A. You can see that these distances numerically differ and not always are equal. Usually L is
a bit bigger than Euclidian distance (or hypotenuse C). L matches with Euclidian distance at
following conditions (see Figure 6):
• if catheti of a triangle are equal and then K = 0 (in case of equilateral triangle);
• or junctions are not mutually moved along vertical or longitude. For example if one of the
catheti of triangle is equal with zero.
To assess numerically the difference between both distances, you can invent the following
coherence: L corresponds to 100% and we have to calculate for how many percent L is bigger
than C, i.e., how big percent refers to (L – C) difference (let’s label the difference percent from L
with P, then P = ((L-C)*100)/L). Differences and L values depend on K and A values. Then P
dependence on K and A can be expressed with a function with two arguments (see Figure 7).
Figure 7. Example for agents moving from junction v0,3 to junction v5,0
If A = const, then P is a function with one argument that depends on K (see Figure 8).
6. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.1, February 2016
18
5 10 15 20 25 30
1
2
3
4
5
6
7
Figure 8. P dependence on K, if A = 1
You can see that at all A values maximum P value is equal with ~7.612%. You can express
(analytically) K value from the following equitation (considering that L = K + 20.5
*A): (((K +
20.5
*A)-C)*100)/( K + 20.5
*A) = 7.611. Solution of equitation is quite simple: K = 1.414*A (it
must be marked that K is a whole number, i.e., it will always be bigger or smaller that the given
value). You can also calculate value of angle α at the maximum value P that will remain constant
irrespective of value A. Considering that K is equal with 1.414*A, then α = arcsin((A +
1.414*A)/(((A + 1.414*A)2
) + A2
)0.5
) = arcsin(2.414*A/(5.827*A2
+A2
)0.5
) =
arcsin(2.414*A/(6.827*A2
)0.5
) = arcsin(2.414*A/2.613*A) = arcsin(2.414/2.613). After all
simplifications angle α is equal with 67.65O
.
5. RESULTS
Taking into account the fact that the distance among all the vertexes (cities) are unknown in the
beginning, it is necessary to define the shortest paths among those vertexes mentioned above.
Dijkstra's algorithm can be used but increasing the measures of the premises, the time is
proportionally increases accordingly that is necessary for evaluating path tree. Therefore, it is
needed to simplify the calculation of the shortest path, which is possible, provided the
peculiarities and nuances of the task are taken into consideration. In addition, the empty premises
should be observed. If all the mentioned above remains valid, the simple algorithm can be worked
out to define the shortest paths.
Let us consider the agent’s general moving paths. If there are no vertexes between the current
initial and goal vertexes, the agent can move only to eight possible positions (cells) depending on
goal vertex (see Figure 3). Admitting that first vertex index i define the vertical position and the
second vertex j defines the horizontal position we can draw a line either horizontally or vertically.
And one of the vertexes will have the index with common value (see Figure 9).
Figure 9. The example of agent moving horizontally (where i index value is common for both vertexes)
Another situation can be seen if the current initial and goal vertexes are neither on the horizontal
nor vertical lines (see Figures 10-12).
7. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.1, February 2016
19
Figure 10. Three examples of agent moving (where A, B and C are sections among the vertexes): agent
moves from v0,2 to v3,0 crossing v1,2
Figure 11. Three examples of agent moving (where A, B and C are sections among the vertexes): agent
moves from v0,2 to v4,0 crossing v2,2
Figure 12. Three examples of agent moving (where A, B and C are sections among the vertexes agent
moves from v0,3 to v4,0 crossing v1,3)
All cases of Figures 10-12 have a common characteristic that unites them. The shortest path from
initial vertex to goal vertex is section C but for the agent this path is unavailable because of
current task conditions and peculiarities. These cases can be described by the right-angled triangle
where C is a side of the triangle. In addition, side B is longer than side A. One of the shortest
paths among the relevant (corresponding) vertexes:
• the agent moves along the longest side B of the right-angled triangle until the gap
between the covered path and side B is equal to side A;
8. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.1, February 2016
20
• if gap between the covered path and side B is equal to side A, then the agent moves along
the angle allowed (along the section D) to the goal vertex (let us mark that this action
corresponds to the case when side B is equal to side A i.e. the right-angled triangle is the
isosceles triangle, too (see Figure 10) in case initial vertex is v1,2, 4, (see Figure 8) in case
initial vertex is v2,2 and (see Figure 12) in case initial vertex is v1,3)).
We can follow that the path is longer than optimal side C. And it can be calculated by the use of
following formulae: L = B-A+20.5
*A, where L is the length of the path from initial vertex to goal
vertex. By turn, C can be calculated from C = (A2
+B2
)0.5
. It is possible to calculate how match
percent L is longer than C (if L is equal to 100 %), then the final result is equal to P=((L-
C)*100)/L. Our goal premises are 100 x 100 cells. The value of P is reflected with contour line
for the given premises depending on A and B (see Figure 13).
