In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO.Comparing the suggested hybrid algorithm with the original algorithms it results were excellent. The optimum solution was found in most of test functions.
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.
This document summarizes a research paper that analyzes the performance and convergence of a novel genetic algorithm model towards finding global minima. The paper introduces genetic algorithms, which are probabilistic search algorithms inspired by natural evolution. It describes the components of genetic algorithms, including chromosomes, fitness functions, reproduction, crossover, and mutation operators. It also discusses encoding solutions as chromosomes and two common genetic algorithm models: Holland's original model and the common model. The paper aims to present an analysis of applying genetic algorithms to optimize test functions and finding their global minima.
For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods.
The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function’s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity.
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.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
This document discusses various classical and advanced optimization techniques. It begins with an overview of classical techniques like single/multivariable optimization and methods using Lagrange multipliers or Kuhn-Tucker conditions. Numerical methods are then introduced, including linear programming, integer programming, and nonlinear programming. Advanced techniques like hill climbing, simulated annealing, genetic algorithms, and ant colony optimization are also summarized. These optimization methods are inspired by natural processes and use techniques such as local search, positive feedback, and path pheromones to find approximate solutions.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...IOSR Journals
This document discusses using genetic algorithms and particle swarm optimization techniques to optimize software testing by finding the most error-prone paths in a program. It begins by providing background on software testing and the need for automated techniques. It then describes how genetic algorithms and particle swarm optimization work as meta-heuristic search techniques that can be applied to the problem of generating optimal test cases. The document presents pseudocode for each algorithm and provides a sample implementation of genetic algorithms to optimize a mathematical function. It similarly provides an overview of implementing particle swarm optimization to minimize another mathematical function. The goal is to generate test cases using these algorithms and do a comparative study of their effectiveness.
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.
This document summarizes a research paper that analyzes the performance and convergence of a novel genetic algorithm model towards finding global minima. The paper introduces genetic algorithms, which are probabilistic search algorithms inspired by natural evolution. It describes the components of genetic algorithms, including chromosomes, fitness functions, reproduction, crossover, and mutation operators. It also discusses encoding solutions as chromosomes and two common genetic algorithm models: Holland's original model and the common model. The paper aims to present an analysis of applying genetic algorithms to optimize test functions and finding their global minima.
For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods.
The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function’s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity.
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.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
This document discusses various classical and advanced optimization techniques. It begins with an overview of classical techniques like single/multivariable optimization and methods using Lagrange multipliers or Kuhn-Tucker conditions. Numerical methods are then introduced, including linear programming, integer programming, and nonlinear programming. Advanced techniques like hill climbing, simulated annealing, genetic algorithms, and ant colony optimization are also summarized. These optimization methods are inspired by natural processes and use techniques such as local search, positive feedback, and path pheromones to find approximate solutions.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Application of Genetic Algorithm and Particle Swarm Optimization in Software ...IOSR Journals
This document discusses using genetic algorithms and particle swarm optimization techniques to optimize software testing by finding the most error-prone paths in a program. It begins by providing background on software testing and the need for automated techniques. It then describes how genetic algorithms and particle swarm optimization work as meta-heuristic search techniques that can be applied to the problem of generating optimal test cases. The document presents pseudocode for each algorithm and provides a sample implementation of genetic algorithms to optimize a mathematical function. It similarly provides an overview of implementing particle swarm optimization to minimize another mathematical function. The goal is to generate test cases using these algorithms and do a comparative study of their effectiveness.
Biology-Derived Algorithms in Engineering OptimizationXin-She Yang
The document discusses biology-derived algorithms and their applications in engineering optimization. It describes several biology-inspired algorithms including genetic algorithms, photosynthetic algorithms, neural networks, and cellular automata. Genetic algorithms and photosynthetic algorithms are discussed in more detail. The document also provides examples of how these algorithms can be applied to problems in engineering optimization, such as parameter estimation and inverse analysis.
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
Comparative Analysis: Effective Information Retrieval Using Different Learnin...RSIS International
Information Retrieval is the activity of searching meaningful information from a collection of information resources such as Documents, relational databases and the World Wide Web. Information retrieval system mainly consists of two phases, storing indexed documents and retrieval of relevant result. Retrieving information effectively from huge data storage, it requires Machine Learning for computer systems. Machine learning has objective to instruct computers to use data or past experience to solve a given problem. Machine learning has number of applications, including classifier to be trained on email messages to learn in order to distinguish between spam and non-spam messages, systems that analyze past sales data to predict customer buying behavior, fraud detection etc. Machine learning can be applied as association analysis through supervised learning, unsupervised learning and Reinforcement Learning. The goal of these three learning is to provide an effective way of information retrieval from data warehouse to avoid problems such as ambiguity. This study will compare the effectiveness and impuissance of these learning approaches.
This document discusses fuzzy genetic algorithms (FGAs), which combine fuzzy logic and genetic algorithms. It provides definitions of fuzzy logic and genetic algorithms. Fuzzy logic handles imprecise variables between 0 and 1, while genetic algorithms use techniques like selection, crossover and mutation to evolve solutions. The document notes that FGAs use fuzzy logic techniques to improve genetic algorithm behavior and components. It describes different FGA approaches and lists application sectors like engineering and economics.
Automatic Feature Subset Selection using Genetic Algorithm for Clusteringidescitation
Feature subset selection is a process of selecting a
subset of minimal, relevant features and is a pre processing
technique for a wide variety of applications. High dimensional
data clustering is a challenging task in data mining. Reduced
set of features helps to make the patterns easier to understand.
