This document discusses various artificial intelligence techniques for robot path planning, including ant colony optimization. It provides background on particle swarm optimization, genetic algorithms, tabu search, simulated annealing, reactive search optimization, and ant colony algorithms. It then proposes a solution for robotic path planning that uses ant colony optimization. The proposed solution involves defining a source and destination point for the robot, moving it forward one step at a time while checking for obstacles, having it take three steps back if an obstacle is encountered, and applying ant colony optimization algorithms to help the robot find an optimal path to bypass obstacles and reach the destination point.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A NEW APPROACH IN DYNAMIC TRAVELING SALESMAN PROBLEM: A HYBRID OF ANT COLONY ...ijmpict
Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we’re going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.
AN OPTIMUM TIME QUANTUM USING LINGUISTIC SYNTHESIS FOR ROUND ROBIN CPU SCHEDU...ijsc
In Round Robin CPU scheduling algorithm the main concern is with the size of time quantum and the increased waiting and turnaround time. Decision for these is usually based on parameters which are assumed to be precise. However, in many cases the values of these parameters are vague and imprecise.
The performance of fuzzy logic depends upon the ability to deal with Linguistic variables. With this intent, this paper attempts to generate an Optimal Time Quantum dynamically based on the parameters which are treated as Linguistic variables. This paper also includes Mamdani Fuzzy Inference System using Trapezoidal membership function, results in LRRTQ Fuzzy Inference System. In this paper, we present an algorithm to improve the performance of round robin scheduling algorithm. Numerical analysis based on LRRTQ results on proposed algorithm show the improvement in the performance of the system by reducing unnecessary context switches and also by providing reasonable turnaround time.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A NEW APPROACH IN DYNAMIC TRAVELING SALESMAN PROBLEM: A HYBRID OF ANT COLONY ...ijmpict
Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we’re going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.
AN OPTIMUM TIME QUANTUM USING LINGUISTIC SYNTHESIS FOR ROUND ROBIN CPU SCHEDU...ijsc
In Round Robin CPU scheduling algorithm the main concern is with the size of time quantum and the increased waiting and turnaround time. Decision for these is usually based on parameters which are assumed to be precise. However, in many cases the values of these parameters are vague and imprecise.
The performance of fuzzy logic depends upon the ability to deal with Linguistic variables. With this intent, this paper attempts to generate an Optimal Time Quantum dynamically based on the parameters which are treated as Linguistic variables. This paper also includes Mamdani Fuzzy Inference System using Trapezoidal membership function, results in LRRTQ Fuzzy Inference System. In this paper, we present an algorithm to improve the performance of round robin scheduling algorithm. Numerical analysis based on LRRTQ results on proposed algorithm show the improvement in the performance of the system by reducing unnecessary context switches and also by providing reasonable turnaround time.
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
Performance Analysis of Genetic Algorithm as a Stochastic Optimization Tool i...paperpublications3
Abstract: Engineering design problems are complex by nature because of their critical objective functions involving many variables and Constraints. Engineers have to ensure the compatibility with the imposed specifications keeping the manufacturing costs low. Moreover, the methodology may vary according to the design problem.
The main issue is to choose the proper tool for optimization. In the earlier days, a design problem was optimized by some of the conventional optimization techniques like gradient Search, evolutionary optimization, random search etc. These are known as classical methods.
The method is to be properly Chosen depending on the nature of the problem- an incorrect choice may sometimes fail to give the optimal solution. So the methods are less robust.
Now-a-days soft-computing techniques are being widely used for optimizing a function. These are more robust. Genetic algorithm is one such method. It is an effective tool in the realm of stochastic optimization (non-classical). The algorithm produces many strings and generation to reach the optimal point.
The main objective of the paper is to optimize engineering design problems using Genetic Algorithm and to analyze how the algorithm reaches the optima effectively and closely. We choose a mathematical expression for the objective function in terms of the design variables and optimize the same under given constraints using GA.
Optimal rule set generation using pso algorithmcsandit
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
The potential role of ai in the minimisation and mitigation of project delayPieter Rautenbach
Artificial intelligence (AI) can have wide reaching application within the construction
industry, however, the actual application of this set of technologies is currently under exploited. This
paper considers the role that the application of AI can take in optimising the efficiencies of project
execution and how this can potentially reduce project duration and minimise and mitigate delay on
projects.