The method/algorithm mentioned was applied instead of Dijkstra's algorithm to calculate total
path or covering of 100 x 100 cells big premises and it is obstacles free (see Figure 14).
Figure 13. P value depending on B and A, if A > 1 and B > A
.
Figure 14. The density of covering for 100 x 100 cells big
Density of covering changes from 1 up to 40 (there are the cells which were covered only once
and there are the cells covered maximum 40 times). Totally, the agent performed 192666 steps in
order to cross each cell of the premises.
9. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.1, February 2016
21
6. COMPARISON WITH OTHER SOLUTIONS
This section presents several search algorithms. Most of these are just classical graph search
algorithms [18, 19].
Breadth first – selects states using the first-come, first-serve principle. This causes the search
frontier to grow uniformly and is therefore referred to as breadth-first search. The asymptotic
running time of breadth-first search is O(|V|+|E|), in which |V| and |E| are the numbers of vertices
and edges, respectively, in the state transition graph [19].
Depth first – the resulting variant is called depth-first search because the search dives quickly into
the graph. The running time of depth first search is also O(|V|+|E|) [19].
Dijkstra’s algorithm – systematic search algorithm that finds optimal plans because it is also
useful for finding feasible plans. The running time is O(|V| lg |V|+|E|), in which |V| and |E| are the
numbers of edges and vertices, respectively, in the graph representation of the discrete planning
problem [19].
A-star algorithm – works in exactly the same way as Dijkstra’s algorithm. The only difference is
the function used to sort a priority queue. It has some advantages, but in some problems it is
difficult or impossible to find a heuristic that is both efficient to evaluate and provides good
search guidance [19, 20, 21].
Best first – the priority queue is sorted according to an estimate of the optimal cost-to-go. The
solutions obtained in this way are not necessarily optimal; therefore, it does not matter whether
the estimate exceeds the true optimal cost-to-go, which was important to maintain optimality for
A∗ search. The worst-case performance of best-first search is worse than that of A∗ search and
dynamic programming [19].
Iterative deepening – approach is usually preferable if the search tree has a large branching factor.
This could occur if there are many actions per state and only a few states are revisited. The
iterative deepening method has better worst-case performance than breadth- first search for many
problems [19, 21].
Our approach – implementation is very simple according to the task (which is envisaged in the
chapter “Results” in detail). The running time is O(C), in which C is a constant. The value of
running time is bounded by a value that does not depend on the size of the input.
7. CONCLUSIONS
It can be concluded that the algorithm offered is rather simple and it replaced Dijkstra's algorithm
effectively according to the task. The algorithm allows decreasing the time of calculation, which
is necessary to define the shortest route among graph vertexes.
The shortest path can be defined in a simple way (even in such cases mentioned in Figures 7-9),
provided that it is necessary to know the gap between the indexes of initial and goal vertexes. For
instance, if initial vertex is vi1,j1 and goal vertex is vi2,j2, the first gap is ∆1=|i1-i2| and the second
gap is ∆2=|j1-j2|. As to the next step, it is needed to calculate the biggest gap between both the
gaps. The shortest path is equal to the biggest gap. For instance, Figure 7 reflects the shortest path
which occupies 4 cells, but in other cases (see Figures 8-9) it is 5 cells big.
10. International Journal of Computer Science & Engineering Survey (IJCSES) Vol.7, No.1, February 2016
22
It must be marked that total path can be a bit longer it is connected to the specific task which was
envisaged in the chapters “Assumptions” and “Results” in detail. The worst case can be evaluated
theoretically for the premises of 100 x 100 cells. If we take into consideration that the total route
will consist of path pieces, which are longer than 7.61 % in comparison with C value (see Figure
10), the total path will be longer than optimal 7.61 % (actually, it is the worst maximal variant.
We must pay attention to the fact that SA provides only approximate solution).
The algorithm can be successfully used e.g. in autonomous public transport restricted by means of
rules, technical requirements in autonomous robots and military equipment. In addition, the
algorithm can be used in various computer games where a path planning is done in dynamic
environment.
It is possible to conclude that the algorithm offered can be used in the different application areas
not only for path planning of a robot.
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AUTHORS
Edvards Valbahs was born in Riga, Latvia. He received his Mg.sc.ing. degree in Information Technology
from Riga Technical University in 2011. Since 2009 he has been working at A/S “Exigen Services Latvia”
as a Systems Analyst. He is a PhD student at Rezekne Academy of Technologies. His research interests
include neural networks and path planning algorithms. His current research focuses on techniques for path
planning.
Peter Grabusts was born in Rezekne, Latvia. He received his Dr.sc.ing. degree in
Information Technology from Riga Technical University in 2006. Since 2014 he is a
Professor at the Department of Computer Science, IEEE member. His research interests
include data mining technologies, neural networks and clustering methods. His current
research focuses on techniques for clustering and fuzzy clustering