Reduced set of features are more significant if they are
application specific. Almost all existing feature subset
selection algorithms are not automatic and are not application
specific. This paper made an attempt to find the feature subset
for optimal clusters while clustering. The proposed Automatic
Feature Subset Selection using Genetic Algorithm (AFSGA)
identifies the required features automatically and reduces
the computational cost in determining good clusters. The
performance of AFSGA is tested using public and synthetic
datasets with varying dimensionality. Experimental results
have shown the improved efficacy of the algorithm with optimal
clusters and computational cost.
How to optimize with the help of the Particle Swarm Optimization Technique and xlOptimizer ? This brief tutorial will enable you to solve any optimization problem with the application of Particle Swarm Optimization Method. After a brief introduction about the method the tutorial will show you the steps that you will need to follow for application of PSO in optimization even if you do not know any programming.(Some basic knowledge of MS Excel 2010 and later is required).
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
Introduction to Optimization with Genetic Algorithm (GA)Ahmed Gad
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
Convergence tendency of genetic algorithms and artificial immune system in so...ijcsity
By the advances in the Evolution Algorithms (EAs) and the intelligent optimization metho
ds we witness the
big revolutions in solving the optimization problems. The application of the evolution algorithms are not
only not limited to the combined optimization problems, but also are vast in domain to the continuous
optimization problems. In this
paper we analyze and study the Genetic Algorithm (GA) and the Artificial
Immune System (AIS)
algorithm
which are capable in escaping the local optimization and also fastening
reaching the global optimization and to show the efficiency of the GA and AIS th
e application of them in
Solving Continuous Optimization Functions (SCOFs) are studied. Because of the multi variables and the
multi
-
dimensional spaces in SCOFs the use of the classic optimization methods, is generally non
-
efficient
and high cost. In other
words the use of the classic optimization methods for SCOFs generally leads to a
local optimized solution. A possible solution for SCOFs is to use the EAs which are high in probability of
succeeding reaching the local optimized solution. The results in pa
per show that GA is more efficient than
AIS in reaching the optimized solution in SCOFs.
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.
How to use data to design and optimize reaction? A quick introduction to work...Ichigaku Takigawa
(Journal Club) ICReDD Seminar, Apr 27 2020
Institute for Chemical Reaction Design and Discovery (ICReDD)
Hokkaido University
Sapporo, JAPAN
https://www.icredd.hokudai.ac.jp
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
FACE RECOGNITION USING PRINCIPAL COMPONENT ANALYSIS WITH MEDIAN FOR NORMALIZA...csandit
Recognizing Faces helps to name the various subjects present in the image. This work focuses
on labeling faces on an image which includes faces of humans being of various age group
(heterogeneous set ). Principal component analysis concentrates on finds the mean of the data
set and subtracts the mean value from the data set with an intention to normalize that data.
Normalization with respect to image is the removal of common features from the data set. This
work brings in the novel idea of deploying the median another measure of central tendency for
normalization rather than mean. The above work was implemented using matlab. Results show
that Median is the best measure for normalization for a heterogeneous data set which gives
raise to outliers.
This document provides an introduction to genetic algorithms (GAs) including:
- GAs are inspired by Darwin's theory of evolution and use techniques like inheritance, mutation, selection, and crossover to find solutions to optimization problems.
- The document discusses key GA components like populations of individuals, fitness functions, selection, crossover, and mutation.
- Examples of GA applications to energy management problems are presented including categorizing appliances, pricing schemes, and parametrizing a GA for scheduling home appliances.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
The document describes a non-revisiting genetic algorithm with adaptive mutation for optimizing multi-dimensional numeric functions. The proposed algorithm avoids revisiting previously evaluated solutions by replacing any duplicates with a mutated version of either the best or random individual. The mutation rate adapts over generations, starting with more exploration and ending with more exploitation. Experimental results on 9 benchmark functions in 2 and 4 dimensions show the proposed algorithm achieves better best and average fitness than a standard genetic algorithm, with improvements ranging from 10-98% depending on the function.
Multiobjective Flexible Job Shop Scheduling Using A Modified Invasive Weed Op...ijsc
This document summarizes a research paper that proposes using a modified Invasive Weed Optimization (IWO) algorithm to solve multi-objective flexible job shop scheduling problems. The goals are to minimize makespan, total machine workload, and workload of the most loaded machine. IWO is a bio-inspired metaheuristic algorithm that mimics how weeds colonize and spread. The researchers encode job scheduling solutions as "weeds" and use properties of weed colonization like reproduction, spatial dispersal, and competition in the IWO algorithm. Computational tests on benchmark problems show the modified IWO finds optimal or best-known solutions, showing it is competitive with state-of-the-art methods for flexible job shop scheduling.
Recently, many studies are carried out with inspirations from ecological phenomena for developing
optimization techniques. The new algorithm that is motivated by a common phenomenon in agriculture is
colonization of invasive weeds. In this paper, a modified invasive weed optimization (IWO) algorithm is
presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the
criteria to minimize the maximum completion time (makespan), the total workload of machines and the
workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour
of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve
continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV) is used to
convert the continuous position values to the discrete job sequences. The computational experiments show
that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is
able to find the optimal and best-known solutions on the instances studied.
A comprehensive review of the firefly algorithmsXin-She Yang
This document provides a comprehensive review of firefly algorithms. It begins with background on swarm intelligence and how firefly algorithms were inspired by the flashing lights of fireflies. It then describes the basic structure of firefly algorithms, including initializing a population of fireflies, evaluating their fitness, sorting by fitness, selecting the best solution, and moving fireflies toward more attractive solutions over generations. The document reviews applications of firefly algorithms in areas like continuous, combinatorial, and multi-objective optimization as well as engineering problems. It concludes by discussing exploration vs exploitation in firefly algorithms and directions for further development.