Collocation Extraction Performance Ratings Using Fuzzy logicWaqas Tariq
The performance of Collocation extraction cannot quantified or properly express by a single dimension. It is very imprecise to interpret collocation extraction metrics without knowing what application (users) are involved. Most of the existing collocation extraction techniques are of Berry-Roughe, Church and Hanks, Kita, Shimohata, Blaheta and Johnson, and Pearce. The extraction techniques need to be frequently updated based on feedbacks from implementation of previous policies. These feedbacks are always stated in the form of ordinal ratings, e.g. “high speed”, “average performance”, “good condition”. Different people can describe different values to these ordinal ratings without a clear-cut reason or scientific basis. There is need for a way or means to transform vague ordinal ratings to more appreciable and precise numerical estimates. The paper transforms the ordinal performance ratings of some Collocation performance techniques to numerical ratings using Fuzzy logic. Keywords: Fuzzy Set Theory, collocation extraction, Transformation, performance Techniques, Criteria.
A performance analysis of metaheuristics and hybrid metaheuristics for the travel salesman problem is presented. Four single classical metaheuristics (genetic algorithm, memetic algorithm, iterated local search, and simulated annealing) were used. In addition, hybrid variations using nine different heuristic techniques for the local search, the mutation, and the intensification were used. The performance analysis was made using the Friedman test, and for the simulated annealing and local search algorithms statistical evidence was found that hybridization provides a difference in performance, while no evidence was found for the genetic and memetic algorithms. Up to six combinations were found to improve performance, five of them based on local search and one more based on simulated annealing.
Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Geneti...AI Publications
Modeling time series is often associated with the process forecasts certain characteristics in the next period. One of the methods forecasts that developed nowadays is using artificial neural network or more popularly known as a neural network. Use neural network in forecasts time series can be a good solution, but the problem is network architecture and the training method in the right direction. One of the choices that might be using a genetic algorithm. A genetic algorithm is a search algorithm stochastic resonance based on how it works by the mechanisms of natural selection and genetic variation that aims to find a solution to a problem. This algorithm can be used as teaching methods in train models are sent back propagation neural network. The application genetic algorithm and neural network for divination time series aim to get the weight optimum. From the training and testing on the data index share price euro 50 obtained by the RMSE testing 27.8744 and 39.2852 RMSE training. The weight or parameters that produced by has reached an optimum level in second-generation 1000 with the best fitness and the average 0.027771 the fitness of 0.0027847.Model is good to be used to give a prediction that is quite accurate information that is shown by the close target with the output.
MARKOV CHAIN AND ADAPTIVE PARAMETER SELECTION ON PARTICLE SWARM OPTIMIZERijsc
Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic
behavior of the PSO is not easy. The choosing of parameters plays an important role since it is critical in
the performance of PSO. As far as our investigation is concerned, most of the relevant researches are
based on computer simulations and few of them are based on theoretical approach. In this paper,
theoretical approach is used to investigate the behavior of PSO. Firstly, a state of PSO is defined in this
paper, which contains all the information needed for the future evolution. Then the memory-less property of
the state defined in this paper is investigated and proved. Secondly, by using the concept of the state and
suitably dividing the whole process of PSO into countable number of stages (levels), a stationary Markov
chain is established. Finally, according to the property of a stationary Markov chain, an adaptive method
for parameter selection is proposed.
EFFICIENT FEATURE SUBSET SELECTION MODEL FOR HIGH DIMENSIONAL DATAIJCI JOURNAL
This paper proposes a new method that intends on reducing the size of high dimensional dataset by
identifying and removing irrelevant and redundant features. Dataset reduction is important in the case of
machine learning and data mining. The measure of dependence is used to evaluate the relationship
between feature and target concept and or between features for irrelevant and redundant feature removal.
The proposed work initially removes all the irrelevant features and then a minimum spanning tree of
relevant features is constructed using Prim’s algorithm. Splitting the minimum spanning tree based on the
dependency between features leads to the generation of forests. A representative feature from each of the
forests is taken to form the final feature subset
A constraint is defined as a logical relation among several unknown quantities or variables, each taking a value in a given
domain. Constraint Programming (CP) is an emergent field in operations research. Constraint programming is based on feasibility
which means finding a feasible solution rather than optimization which means finding an optimal solution and focuses on the
constraints and variables domain rather than the objective functions. While defining a set of constraints, this may seem a simple way to
model a real-world problem but finding a good model that works well with a chosen solver is not that easy. A model could be very
hard to solve if it is poorly chosen
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
Performance Analysis of Genetic Algorithm as a Stochastic Optimization Tool i...paperpublications3
Abstract: Engineering design problems are complex by nature because of their critical objective functions involving many variables and Constraints. Engineers have to ensure the compatibility with the imposed specifications keeping the manufacturing costs low. Moreover, the methodology may vary according to the design problem.
