This document proposes a new variant of the differential evolution algorithm called DE New. It presents the basic differential evolution algorithm and the proposed DE New algorithm. The DE New algorithm modifies the mutation strategy to better explore the search space and solve stagnation problems. The performance of DE New is evaluated on 24 benchmark functions from 2D to 40D and is found to outperform GA, DE-PSO, and DE-AUTO on most of the benchmark functions, particularly for unimodal and multi-modal problems.
APPLYING TRANSFORMATION CHARACTERISTICS TO SOLVE THE MULTI OBJECTIVE LINEAR F...ijcsit
For some management programming problems, multiple objectives to be optimized rather than a single objective, and objectives can be expressed with ratio equations such as return/investment, operating
profit/net-sales, profit/manufacturing cost, etc. In this paper, we proposed the transformation characteristics to solve the multi objective linear fractional programming (MOLFP) problems. If a MOLFP problem with both the numerators and the denominators of the objectives are linear functions and some
technical linear restrictions are satisfied, then it is defined as a multi objective linear fractional programming problem MOLFPP in this research. The transformation characteristics are illustrated and the solution procedure and numerical example are presented.
A NEW ALGORITHM FOR SOLVING FULLY FUZZY BI-LEVEL QUADRATIC PROGRAMMING PROBLEMSorajjournal
This paper is concerned with new method to find the fuzzy optimal solution of fully fuzzy bi-level non-linear (quadratic) programming (FFBLQP) problems where all the coefficients and decision variables of both objective functions and the constraints are triangular fuzzy numbers (TFNs). A new method is based on decomposed the given problem into bi-level problem with three crisp quadratic objective functions and bounded variables constraints. In order to often a fuzzy optimal solution of the FFBLQP problems, the concept of tolerance membership function is used to develop a fuzzy max-min decision model for generating satisfactory fuzzy solution for FFBLQP problems in which the upper-level decision maker (ULDM) specifies his/her objective functions and decisions with possible tolerances which are described by membership functions of fuzzy set theory. Then, the lower-level decision maker (LLDM) uses this preference information for ULDM and solves his/her problem subject to the ULDMs restrictions. Finally, the decomposed method is illustrated by numerical example.
MULTIPROCESSOR SCHEDULING AND PERFORMANCE EVALUATION USING ELITIST NON DOMINA...ijcsa
Task scheduling plays an important part in the improvement of parallel and distributed systems. The problem of task scheduling has been shown to be NP hard. The time consuming is more to solve the problem in deterministic techniques. There are algorithms developed to schedule tasks for distributed environment, which focus on single objective. The problem becomes more complex, while considering biobjective.This paper presents bi-objective independent task scheduling algorithm using elitist Nondominated
sorting genetic algorithm (NSGA-II) to minimize the makespan and flowtime. This algorithm generates pareto global optimal solutions for this bi-objective task scheduling problem. NSGA-II is implemented by using the set of benchmark instances. The experimental result shows NSGA-II generates efficient optimal schedules.
Penalty Function Method For Solving Fuzzy Nonlinear Programming Problempaperpublications3
Abstract: In this work, the fuzzy nonlinear programming problem (FNLPP) has been developed and their result have also discussed. The numerical solutions of crisp problems and have been compared and the fuzzy solution and its effectiveness have also been presented and discussed. The penalty function method has been developed and mixed with Nelder and Mend’s algorithm of direct optimization problem solutionhave been used together to solve this FNLPP.
Keyword:Fuzzy set theory, fuzzy numbers, decision making, nonlinear programming, Nelder and Mend’s algorithm, penalty function method.