SWARM INTELLIGENCE FROM NATURAL TO ARTIFICIAL SYSTEMS: ANT COLONY OPTIMIZATIONFransiskeran
This document summarizes research on ant colony optimization (ACO), a metaheuristic algorithm inspired by the foraging behavior of ants. It describes how real ant colonies use pheromone trails to efficiently find short paths between their nest and food sources through decentralized cooperation. The document then explains how ACO works by simulating artificial ants that probabilistically construct solutions and update pheromone values to guide future construction. Several standard ACO algorithms are outlined, including Ant System, Ant Colony System, Max-Min Ant System, and Rank-Based Ant System. Applications of ACO discussed include the traveling salesman problem.
A review meta heuristic approaches for solving rectangle packing problemIAEME Publication
This document summarizes various meta-heuristic approaches that have been used to solve the rectangle packing problem, including genetic algorithms, ant colony optimization, particle swarm optimization, iterated local search, and their basic workings. It then reviews literature on applying these meta-heuristics to the rectangular packing problem, describing several approaches that combine genetic algorithms with heuristic packing routines to generate high-quality layouts.
Biology-Derived Algorithms in Engineering OptimizationXin-She Yang
The document discusses biology-derived algorithms and their applications in engineering optimization. It describes several biology-inspired algorithms including genetic algorithms, photosynthetic algorithms, neural networks, and cellular automata. Genetic algorithms and photosynthetic algorithms are discussed in more detail. The document also provides examples of how these algorithms can be applied to problems in engineering optimization, such as parameter estimation and inverse analysis.
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
Comparative Analysis: Effective Information Retrieval Using Different Learnin...RSIS International
Information Retrieval is the activity of searching meaningful information from a collection of information resources such as Documents, relational databases and the World Wide Web. Information retrieval system mainly consists of two phases, storing indexed documents and retrieval of relevant result. Retrieving information effectively from huge data storage, it requires Machine Learning for computer systems. Machine learning has objective to instruct computers to use data or past experience to solve a given problem. Machine learning has number of applications, including classifier to be trained on email messages to learn in order to distinguish between spam and non-spam messages, systems that analyze past sales data to predict customer buying behavior, fraud detection etc. Machine learning can be applied as association analysis through supervised learning, unsupervised learning and Reinforcement Learning. The goal of these three learning is to provide an effective way of information retrieval from data warehouse to avoid problems such as ambiguity. This study will compare the effectiveness and impuissance of these learning approaches.
This document discusses fuzzy genetic algorithms (FGAs), which combine fuzzy logic and genetic algorithms. It provides definitions of fuzzy logic and genetic algorithms. Fuzzy logic handles imprecise variables between 0 and 1, while genetic algorithms use techniques like selection, crossover and mutation to evolve solutions. The document notes that FGAs use fuzzy logic techniques to improve genetic algorithm behavior and components. It describes different FGA approaches and lists application sectors like engineering and economics.
Automatic Feature Subset Selection using Genetic Algorithm for Clusteringidescitation
Feature subset selection is a process of selecting a
subset of minimal, relevant features and is a pre processing
technique for a wide variety of applications. High dimensional
data clustering is a challenging task in data mining. Reduced
set of features helps to make the patterns easier to understand.
Reduced set of features are more significant if they are
application specific. Almost all existing feature subset
selection algorithms are not automatic and are not application
specific. This paper made an attempt to find the feature subset
for optimal clusters while clustering. The proposed Automatic
Feature Subset Selection using Genetic Algorithm (AFSGA)
identifies the required features automatically and reduces
the computational cost in determining good clusters. The
performance of AFSGA is tested using public and synthetic
datasets with varying dimensionality. Experimental results
have shown the improved efficacy of the algorithm with optimal
clusters and computational cost.
How to optimize with the help of the Particle Swarm Optimization Technique and xlOptimizer ? This brief tutorial will enable you to solve any optimization problem with the application of Particle Swarm Optimization Method. After a brief introduction about the method the tutorial will show you the steps that you will need to follow for application of PSO in optimization even if you do not know any programming.(Some basic knowledge of MS Excel 2010 and later is required).
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
Introduction to Optimization with Genetic Algorithm (GA)Ahmed Gad
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
Convergence tendency of genetic algorithms and artificial immune system in so...ijcsity
By the advances in the Evolution Algorithms (EAs) and the intelligent optimization metho
ds we witness the
big revolutions in solving the optimization problems. The application of the evolution algorithms are not
only not limited to the combined optimization problems, but also are vast in domain to the continuous
optimization problems. In this
paper we analyze and study the Genetic Algorithm (GA) and the Artificial
Immune System (AIS)
algorithm
which are capable in escaping the local optimization and also fastening
reaching the global optimization and to show the efficiency of the GA and AIS th
e application of them in
Solving Continuous Optimization Functions (SCOFs) are studied. Because of the multi variables and the
multi
-
dimensional spaces in SCOFs the use of the classic optimization methods, is generally non
-
efficient
and high cost. In other
words the use of the classic optimization methods for SCOFs generally leads to a
local optimized solution. A possible solution for SCOFs is to use the EAs which are high in probability of
succeeding reaching the local optimized solution. The results in pa
per show that GA is more efficient than
AIS in reaching the optimized solution in SCOFs.
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.
How to use data to design and optimize reaction? A quick introduction to work...Ichigaku Takigawa
(Journal Club) ICReDD Seminar, Apr 27 2020
Institute for Chemical Reaction Design and Discovery (ICReDD)
Hokkaido University
Sapporo, JAPAN
https://www.icredd.hokudai.ac.jp
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
FACE RECOGNITION USING PRINCIPAL COMPONENT ANALYSIS WITH MEDIAN FOR NORMALIZA...csandit
Recognizing Faces helps to name the various subjects present in the image. This work focuses
on labeling faces on an image which includes faces of humans being of various age group
(heterogeneous set ). Principal component analysis concentrates on finds the mean of the data
set and subtracts the mean value from the data set with an intention to normalize that data.