The main issue is to choose the proper tool for optimization. In the earlier days, a design problem was optimized by some of the conventional optimization techniques like gradient Search, evolutionary optimization, random search etc. These are known as classical methods.
The method is to be properly Chosen depending on the nature of the problem- an incorrect choice may sometimes fail to give the optimal solution. So the methods are less robust.
Now-a-days soft-computing techniques are being widely used for optimizing a function. These are more robust. Genetic algorithm is one such method. It is an effective tool in the realm of stochastic optimization (non-classical). The algorithm produces many strings and generation to reach the optimal point.
The main objective of the paper is to optimize engineering design problems using Genetic Algorithm and to analyze how the algorithm reaches the optima effectively and closely. We choose a mathematical expression for the objective function in terms of the design variables and optimize the same under given constraints using GA.
Optimal rule set generation using pso algorithmcsandit
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
The potential role of ai in the minimisation and mitigation of project delayPieter Rautenbach
Artificial intelligence (AI) can have wide reaching application within the construction
industry, however, the actual application of this set of technologies is currently under exploited. This
paper considers the role that the application of AI can take in optimising the efficiencies of project
execution and how this can potentially reduce project duration and minimise and mitigate delay on
projects.
Collocation Extraction Performance Ratings Using Fuzzy logicWaqas Tariq
The performance of Collocation extraction cannot quantified or properly express by a single dimension. It is very imprecise to interpret collocation extraction metrics without knowing what application (users) are involved. Most of the existing collocation extraction techniques are of Berry-Roughe, Church and Hanks, Kita, Shimohata, Blaheta and Johnson, and Pearce. The extraction techniques need to be frequently updated based on feedbacks from implementation of previous policies. These feedbacks are always stated in the form of ordinal ratings, e.g. “high speed”, “average performance”, “good condition”. Different people can describe different values to these ordinal ratings without a clear-cut reason or scientific basis. There is need for a way or means to transform vague ordinal ratings to more appreciable and precise numerical estimates. The paper transforms the ordinal performance ratings of some Collocation performance techniques to numerical ratings using Fuzzy logic. Keywords: Fuzzy Set Theory, collocation extraction, Transformation, performance Techniques, Criteria.
A performance analysis of metaheuristics and hybrid metaheuristics for the travel salesman problem is presented. Four single classical metaheuristics (genetic algorithm, memetic algorithm, iterated local search, and simulated annealing) were used. In addition, hybrid variations using nine different heuristic techniques for the local search, the mutation, and the intensification were used. The performance analysis was made using the Friedman test, and for the simulated annealing and local search algorithms statistical evidence was found that hybridization provides a difference in performance, while no evidence was found for the genetic and memetic algorithms. Up to six combinations were found to improve performance, five of them based on local search and one more based on simulated annealing.
Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Geneti...AI Publications
Modeling time series is often associated with the process forecasts certain characteristics in the next period. One of the methods forecasts that developed nowadays is using artificial neural network or more popularly known as a neural network. Use neural network in forecasts time series can be a good solution, but the problem is network architecture and the training method in the right direction. One of the choices that might be using a genetic algorithm. A genetic algorithm is a search algorithm stochastic resonance based on how it works by the mechanisms of natural selection and genetic variation that aims to find a solution to a problem. This algorithm can be used as teaching methods in train models are sent back propagation neural network. The application genetic algorithm and neural network for divination time series aim to get the weight optimum. From the training and testing on the data index share price euro 50 obtained by the RMSE testing 27.8744 and 39.2852 RMSE training. The weight or parameters that produced by has reached an optimum level in second-generation 1000 with the best fitness and the average 0.027771 the fitness of 0.0027847.Model is good to be used to give a prediction that is quite accurate information that is shown by the close target with the output.