APPLYING TRANSFORMATION CHARACTERISTICS TO SOLVE THE MULTI OBJECTIVE LINEAR F...ijcsit
For some management programming problems, multiple objectives to be optimized rather than a single objective, and objectives can be expressed with ratio equations such as return/investment, operating
profit/net-sales, profit/manufacturing cost, etc. In this paper, we proposed the transformation characteristics to solve the multi objective linear fractional programming (MOLFP) problems. If a MOLFP problem with both the numerators and the denominators of the objectives are linear functions and some
technical linear restrictions are satisfied, then it is defined as a multi objective linear fractional programming problem MOLFPP in this research. The transformation characteristics are illustrated and the solution procedure and numerical example are presented.
A NEW ALGORITHM FOR SOLVING FULLY FUZZY BI-LEVEL QUADRATIC PROGRAMMING PROBLEMSorajjournal
This paper is concerned with new method to find the fuzzy optimal solution of fully fuzzy bi-level non-linear (quadratic) programming (FFBLQP) problems where all the coefficients and decision variables of both objective functions and the constraints are triangular fuzzy numbers (TFNs). A new method is based on decomposed the given problem into bi-level problem with three crisp quadratic objective functions and bounded variables constraints. In order to often a fuzzy optimal solution of the FFBLQP problems, the concept of tolerance membership function is used to develop a fuzzy max-min decision model for generating satisfactory fuzzy solution for FFBLQP problems in which the upper-level decision maker (ULDM) specifies his/her objective functions and decisions with possible tolerances which are described by membership functions of fuzzy set theory. Then, the lower-level decision maker (LLDM) uses this preference information for ULDM and solves his/her problem subject to the ULDMs restrictions. Finally, the decomposed method is illustrated by numerical example.
MULTIPROCESSOR SCHEDULING AND PERFORMANCE EVALUATION USING ELITIST NON DOMINA...ijcsa
Task scheduling plays an important part in the improvement of parallel and distributed systems. The problem of task scheduling has been shown to be NP hard. The time consuming is more to solve the problem in deterministic techniques. There are algorithms developed to schedule tasks for distributed environment, which focus on single objective. The problem becomes more complex, while considering biobjective.This paper presents bi-objective independent task scheduling algorithm using elitist Nondominated
sorting genetic algorithm (NSGA-II) to minimize the makespan and flowtime. This algorithm generates pareto global optimal solutions for this bi-objective task scheduling problem. NSGA-II is implemented by using the set of benchmark instances. The experimental result shows NSGA-II generates efficient optimal schedules.
Penalty Function Method For Solving Fuzzy Nonlinear Programming Problempaperpublications3
Abstract: In this work, the fuzzy nonlinear programming problem (FNLPP) has been developed and their result have also discussed. The numerical solutions of crisp problems and have been compared and the fuzzy solution and its effectiveness have also been presented and discussed. The penalty function method has been developed and mixed with Nelder and Mend’s algorithm of direct optimization problem solutionhave been used together to solve this FNLPP.
Keyword:Fuzzy set theory, fuzzy numbers, decision making, nonlinear programming, Nelder and Mend’s algorithm, penalty function method.
Universal Portfolios Generated by Reciprocal Functions of Price Relativesiosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Multi-Index Bi-Criterion Transportation Problem: A Fuzzy ApproachIJAEMSJORNAL
This paper represents a non linear bi-criterion generalized multi-index transportation problem (BGMTP) is considered. The generalized transportation problem (GTP) arises in many real-life applications. It has the form of a classical transportation problem, with the additional assumption that the quantities of goods change during the transportation process. Here the fuzzy constraints are used in the demand and in the budget. An efficient new solution procedure is developed keeping the budget as the first priority. All efficient time-cost trade-off pairs are obtained. D1-distance is calculated to each trade-off pair from the ideal solution. Finally optimum solution is reached by using D1-distance.