Normalization with respect to image is the removal of common features from the data set. This
work brings in the novel idea of deploying the median another measure of central tendency for
normalization rather than mean. The above work was implemented using matlab. Results show
that Median is the best measure for normalization for a heterogeneous data set which gives
raise to outliers.
This document provides an introduction to genetic algorithms (GAs) including:
- GAs are inspired by Darwin's theory of evolution and use techniques like inheritance, mutation, selection, and crossover to find solutions to optimization problems.
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In this paper, a modified invasive weed optimization (IWO) algorithm is presented for
optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria
to minimize the maximum completion time (makespan), the total workload of machines and the
workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the
ecological behaviour of weeds in colonizing and finding suitable place for growth and
reproduction. IWO is developed to solve continuous optimization problems that’s why the
heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position
values to the discrete job sequences. The computational experiments show that the proposed
algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to
find the optimal and best-known solutions on the instances studied.
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optimization. The design goal is to minimize the integral absolute error and reduce transient response by
minimizing overshoot, settling time and rise time of step response. An Objective function of these indexes is
defined and minimized applying the four optimization methods mentioned above. After optimization of the
objective function, the optimal parameters for the PID controller are adjusted. Simulation results show that
FNN has a remarkable effect on decreasing the amount of settling time and rise-time and eliminating of
steady-state error while the SFL algorithm performs better on steady-state error and the ABC algorithm is
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Solving np hard problem using artificial bee colony algorithmIAEME Publication
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MOCANAR: A MULTI-OBJECTIVE CUCKOO SEARCH ALGORITHM FOR NUMERIC ASSOCIATION RU...cscpconf
Extracting association rules from numeric features involves searching a very large search space. To
deal with this problem, in this paper a meta-heuristic algorithm is used that we have called
MOCANAR. The MOCANAR is a Pareto based multi-objective cuckoo search algorithm which
extracts high quality association rules from numeric datasets. The support, confidence,
interestingness and comprehensibility are the objectives that have been considered in the
MOCANAR. The MOCANAR extracts rules incrementally, in which, in each run of the algorithm, a
small number of high quality rules are made. In this paper, a comprehensive taxonomy of metaheuristic
algorithm have been presented. Using this taxonomy, we have decided to use a Cuckoo
Search algorithm because this algorithm is one of the most matured algorithms and also, it is simple
to use and easy to comprehend. In addition, until now, to our knowledge this method has not been
used as a multi-objective algorithm and has not been used in the association rule mining area. To
demonstrate the merit and associated benefits of the proposed methodology, the methodology has
been applied to a number of datasets and high quality results in terms of the objectives were
extracted
MOCANAR: A Multi-Objective Cuckoo Search Algorithm for Numeric Association Ru...csandit
Extracting association rules from numeric features
involves searching a very large search space. To
deal with this problem, in this paper a meta-heuris
tic algorithm is used that we have called
MOCANAR. The MOCANAR is a Pareto based multi-object
ive cuckoo search algorithm which
extracts high quality association rules from numeri
c datasets. The support, confidence,
interestingness and comprehensibility are the objec
tives that have been considered in the
MOCANAR. The MOCANAR extracts rules incrementally,
in which, in each run of the algorithm, a
small number of high quality rules are made. In thi
s paper, a comprehensive taxonomy of meta-
heuristic algorithm have been presented. Using this
taxonomy, we have decided to use a Cuckoo
Search algorithm because this algorithm is one of t
he most matured algorithms and also, it is simple
to use and easy to comprehend. In addition, until n
ow, to our knowledge this method has not been
used as a multi-objective algorithm and has not bee
n used in the association rule mining area. To
demonstrate the merit and associated benefits of th
e proposed methodology, the methodology has
been applied to a number of datasets and high quali
ty results in terms of the objectives were
extracted
Comparison Between Pid Controllers for Gryphon Robot Optimized With Neuro-Fuz...ijctcm
In this paper three intelligent evolutionary optimization approaches to design PID controller for a Gryphon Robot are presented and compared to the results of a neuro-fuzzy system applied. The three applied approaches are artificial bee colony, shuffled frog leaping algorithm and particle swarm optimization. The design goal is to minimize the integral absolute error and reduce transient response by minimizing overshoot, settling time and rise time of step response. An Objective function of these indexes is defined and minimized applying the four optimization methods mentioned above. After optimization of the objective function, the optimal parameters for the PID controller are adjusted. Simulation results show that FNN has a remarkable effect on decreasing the amount of settling time and rise-time and eliminating of steady-state error while the SFL algorithm performs better on steady-state error and the ABC algorithm is better on decreasing of overshoot. On the other hand PSO sounds to perform well on steady-state error only. In steady state manner all of the methods react robustly to the disturbance, but FNN shows more stability in transient response.
Comparison Between Pid Controllers for Gryphon Robot Optimized With Neuro-Fuz...AlessioAmedeo
In this paper three intelligent evolutionary optimization approaches to design PID controller for a Gryphon Robot are presented and compared to the results of a neuro-fuzzy system applied. The three applied approaches are artificial bee colony, shuffled frog leaping algorithm and particle swarm optimization. The design goal is to minimize the integral absolute error and reduce transient response by minimizing overshoot, settling time and rise time of step response. An Objective function of these indexes is defined and minimized applying the four optimization methods mentioned above. After optimization of the objective function, the optimal parameters for the PID controller are adjusted. Simulation results show that FNN has a remarkable effect on decreasing the amount of settling time and rise-time and eliminating of steady-state error while the SFL algorithm performs better on steady-state error and the ABC algorithm is better on decreasing of overshoot. On the other hand PSO sounds to perform well on steady-state error only. In steady state manner all of the methods react robustly to the disturbance, but FNN shows more stability in transient response.