MARKOV CHAIN AND ADAPTIVE PARAMETER SELECTION ON PARTICLE SWARM OPTIMIZERijsc
Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic
behavior of the PSO is not easy. The choosing of parameters plays an important role since it is critical in
the performance of PSO. As far as our investigation is concerned, most of the relevant researches are
based on computer simulations and few of them are based on theoretical approach. In this paper,
theoretical approach is used to investigate the behavior of PSO. Firstly, a state of PSO is defined in this
paper, which contains all the information needed for the future evolution. Then the memory-less property of
the state defined in this paper is investigated and proved. Secondly, by using the concept of the state and
suitably dividing the whole process of PSO into countable number of stages (levels), a stationary Markov
chain is established. Finally, according to the property of a stationary Markov chain, an adaptive method
for parameter selection is proposed.
EFFICIENT FEATURE SUBSET SELECTION MODEL FOR HIGH DIMENSIONAL DATAIJCI JOURNAL
This paper proposes a new method that intends on reducing the size of high dimensional dataset by
identifying and removing irrelevant and redundant features. Dataset reduction is important in the case of
machine learning and data mining. The measure of dependence is used to evaluate the relationship
between feature and target concept and or between features for irrelevant and redundant feature removal.
The proposed work initially removes all the irrelevant features and then a minimum spanning tree of
relevant features is constructed using Prim’s algorithm. Splitting the minimum spanning tree based on the
dependency between features leads to the generation of forests. A representative feature from each of the
forests is taken to form the final feature subset
A constraint is defined as a logical relation among several unknown quantities or variables, each taking a value in a given
domain. Constraint Programming (CP) is an emergent field in operations research. Constraint programming is based on feasibility
which means finding a feasible solution rather than optimization which means finding an optimal solution and focuses on the
constraints and variables domain rather than the objective functions. While defining a set of constraints, this may seem a simple way to
model a real-world problem but finding a good model that works well with a chosen solver is not that easy. A model could be very
hard to solve if it is poorly chosen
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is an open access international journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Nature Inspired Models And The Semantic WebStefan Ceriu
In this paper we present a series of nature inspired models used as alternative solutions for Semantic Web concerns. Some of the methods presented in this article perform better than classic algorithms by enhancing response time and computational costs. Others are just proof of concept, first steps towards new techniques that will improve their respective field. The intricate nature of the Semantic Web urges the need for faster, more intelligent algorithms and nature inspired models have been proven to be more than suitable for such complex tasks.
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...IAEME Publication
Close range photogrammetry network design is referred to the process of placing a set of
cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find
the best location of two/three camera stations. The genetic algorithm optimization and Particle
Swarm Optimization are developed to determine the optimal camera stations for computing the three
dimensional coordinates. In this research, a mathematical model representing the genetic algorithm
optimization and Particle Swarm Optimization for the close range photogrammetry network is
developed. This paper gives also the sequence of the field operations and computational steps for this
task. A test field is included to reinforce the theoretical aspects.
To demonstrate our approaches we will use Sudoku puzzles, which are an excellent test bed for
evolutionary algorithms. The puzzles are accessible enough for people to enjoy. However the more complex
puzzles require thousands of iterations before an evolutionary algorithm finds a solution. If we were
attempting to compare evolutionary algorithms we could count their iterations to solution as an indicator
of relative efficiency. Evolutionary algorithms however include a process of random mutation for solution
candidates. We will show that by improving the random mutation behaviours we were able to solve
problems with minimal evolutionary optimisation. Experiments demonstrated the random mutation was at
times more effective at solving the harder problems than the evolutionary algorithms. This implies that the
quality of random mutation may have a significant impact on the performance of evolutionary algorithms
with Sudoku puzzles. Additionally this random mutation may hold promise for reuse in hybrid evolutionary
algorithm behaviours.
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...IOSR Journals
Abstract: Path planning and navigation is essential for an autonomous robot which can move avoiding the
static obstacles in a real world and to reach the specific target. Optimizing path for the robot movement gives
the optimal distance from the source to the target and save precious time as well. With the development of
various evolutionary algorithms, the differential evolution is taking the pace in comparison to genetic algorithm.
Differential evolution has been deployed quite successfully for solving global optimization problem. Differential
evolution is a very simple yet powerful metaheuristics type problem solving method. In this paper we are
proposing a Differential Evolution based path navigation algorithm for mobile path navigation and analyze its
efficiency with other developed approaches. The proposed algorithm optimized the robot path and navigates the
robot to the proper target efficiently.