MIXED 0−1 GOAL PROGRAMMING APPROACH TO INTERVAL-VALUED BILEVEL PROGRAMMING PR...cscpconf
This paper presents how the mixed 0-1 programming in the framework of goal programming (GP) can be used to solve interval-valued fractional bilevel programming (IVFBLP) problems by employing genetic algorithm (GA) in a hierarchical decision making system. In the model formulation of the problem, a goal achievement function for minimizing the lower-bounds of the necessary regret intervals defined for the target intervals of achieving the goals and thereby arriving at a compromise decision is constructed by using both the aspects of ‘minsum’ and ‘minmax’ approaches in GP. In the decision process, an GA scheme is employed for execution
of the problems at the two stages, target interval specification and optimal decision determination, for distribution of decision powers to the decision makers (DMs) in the order of hierarchy. A numerical example is provided to illustrate the potential use of the approach.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
EXPERIMENTS ON HYPOTHESIS "FUZZY K-MEANS IS BETTER THAN K-MEANS FOR CLUSTERING"IJDKP
Clustering is one of the data mining techniques that have been around to discover business intelligence by grouping objects into clusters using a similarity measure. Clustering is an unsupervised learning process that has many utilities in real time applications in the fields of marketing, biology, libraries, insurance, city-planning, earthquake studies and document clustering. Latent trends and relationships among data objects can be unearthed using clustering algorithms. Many clustering algorithms came into existence. However, the quality of clusters has to be given paramount importance. The quality objective is to achieve
highest similarity between objects of same cluster and lowest similarity between objects of different clusters. In this context, we studied two widely used clustering algorithms such as the K-Means and Fuzzy K-Means. K-Means is an exclusive clustering algorithm while the Fuzzy K-Means is an overlapping clustering algorithm. In this paper we prove the hypothesis “Fuzzy K-Means is better than K-Means for Clustering” through both literature and empirical study. We built a prototype application to demonstrate the differences between the two clustering algorithms. The experiments are made on diabetes dataset
obtained from the UCI repository. The empirical results reveal that the performance of Fuzzy K-Means is better than that of K-means in terms of quality or accuracy of clusters. Thus, our empirical study proved the hypothesis “Fuzzy K-Means is better than K-Means for Clustering”.
Duality in nonlinear fractional programming problem using fuzzy programming a...ijscmcj
In this paper we have considered nonlinear fractional programming problem with multiple constraints. A
pair of primal and dual for a special type of nonlinear fractional programming has been considered under
fuzzy environment. Exponential membership function has been used to deal with the fuzziness. Duality
results have been developed for the special type of nonlinear programming using exponential membership function. The method has been illustrated with numerical example. Genetic Algorithm as well as Fuzzy programming approach has been used to solve the problem.
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Optimal components assignment problem subject to system reliability, total lead-time, and total cost
constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy
membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the
presented problem. The optimal solution found by the proposed approach is characterized by maximum
reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different
examples taken from the literature to illustrate its efficiency in comparison with other previous methods
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Optimal components assignment problem subject to system reliability, total lead-time, and total cost constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the presented problem. The optimal solution found by the proposed approach is characterized by maximum reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different examples taken from the literature to illustrate its efficiency in comparison with other previous methods.
A h k clustering algorithm for high dimensional data using ensemble learningijitcs
Advances made to the traditional clustering algorithms solves the various problems such as curse of
dimensionality and sparsity of data for multiple attributes. The traditional H-K clustering algorithm can
solve the randomness and apriority of the initial centers of K-means clustering algorithm. But when we
apply it to high dimensional data it causes the dimensional disaster problem due to high computational
complexity. All the advanced clustering algorithms like subspace and ensemble clustering algorithms
improve the performance for clustering high dimension dataset from different aspects in different extent.
Still these algorithms will improve the performance form a single perspective. The objective of the
proposed model is to improve the performance of traditional H-K clustering and overcome the limitations
such as high computational complexity and poor accuracy for high dimensional data by combining the
three different approaches of clustering algorithm as subspace clustering algorithm and ensemble
clustering algorithm with H-K clustering algorithm.
TRiBE! brings together seven acclaimed shows in one volume. It ranges from raucous stand up poetry to the sophisticated, hard hitting satire that has become the hallmark of Monkey Poet’s performances.