A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problemsijpla
Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop Scheduling Problem is
categorized under popular scheduling problems. In this research work, Bacterial Foraging Optimization
was hybridized with Ant Colony Optimization and a new technique Hybrid Bacterial Foraging
Optimization for solving Job Shop Scheduling Problem was proposed. The optimal solutions obtained by
proposed Hybrid Bacterial Foraging Optimization algorithms are much better when compared with the
solutions obtained by Bacterial Foraging Optimization algorithm for well-known test problems of different
sizes. From the implementation of this research work, it could be observed that the proposed Hybrid
Bacterial Foraging Optimization was effective than Bacterial Foraging Optimization algorithm in solving
Job Shop Scheduling Problems. Hybrid Bacterial Foraging Optimization is used to implement real world
Job Shop Scheduling Problems
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A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOLF OPTIMIZATION ALGORITHM
1. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.1, January 2020
DOI : 10.5121/ijaia.2020.11103 31
A HYBRID ALGORITHM BASED ON INVASIVE WEED
OPTIMIZATION ALGORITHM AND GREY WOLF
OPTIMIZATION ALGORITHM
Wisam Abdulelah Qasim1
and Ban Ahmed Mitras2
1
M.sc. Student, Department of Mathematics, College of Computer Science &
Mathematics, Mosul, Iraq.
2
Department of Mathematics, College of Computer Sciences & Mathematics,
Mosul, Iraq.
ABSTRACT
In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm
represents invasive weed optimization. This algorithm is a random numerical algorithm and the second
algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm
intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as
the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds
are a serious threat to cultivated plants because of their adaptability and are a threat to the overall
planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm.
The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to
reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and
taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new
hybridization process between the previous algorithms GWO and IWO and we will symbolize the new
algorithm IWOGWO.Comparing the suggested hybrid algorithm with the original algorithms it results
were excellent. The optimum solution was found in most of test functions.
KEYWORDS
Invasive weeds optimization algorithm , grey wolves optimization algorithm , hybrid algorithms,
optimization.
1. INTRODUCTION
Optimization is used in our daily lives (daily routine). The departments work to increase profits
while reducing costs, engineering designer when designing the product works to maximize the
performance of the product while working to reduce the cost at the same time. Recent years have
seen the emergence of many complex optimization issues that are the reason for the development
of highly efficient algorithms. Optimization suffers from several problems including:
1. There is chaos in evaluating the solution.
2. It is not easy to sort (distinguish) the optimal comprehensive solution from the local.
3. Problems related to matter constraints [1],[2].
The tendency used in the intuitive algorithms is one of the latest developments in the past two
decades. Note that algorithms (intuitive or post-intuitive) make up the vast majority of modern
techniques of optimization.
2. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.1, January 2020
32
We note that they have become very useful and powerful for solving complex optimization issues
and have applied these algorithms in all important areas including agricultural services,
engineering, science and in industrial applications as well [3].
The ways to solve optimization problems are two types:
1- Random algorithms.
2- Specific algorithms.
The specific algorithms represents most of the classical algorithms. The simplex method is a clear
example of linear programming. It is a specific algorithm. We also observe the use of the
derivative and are called derivative-based algorithms. One example is the Newton-Raphson
algorithm, which relies on the derivative, which uses the values of the target function and its
derivative, which we observe to behave well in smooth unimodal problems[2].
Methods of random algorithms:
1. Intuitive methods.
2. Post-Intuitive methods.
Glover was the first to introduce the term intuitive algorithm in 1986, which is the evolution of
the intuitive algorithm[2].
The post-intuitive algorithm operates at a higher level than the intuitive algorithm.
We observe that all post-intuitive are used
1- Local Search.
2- Confirmed swap for random distribution[2].
It is noticeable that there is no agreed definition of methods (intuitive or post-intuitive) where the
term (intuitive and post-intuitive) is used interchangeably and note that the recent trend refers to
the designation of all random algorithms that use local research and random distribution intuitive
algorithmic. Most intuitive algorithms are appropriate algorithms to solve global optimization
matters[4].
The two important elements in the (post-intuitive) algorithm, which are considered important
elements are: -
1- Intensification: It is intended to intensify research in the local area is done by using
information that the current best solution can be available in this region and this
corresponds to the principle of selecting the best ensures that the results will approach the
optimization.
2- Diversification : is the generation of contradictory solutions for exploring the research
space at the general level[2],[4].
The post intuitive can be classified in several methods. These methods can be classified
depending on the path and society. Examples are the genetic algorithm, which is classified
according to the community. A set of sections are used during the solution. The method of
simulating steel is that it uses a single element that moves within the search space and the search
method is a piece-piece that is the best solution or best move. It is usually acceptable and when
the move is not good, it usually accepts a certain/ probability, since the movements affect the path
in the search space and the probability of non-zero, the path up to a comprehensive
optimization[4].
3. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.1, January 2020
33
The problem of the research focused on obtaining the optimal comprehensive solution for the
problems of unrestricted and high measurement and avoid falling into the problems of local
solutions using the characteristics of the algorithm of swarms have been selected, one of the
algorithms of the swarms algorithm grey wolves GWO. So as to avoid the emergence of local
solutions[5].
The importance of the research is to develop a new way to solve the problems of optimization,
which is a difficult issue, in order to benefit the designers of the proposed hybrid algorithm and
any question falls within the difficult issues such as the issue of vehicle paths and many other
issues.
The aim of the research is to suggestion a new algorithm called Hybrid Algorithm where the
algorithm (invasive weeds optimization ) and the algorithm (grey wolves optimization) and the
resulting algorithm IWOGWO is a hybrid algorithm to take advantage of the positive qualities
and minimize the negatives of the previous two algorithms.
In recent years, hybrid algorithm has been introduced, knowing that designers have known it
since the 1980s.