Query Plan Generation using Particle Swarm OptimizationAkshay Jain
This presentation on “Generation of Query Plan using Particle Swarm Optimization.” On the database, there are number of queries which are spread across the world. Now in order to appendage these queries efficiently, best query processing techniques that generate best query processing plans are being formulated. In distributed relational database systems, due to replica of relations at various sites, the relations are required to answer a query may necessitate accessing of data from various sites. This leads to an extensive increase in the possible alternative query plans for evaluating a query. Though it is not actually feasible to find all possible query plans in such a large search space, the query plan that will be the most cost-efficacious option for processing a query is reckoned necessary and is finally given out for the given query.
Markov Chain and Adaptive Parameter Selection on Particle Swarm Optimizer ijsc
Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic behavior of the PSO is not easy. The choosing of parameters plays an important role since it is critical in the performance of PSO. As far as our investigation is concerned, most of the relevant researches are based on computer simulations and few of them are based on theoretical approach. In this paper, theoretical approach is used to investigate the behavior of PSO. Firstly, a state of PSO is defined in this paper, which contains all the information needed for the future evolution. Then the memory-less property of the state defined in this paper is investigated and proved. Secondly, by using the concept of the state and suitably dividing the whole process of PSO into countable number of stages (levels), a stationary Markov chain is established. Finally, according to the property of a stationary Markov chain, an adaptive method for parameter selection is proposed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
Genetic Algorithm (GA) is a robust and popular stochastic optimization algorithm for large and complex search spaces. The major shortcomings of Genetic Algorithms are premature convergence and revisits to individual solutions in the search space. In other words, Genetic algorithm is a revisiting algorithm that escorts to duplicate function evaluations which is a clear wastage of time and computational resources. In this paper, a non-revisiting genetic algorithm with adaptive mutation is proposed for the domain of MultiDimensional numeric function optimization. In this algorithm whenever a revisit occurs, the underlined search point is replaced with a mutated version of the best/random (chosen probabilistically) individual from the GA population. Furthermore, the recommended approach is not using any extra memory resources to avoid revisits. To analyze the influence of the method, the proposed non-revisiting algorithm is evaluated using nine benchmarks functions with two and four dimensions. The performance of the proposed genetic algorithm is superior as contrasted to simple genetic algorithm as confirmed by the experimental results.
GA is a search technique that depends on the natural selection and genetics principles and which determines a optimal solution for even a hard issue.genetic algorithm crossover and genetic algorithm for optimization
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
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Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
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UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
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GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
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1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
T01732115119
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 3, Ver. II (May – Jun. 2015), PP 115-119
www.iosrjournals.org
DOI: 10.9790/0661-1732115119 www.iosrjournals.org 115 | Page
Artificial Intelligence in Robot Path Planning
Pranav Reddy Kambam1
, Rahul Brungi2
, Prof. Gopichand G3
1
(Electronics and Instrumentation, VIT University, India)
2
(Computer Science, VIT University, India)
3
(Computer Science, VIT University, India)
Abstract: Mobile robot path planning problem is an important combinational content of artificial intelligence
and robotics. Its mission is to be independently movement from the starting point to the target point make robots
in their work environment while satisfying certain constraints. Constraint conditions are as follows: not a
collision with known and unknown obstacles, as far as possible away from the obstacle, sports the shortest path,
the shortest time, robot-consuming energy minimization and so on. In essence, the mobile robot path planning
problem can be seen as a conditional constraint optimization problem. To overcome this problem, ant colony
optimization algorithm is used.
Keywords: Particle Swarm Optimization (PSO), Genetic Algorithm(GA), Tabu Search, Simulated Annealing
(SA), Reactive Search Optimization (RSO), Proportional–Integral–Derivative(PID).
I. Introduction
Robot path planning is about finding a collision free motion from one position to another. Efficient
algorithms for solving problems of this type have important applications in areas such as: industrial robotics,
computer animation, drug design, and automated surveillance [1,2]. By representing synthetic, simulated
humans as robots, we can use motion planning algorithms to develop convincing computer generated animation.
There are many traditional techniques used in past in robot control such as PID. Problem with PID control is
that they perform process efficiently over very limited range of environment. It is very difficult to have highly
accurate performance especially at high speed of processes. This is because of PID control i.e. PID is linear and
not suitable for non-linear system with varying dynamic parameters and PID requires precise knowledge of
dynamic model. This may explain the dominant role of soft computing techniques in robotics. During the last
four decades, researchers have proposed many techniques for control and automation. There are various step
involved in designing of control system. These are modeling, analysis, simulation, implementation and
verification. In conventional/traditional techniques of control, the prime objectives had been precision and
uncertainty. However, in soft computing, the precision and certainty can be achieved by techniques of fuzzy
logic, neural network, evolutionary algorithm, and hybrid. The main emphasis of the paper is to explore the
efficient and accurate procedure based on soft-computing algorithm to provide the online learning mechanism
which performs better in dynamic, unstructured environment of robot [3]. Many techniques are used for this.