2. INVASIVE WEED OPTIMIZATION ALGORITHM (IWO)
It is an algorithm designed first by Mehrabian and Lucas in 2006 and the algorithm is inspired by
biology (bio). It is a numerical optimization algorithm that simply simulates the natural behavior
of weeds in colonization and finding a good place for growth and reproduction. Some of the
characteristics of the IWO algorithm compared to other development algorithms. They are
methods of reproduction, spatial dispersion and competitive exclusion[6].
The IWO algorithm includes a number of key steps that are applied to issues to find and improve
the solution, and are usually interrelated as below:
2.1. Steps of invasive weeds algorithm
Setp1 Primary community:
A search area is taken and some number of weeds are randomly initialized in the full search
space.
Step2 Reproduction:
Plants produce seeds based on the given fitness function. As well as the limits (upper-lower) of
the function of decency in the colony. We observe that theNo. of seeds which the plant produce
linearly increases from the min.probable seed producing to the max.[6].
Equ.1 describe how to get seeds from the target function[7].
Step3 Spatial dispersion:
In this step randomly generated seeds are distributed over dimensions in the entire search space.
By normal distribution at a rate of zero and a different variation (variable).
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This step means that the seeds are randomly distributed around the mother plant.
However, the standard deviation of the random function will decrease from a predetermined
initial value( σinitial) to a final value (σfinal) at each step as
To calculate the location of the new seeds, we use the following equation:
where
Xson represents the location of the sons.
Xparentrepresentsparents site.
Randn generate random numbers by normal distribution (0,1) and record[8].
Step4 Competitive exclusion
Competition exists among plants for survival and if the plant is without offspring and its fate is
extinct when Pmax reaches the maximum number of plants in the colony, the process of exclusion
for a plant with poor fitness will begin.
2.2. Exclusion mechanism:
Upon reaching the maximum amount of herbs in the colony, each herb will be able to produce
seeds and will then spread to the place of research. When all the seeds are concentrated in their
position they are fitted with the mother plant (colony). Weeds with a low fitness function are
excluded to reach the maximum acceptable to the community in the colony.
Note that plants and their offspring, as well as the element that has a high fitness function or (the
best fitness function) will survive and will have a repetition in the algorithm [6].
3. GREY WOLVESOPTIMIZATION ALGORITHM (GWO)
This algorithm is based on the behavior of hunting and is considered this algorithm inspired by
nature in the past years of great importance has helped accept this algorithm between designers
and applied in different areas of life, because it is characterized by 1-Flexibility 2- Simplicity
The GWO algorithm is one of the methods of optimization adopted swarm intelligence, which is
within the social intelligence, which is based on supra-intuitive, which solves difficult problems.
It was designed by SeyedaliMirijalili in 2014 [9].
The levels of grey wolves are divided into four levels:
1) α alpha 2) β beta 3) δ Delta 4) ω omega The figure shows the hierarchy
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Figure 1. The hierarchy of the four levels [10].
The wolves α represent the leader of the group and are recognized by the elements of the group
by fixing their tails. The alpha element is called (the mastermind). The only element α is allowed
to mate in the group and the element α is responsible for decision-making and is also responsible
for managing the group. The decisions he makes are 1-bedtime 2-fishing time 3-waking up time
and many other decisions.
The strength of the grey wolves lies in the commands of the α commanding element by the group,
which is evidence of the organization and discipline of the group[11],[12].
Followed by the element β and come on in the second level after element α. The element β must
respect the element α. The element β plays the role of the leader on lower-level elements of the
group.The element β is a consultant to the α and polite element of the group. At the third level
come the wolves (element δ), which work with β to obey the group of element α and be superior
to element ω.The element ω offers obedience to all higher levels. The ram is considered a
scapegoat for the group.
The element ω allows it to eat at the end after the elements of the higher-level group are finished.
It may seem that its role is not important in the group, but when elements are lost, internal
fighting and problems within the group are observed. This helps to satisfy the whole group and
maintain the structure of hegemony [11],[12].
3.1. Mathematical model
The search is performed by the α element. Often unless research is assisted by elements β and δ,
hunting may also be shared if required, but not always.
The first three solutions α, β and δ mimic the hunting behavior of the grey wolves, which are
usually preserved. And got it yet.
Modeling in grey wolf optimization is the best solution. The element α is the best solution and
then the element β and then the element δ respectively. Finally the element omega. Grey wolves
adopted the hunting technique based on α, β, and δ, and only the omega element followed[13].
The main hunting stages of grey wolves are:
1. Search for prey.
2. Encircle and bother the prey till it stops movement
3. Attack towards prey (hunting).
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First: Search for prey:
The prey hunt is first prepare the way for prey hunt through random configuration in the grey
wolf search space [14].
Second: Encircling and harassing the prey
Equationsnumbered ( 4 and 5) represent the encircling behavior of grey wolves around prey
through mathematical modeling. Using equations numbered (4 and 5), the elements update their
position within the solution space around the prey.
A⃗⃗ and c represent Vector coefficients are calculated as follows:
The values of a⃗⃗⃗ linearly decrease from 2 to 0 over the iterations
r1 and r2 are random vectorstracking standard uniform distribution [0,1]
Equation number (8) represents the updating of the parameter (a):
t: is the recent repetition.
T: is the greater number of repetition [10].
Using equations numbered (4 and 5), the grey wolf can update its position in the space around the
prey and at any random location. Grey wolves will move around the best solution ever obtained
[11].
Third: Attack towards prey (hunting)
Grey wolves have the potential to characterize prey and encircle them. After completing the
encirclement, it focuses on hunting prey. The catch is usually by α and element βand δ may be
involved in the catch in the abstract search space. The best location of prey cannot be predicted.
To simulate the hunting behavior we assume that α is the best candidate solution. Element β and δ
have a better knowledge of the potential location of prey. It can simulate the behavior of elements
α, β and δ in the following formulas [15]:
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The following equations can be used to calculate the position vector of prey for wolves α, β and δ.
It is finding the rate for α, β and δ.
The best position can be calculated as shown in formula (15).
3.2. The Grey Wolf Algorithm (Gwo)
We can summarize the steps of the Grey Wolf Optimization Algorithm as shown below:
1- A random preparation for the population of the community of grey wolves.
2- Random configuration of parameters A, c and a.
3- find the fitness value.
4- For all iterations.
* We find the best search element is Xα.
* We find the second best search element X β.
* We find the third search element X δ.
5- If the current iteration is less than the total iteration, then the research elements = Omega.
Equation 4 and 5 of the mathematical model are used
Otherwise, the search elements are , xα , xβ , xδ
Equations 9 to 15 of the mathematical model are used
6- i = i + 1
7- Return , xα
8- the end
3.3. Exploration And Exploitation
The optimization algorithm may highlight a particular problem during the exploration process.
We note that the algorithm tries to discover new areas of the search area for the problem by
applying sudden changes in solutions and that the main goal is to discover the search space and
not to fall into the best local position.
The main objective of exploitation is to approach the estimated optimal solutions achieved in the
previous process (exploration).
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By discovering the neighborhood for each solution. Changes in solutions must be made and be
gradual in order to conform to the optimal level.
The variable C is a parameter for the main control of the grey wolf optimization algorithm that
enhances exploration. This parameter always returns the random value in the period [0,2] This
contribution is usually strong at number [1]. Any greater than C note that the solution is attracted
towards prey.
This parameter will provide random values and even that iteration number. Therefore, emphasis
is placed on exploitation during improvement in the case of stagnation of the local situation.
The second parameter is A which is the control parameter that leads to exploration, where the
value is defined based on a, decreasing linearly from 2 to 0.
Because the random elements change their range within the period [-2,2] in this parameter, this is
for parameter A, where the exploration is strengthened when A is greater than one and A is
smaller than one negative, i.e. at half of the iterations. While there is an emphasis on exploitation
when A falls within the period [-1,1] of the second half of the iterations, a good balance between
exploitation and exploration may be required to find an accurate approximation of optimal global
optimization.
So using random algorithms. This equilibrium occurs in the GWO algorithm, with the behavior of
coefficient a decreasing in equation to obtain the vector A [13].
4. HYBRID ALGORITHM
Many researchers have developed algorithms through hybridization between common and
difficult NP-Hard problems. Therefore, researchers have been working in the past years to
hybridize algorithms with normal and difficult issues and have obtained the best results through
the application of hybrid issues. The real purpose of the hybridization process is to obtain general
and varied solutions that can deal with real world problems.
Hybrid algorithms have relied on swarm intelligence, a newly recognized branch of artificial
intelligence that studies the collective behavior of complex self-regulating and decentralized
systems. The term intelligence of the swarms emerged by the two researchers Jingwing and
Geradobeni in 1989 as a set of algorithms and was used to control the swarms of robots[1].
In addition to the IWO optimization algorithm with GWO grey wolves algorithm to find a
comprehensive solution to optimization issues.
4.1. Hybrid Invasive Weed Algorithm Suggested
The new hybrid algorithm has been named IWOGWO suggested.
The invasive weed optimization algorithm is distinct from other evolutionary algorithms
including:
1 – reproduction. 2 – spatial dispersion. 3 – competitive exclusion.
The maximum benefit was achieved from the application of these properties in the process of
hybridization.
Steps of the IWOGWO hybrid algorithm
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Step1 Preparingan initial population
The primary community is created by generating a primary community of solutions and
calculating the value of the fitness function of that community.
Step2 use the gray wolves algorithm
Use the gray wolves algorithm to improve random initial population generated from step number
(1) and we get improved population enters into step number (3).
Step3 Re-producing
New seeds can be obtained by equation (1), which allows obtaining new offspring.
Step4 Spatial dispersion
Seed propagation in the research space gives this algorithm the adaptability and randomness
algorithm to use
1- Standard deviation. 2- Normal distribution.
And those calculated from equation numbered (2) and this allows the spread of seeds in the
research space.
Step5 Locating the sons
It is determined by equation numbered (3) After parents and children are brought together, they
will form a colony of weeds.
Step6 Competitive exclusion
After the order of the improved community comes the role on the property of competitive
exclusion. Upon reaching the maximum allowable in the colony of (number of plants) Pmax then
are excluded elements of low fitness and repeat the process until the optimal solution or until the
condition is stop.
Figure 2 .Flowchart Of the Proposed IWOGWO Algorithm
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5. PRACTICAL ASPECT
The hybrid algorithm (IWOGWO) has been tested in terms of efficiency by using this algorithm
to solve 10 a function of numerical optimization functions which is a highly efficient problems.
The IWIGWO hybrid algorithm is a combination of the algorithms of weed optimization and the
algorithm of grey wolves.
The table below shows the initial parameters that must be specified before starting the program.
Table1. Initial parameters
Parameter IWO GWO IWOGWO
Npop0 5-10-15-20-25 5-10-15-20-25 5-10-15-20-25
Npop 25 25 25
N 2 --- 2
Smin 0 --- 0
Smax 5 --- 5
MaxIt 1000 1000 1000
Sigma initial 0.5 ---- 0.5
Sigma final 0.001 --- 0.001
The laptop was used has the following specifications:
CPU speed is 2.4 GHz.
RAM size is 4GB
Matlab R2013a program works on windows7.