1.1 Particle Swarm Optimization (PSO)
Particle Swarm Optimization (PSO) is a computational method that optimizes a problem by iteratively
trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by
having a population of candidate solutions, with dubbed particles, and moving these particles around in the
search-space according to simple mathematical formulae over the particle's position and velocity. Each particle's
movement is influenced by its local best known position and is also guided toward the best known positions in
the search-space, which are updated as better positions are found by other particles. This is expected to move the
swarm toward the best solutions. PSO is a meta heuristic as it makes few or no assumptions about the problem
being optimized and can search very large spaces of candidate solutions. However, meta heuristics such as PSO
do not guarantee an optimal solution is ever found. More specifically, PSO does not use the gradient of the
problem being optimized, which means PSO does not require that the optimization problem be differentiable as
is required by classic optimization methods such as gradient descent and Quasi-Newton methods. PSO can
therefore also be used on optimization problems that are partially irregular, noisy, change over time.
1.2 Genetic Algorithm (GA)
A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This
heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms
belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems
using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. In a
genetic algorithm, a population of strings called chromosomes or the genotype of the genome, which encodes
candidate solutions called individuals, creatures, or phenotypes to an optimization problem, evolves toward
2. Artificial Intelligence in Robot Path Planning
DOI: 10.9790/0661-1732115119 www.iosrjournals.org 116 | Page
better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are
also possible. The evolution usually starts from a population of randomly generated individuals and happens in
generations. In each generation, the fitness of every individual in the population is evaluated, multiple
individuals are stochastically selected from the current population (based on their fitness), and modified
(recombined and possibly randomly mutated) to form a new population. The new population is then used in the
next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of
generations has been produced, or a satisfactory fitness level has been reached for the population. If the
algorithm has terminated due to a maximum number of generations, a satisfactory solution may or may not have
been reached.
1.3 Tabu Search
Tabu search is a local search method used for mathematical optimization. Local searches take a
potential solution to a problem and check its immediate neighbors in the hope of finding an improved solution.
Local search methods have a tendency to become stuck in suboptimal regions or on plateaus where many
solutions are equally fit. Tabu search enhances the performance of these techniques by using memory structures
that describe the visited solutions or user-provided sets of rule. If a potential solution has been previously visited
within a certain short-term period or if it has violated a rule, it is marked as "taboo" so that the algorithm does
not consider that possibility repeatedly.
1.4 Simulated Annealing (SA)
Simulated annealing (SA) is a generic probabilistic meta heuristic for the global optimization problem
of locating a good approximation to the global optimum of a given function in a large search space. It is often
used when the search space is discrete. For certain problems, simulated annealing may be more efficient than
exhaustive enumeration— provided that the goal is merely to find an acceptably good solution in a fixed amount
of time, rather than the best possible solution. The name and inspiration come from annealing in metallurgy, a
technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce
their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the
internal energy) and wander randomly through states of higher energy; the slow cooling gives them more
chances of finding configurations with lower internal energy than the initial one.
By analogy with this physical process, each step of the SA algorithm attempts to replace the current
solution by a random solution (chosen according to a candidate distribution, often constructed to sample from
solutions near the current solution). The new solution may then be accepted with a probability that depends both
on the difference between the corresponding function values and also on a global parameter T (called the
temperature), that is gradually decreased during the process. The dependency is such that the choice between
the previous and current solution is almost random when T is large, but increasingly selects the better or
"downhill" solution (for a minimization problem) as T goes to zero. The allowance for "uphill" moves
potentially saves the method from becoming stuck at local optima—which are the bane of greedier methods.
1.5 Reactive Search Optimization (RSO)
Reactive Search Optimization (RSO) defines local-search heuristics based on machine learning, a
family of optimization algorithms based on the local search techniques. It refers to a class of heuristics that
automatically adjust their working parameters during the optimization phase. Reactive Search Optimization
(RSO), like all local search techniques, is applied to the problem of finding the optimal configuration of a
system; such configuration is usually composed of continuously or discretely varying parameters, while the
optimality criterion is a numerical value associated to each configuration. In most cases, an optimization
problem can be reduced to finding the (global) minimum of a function whose arguments are the configuration
parameters, seen as free variables in the function's domain space.
Reactive Search Optimization advocates the integration of sub-symbolic machine learning techniques
into search heuristics for solving complex optimization problems. The word reactive hints at a ready response to
events during the search through an internal feedback loop for online self-tuning and dynamic adaptation. In
Reactive Search the past history of the search and the knowledge accumulated while moving in the
configuration space is used for self-adaptation in an autonomic manner: the algorithm maintains the internal
flexibility needed to address different situations during the search, but the adaptation is automated, and executed
while the algorithm runs on a single instance and reflects on its past experience.
1.6 Ant colony algorithms
In the natural world, ants (initially) wander randomly, and upon finding food return to their colony
while laying down pheromone trails. If other ants find such a path, they are likely not to keep traveling at
random, but to instead follow the trail, returning and reinforcing it if they eventually find food.
3. Artificial Intelligence in Robot Path Planning
DOI: 10.9790/0661-1732115119 www.iosrjournals.org 117 | Page
Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength. The
more time it takes for an ant to travel down the path and back again, the more time the pheromones have to
evaporate. A short path, by comparison, gets marched over more frequently, and thus the pheromone density
becomes higher on shorter paths than longer ones. Pheromone evaporation also has the advantage of avoiding
the convergence to a locally optimal solution. If there were no evaporation at all, the paths chosen by the first
ants would tend to be excessively attractive to the following ones. In that case, the exploration of the solution
space would be constrained.
II. Overcoming The Problem Of ACO
Various approaches to overcome the problem of ACO i.e. mitigating stagnation which include:- evaporation,
aging and pheromone smoothing .
The Approaches to alleviate stagnation is pheromone control. Pheromone control adopts several
approaches to reduce the influence from past experience and encourage the exploration of new paths that are
non-optimal.
2.1 Evaporation
To reduce the effect of past experience, an approach called evaporation is used in conjunction in
optimal path from being excessively high and preventing ants from exploring the other paths. In each iteration,
the pheromone value ij in all edges are decremented by a factor p such that ij ←ij (1-p)
2.2 Aging
A past experience can also be reduced by controlling the amount of pheromone deposited for each ant
according to its age. This approach is known as aging. In aging, an ant deposits lesser and lesser amount of
pheromone as it moves from one obstacle to other obstacle. Aging is based on the rationale that ―old ants are
less successful in locating the optimal paths since they take longer time to reach their destination. Both aging
and evaporation encourage discoveries of new paths that are previously non-optimal
2.3 Limiting and smoothing pheromone
Limiting the amount of pheromone in every path, by placing an upper bound on the amount of
pheromone for every edge(i,j), the preference of an ant for optimal path is reduced. This approach prevents the
situation of generating a dominant path. A variation of such an approach is called pheromone smoothing.
III. Proposed Solution For Robotic Path Planning
Fig1: Layout of Robot Path Planning
3.1 Source
Robot starts walking from source point (Xs,Ys) and it is fixed
3.2 Robot Moves one step
From source point(Xs,Ys), Robot is moving towards the destination point by taking one step ahead and
changes the value of (Xs,Ys) to (Xsnew,Ysnew) by using the below equations :
4. Artificial Intelligence in Robot Path Planning
DOI: 10.9790/0661-1732115119 www.iosrjournals.org 118 | Page
Xsnew = Xprev + step*cos(e) (1)
Ysnew = Yprev + step*sin(e) (2)
Where, Xprev,Yprev denotes where the robot is currently situated in which we add the step size multiplied by
cos(ө)and sin(ө) respectively which will gives the robot’s next position. Where ө is dynamic angle and it can be
calculated by :
Ө = Tan-1
(Xprev/Yprev) (3)
3.3 Flag Setting
Robot sees the value of the flag, if it's value is zero, it indicates that there is no obstacle and robot can
take a step ahead and can reach to the destination point.
3.4 Encounter with obstacle
Whenever the robot encounter with obstacle, it has to stop moving means there is no increase in the
step size and robot has to take three steps back. In our proposed work, twenty obstacle are generated randomly
which is of rectangular shape. Number of obstacles is fixed which is a constraint in our work.