Table 2. Test functions
Function Range Dim
F1(x) = ∑ xi
2
n
i=1
[-100,100] 30
F2(x) = ∑|xi| + ∏ |xi|
n
i=1
n
i=1
[-10,10] 30
F3(x) = ∑(∑ xi
i
j−1
)2
n
i=1
[-100,100] 30
F4(x) = maxi{|xi|. 1 ≤ i ≤ n} [-100,100] 30
F5(x) = ∑|xi
2
− 10 cos(2πxi) + 10|
n
i=1
[-5,5] 30
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Table 3. results of the algorithm IWO and GWO and compared with the results of hybrid algorithm
IWOGWO by using 5 elements and 1000 iterations.
Function IWO GWO IWOGWO
F1 3.48348*10-5 1.727891*10-22 0
F2 0.1421731 12.8631*10-14 0
F3 3.847*10-4 3.36267*10-2 0
F4 31.06199 21.81783*10-6 0
F5 121.29128 9.8558*10-1 0
F6 19.10598 3.83533*10-12 4.4409*10-15
F7 9. 8396308*10-3 1.09755*10-2 0
F8 25.26338 0.236355 0.63752
F9 3.9978*10-4 2.5659*10-3 0.10027
F10 -1.0316 -1.0316 -0.99311
F6(x) = −20 exp (−0.2√
1
n
∑ xi
2
n
i=1
)
− exp (
1
n
∑ cos(2πxi)
n
i=1
) + 20
+ e
[-500,500] 30
F7(x) =
1
4000
∑ xi
2
n
i=1
− ∏ cos(
xi
√i
n
i=1
) − 1
[-0.5,0.5] 30
F8(x) =
π
n
(10 sin(πyi − 1)2[1
+ 10sin2(πyi+1)] + (yn−1)2
+ ∑ U(xi
n
i=1
, 10,100,4)
yi = 1 +
Xi+1
4
u(xi, a, k, m)
= {
k(xi − a)m
xi > 0
o − a < xi < 𝑎
k(−xi − a)m
xi < −𝑎
[-pi, pi] 30
F9(x) = ∑ |ai −
xi(bi
2
+ bix2)
bi
2
+ bix2 + x4
|
211
i=1
[-15,15] 4
F10(x) = 4xi
2
− 2.1x1
4
+
1
3
x1
6
+ x1x2 − 4x2
2
+ 4x2
4
[-5,5] 2
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Table4. results of the algorithm IWO and GWO and compared with the results of hybrid algorithm
IWOGWO by using 10 elements and 1000 iterations.
Function IWO GWO IWOGWO
F1 3.38112*10-5 4.489823*10-35 0
F2 0.0247183 2.176478*10-21 0
F3 1.91002*10-3 6.74407*10-7 0
F4 32.73155 70.59953*10-8 0
F5 85.77135 1.93722000 0
F6 19.1387 3.81918*10-14 7.9936*10-15
F7 8. 865911*10-2 4.0325*10-3 0
F8 19.86204 8.82853*10-2 0.23232
F9 2.45339*10-3 4.406469*10-3 0.028729
F10 -1.0316 -1.0316 -0.13653
Table5. results of the algorithm IWO and GWO and compared with the results of hybrid algorithm
IWOGWO by using 15 elements and 1000 iterations.
Function IWO GWO IWOGWO
F1 3.31392*10-5 32.76772*10-44 0
F2 0.0252103 6.00473*10-26 0
F3 1.786933*10-4 2.4346657*10-9 0
F4 2.62742*10-3 7.18576*10-11 0
F5 76.22018 0.21267000 0
F6 19.07697 2.71782*10-14 7.9936*10-15
F7 1.377530*10-2 3.9167*10-3 0
F8 18.07077 7.62111*10-2 0.77268
F9 7.67408*10-4 4.57768*10-4 0.030296
F10 -1.0316 -1.0316 -1.0053
Table6. results of the algorithm IWO and GWO and compared with the results of hybrid algorithm
IWOGWO by using 20 elements and 1000 iterations.
Function IWO GWO IWOGWO
F1 3.31477*10-5 8.20779*10-50 0
F2 0.026135 1.531945*10-29 0
F3 1.367628*10-3 5.4470181*10-13 0
F4 2.48269*10-3 96.194778*10-12 0
F5 89.4533 0.3569500 0
F6 18.8478 2.29148*10-14 7.9936*10-15
F7 1.747331*10-2 3.3852*10-3 0
F8 18.44998 5.25017*10-2 0.47124
F9 4.7212*10-4 8.33160*10-3 0.20087
F10 -1.0316 -1.0316 -0.99192
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Table7. results of the algorithm IWO and GWO and compared with the results of hybrid algorithm
IWOGWO by using 25 elements and 1000 iterations.
Function IWO GWO IWOGWO
F1 298.640611 84.03248*10-56 0
F2 0.0353479 1.490545*10-32 0
F3 5924.11338 752.383373*10-
15
0
F4 38.67039 96.73922*10-13 0
F5 96.32194 1.13686*10-14 0
F6 18.93683 1.68753*10-14 1.1546*10-14
F7 423.89807 0 0
F8 19.1291 6.605576*10-2 0.39953
F9 6.97397*10-4 6.335574*10-3 0.050696
F10 -1.0316 -1.0316 -0.88631
6. CONCLUSIONS
In this research we have studied the algorithm of weed optimization and we have seen poor
performance. This algorithm in terms of reaching the local micro point and this indicates a
divergence from the optimal solution and to improve the performance. The original wolves
algorithm is accessible to the local micro point and moves away from the optimal solution. As for
the hybrid algorithm that avoids falling into the local IGA d. The swarm algorithm had a great
impact on improving the IWO algorithm and for this the new hybrid algorithm was proposed
IWOGWO.We have obtained very good results. The overall optimal solution has been reached
for most (10) test functions. This is what our results indicate. In the future, the authors plan to
integrate the proposed algorithm IWO with another algorithm that depended on the intelligent
swarm. Such integration will improve the performance to get more accurate results.
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