Obstacle (oopsV) can be generated by the following pseudo code :
oopsV=20;
x=100*rand(1,oopsV);
y=100*rand(1,oopsV);
l=10*rand(1,oopsV);
w=10*rand(1,oopsV); for m=1:oopsV
plot([x(1,m) x(1,m)+w(1,m)], [y(1,m) y(1,m)]); plot([x(1,m) x(1,m)], [y(1,m) y(1,m)+l(1,m)]); plot[x(1,m)
x(1,m)+w(1,m)],[y(1,m)+l(1,m) y(1,m)+l(1,m)]);
plot([x(1,m)+w(1,m)x(1,m)+w(1,m)],[y(1,m) y(1,m)+l(1,m)]);
end
In this pseudo code initialize the number of obstacles oopsV=20 and obstacles are generated in the moving
space of 100*100 and whose length and width varies between 0 1o 10 dynamically.
3.5Take three step back
Whenever the robot encounter with obstacle, robot stop moving and take three step back by using the
following equation :
Xsnew = Xprev-3*step*cos(e) (4)
Ysnew = Yprev-3*step*sin(e) (5)
Ө =Tan-1
(Xprev/Yprev) (6)
3.6Destination
Finally robot has to reach at the point (XT,YT), which is fixed. Robot has to bypass the obstacle and by
following optimal.
Path has to reach to target point.
3.7Apply the ACO algorithm to bypass the obstacle
ACO is a met heuristic algorithm inspired by the real ant for the forage for food. ACO is applied to the
problems which can be described by the graphs so that feasible solution can be expressed in terms of paths on
the graph. It was first applied to TSP. Among the feasible paths, ACO is used to find out the optimal one i.e.
locally or globally optimal. This algorithm is implemented in two steps. In first step, the edge is selected on the
basis of probability formula. Assume that ant k is located at node i, uses the pheromone ij deposited on the
edge (i,j) to compute the probability of choosing next node.
ij
if j N k
ij
i
(7)Pij =
j
N
( k )
i
5. Artificial Intelligence in Robot Path Planning
DOI: 10.9790/0661-1732115119 www.iosrjournals.org 119 | Page
0 otherwise
Where α denotes the degree of importance of pheromone trail and Ni
(k)
indicates the set of neighbors of
ant k when located at node i except the predecessor node i.e. the last node visited by ant k. This will prevent the
ant k from returning to the same node. An ant travels from node to node until it reaches the destination node
and comes back to the source node. In second step, once all the ants complete their tour, then global
optimization of the pheromone trail takes place.
N
ij =(1- ) + ij
(k)
k 1 (8)
Where, (0,1] is the evaporation rate and ij ( k ) is the amount of pheromone deposited on the
edge (i,j) selected by the best ant k. The aim of pheromone updating is to increase the pheromone value
associated with optimal path. The pheromone deposited on arc (i, j) by the best ant k is ij ( k ) .
Where,
( k )
=
Q
(9)
Lk
ij
Here Q is a constant and Lk is the length of the path traversed by the best ant k. This equation is also
implemented as :
f
best (10)( k )
if (i, j) global best tour
ij
f
worst
0
otherwis
e
IV. Conclusion
Ant colony optimization is to be applied for robot–motion control such as navigation and obstacle
avoidance in an efficient manner. From this, money can be saved and reliability can be increased by allowing
them to adapt themselves according to the environment without further programming. Ant colony optimization
(ACO) takes inspiration from the foraging behavior of ant species. These ants deposit pheromone on the ground
in order to mark some favorable path.
References
[1]. Yao-hong Qu, Quan Pan, Jian-guo Yan, ―Flight Path Planning of UAV Based on Heuristically Search and Genetic Algorithms‖,
Annual Conference of IEEE on Industrial Electronics Society, (IECON),pp:5,2005.
[2]. Chih-Lyang Hwang, Member, IEEE, and Li-Jui Chang, ―Internet-Based Smart-Space Navigation of a Car-Like Wheeled Robot
Using Fuzzy-Neural Adaptive Control‖, IEEE Transactions on Fuzzy Systems, pp: 1271 – 1284,2008
[3]. Abdullah Zawawi MOHAMED, Sang Heon LEE, Mahfuz AZIZ, Hung Yao HSU, Wahid Md FERDOUS, ―A Proposal on
Development of Intelligent PSO Based Path Planning and Image Based Obstacle Avoidance for Real Multi Agents Robotics System
Application‖, International Conference on Electronic Computer Technology (ICECT), pp: 128 – 132, 2010.