Matt Panesh fuses comedy into poetry writing and performing under the stage name "Monkey Poet". He is has taken his shows across North America and is is a regular performer at the Edinburgh Festival Free Fringe where he won the Edinburgh Fringe Editors' Award 2014.
Universal Portfolios Generated by Reciprocal Functions of Price Relativesiosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Multi-Index Bi-Criterion Transportation Problem: A Fuzzy ApproachIJAEMSJORNAL
This paper represents a non linear bi-criterion generalized multi-index transportation problem (BGMTP) is considered. The generalized transportation problem (GTP) arises in many real-life applications. It has the form of a classical transportation problem, with the additional assumption that the quantities of goods change during the transportation process. Here the fuzzy constraints are used in the demand and in the budget. An efficient new solution procedure is developed keeping the budget as the first priority. All efficient time-cost trade-off pairs are obtained. D1-distance is calculated to each trade-off pair from the ideal solution. Finally optimum solution is reached by using D1-distance.
MIXED 0−1 GOAL PROGRAMMING APPROACH TO INTERVAL-VALUED BILEVEL PROGRAMMING PR...cscpconf
This paper presents how the mixed 0-1 programming in the framework of goal programming (GP) can be used to solve interval-valued fractional bilevel programming (IVFBLP) problems by employing genetic algorithm (GA) in a hierarchical decision making system. In the model formulation of the problem, a goal achievement function for minimizing the lower-bounds of the necessary regret intervals defined for the target intervals of achieving the goals and thereby arriving at a compromise decision is constructed by using both the aspects of ‘minsum’ and ‘minmax’ approaches in GP. In the decision process, an GA scheme is employed for execution
of the problems at the two stages, target interval specification and optimal decision determination, for distribution of decision powers to the decision makers (DMs) in the order of hierarchy. A numerical example is provided to illustrate the potential use of the approach.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
EXPERIMENTS ON HYPOTHESIS "FUZZY K-MEANS IS BETTER THAN K-MEANS FOR CLUSTERING"IJDKP
Clustering is one of the data mining techniques that have been around to discover business intelligence by grouping objects into clusters using a similarity measure. Clustering is an unsupervised learning process that has many utilities in real time applications in the fields of marketing, biology, libraries, insurance, city-planning, earthquake studies and document clustering. Latent trends and relationships among data objects can be unearthed using clustering algorithms. Many clustering algorithms came into existence. However, the quality of clusters has to be given paramount importance. The quality objective is to achieve
highest similarity between objects of same cluster and lowest similarity between objects of different clusters. In this context, we studied two widely used clustering algorithms such as the K-Means and Fuzzy K-Means. K-Means is an exclusive clustering algorithm while the Fuzzy K-Means is an overlapping clustering algorithm. In this paper we prove the hypothesis “Fuzzy K-Means is better than K-Means for Clustering” through both literature and empirical study. We built a prototype application to demonstrate the differences between the two clustering algorithms. The experiments are made on diabetes dataset
obtained from the UCI repository. The empirical results reveal that the performance of Fuzzy K-Means is better than that of K-means in terms of quality or accuracy of clusters. Thus, our empirical study proved the hypothesis “Fuzzy K-Means is better than K-Means for Clustering”.
Duality in nonlinear fractional programming problem using fuzzy programming a...ijscmcj
In this paper we have considered nonlinear fractional programming problem with multiple constraints. A
pair of primal and dual for a special type of nonlinear fractional programming has been considered under
fuzzy environment. Exponential membership function has been used to deal with the fuzziness. Duality
results have been developed for the special type of nonlinear programming using exponential membership function. The method has been illustrated with numerical example. Genetic Algorithm as well as Fuzzy programming approach has been used to solve the problem.
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Optimal components assignment problem subject to system reliability, total lead-time, and total cost
constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy
membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the
presented problem. The optimal solution found by the proposed approach is characterized by maximum
reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different
examples taken from the literature to illustrate its efficiency in comparison with other previous methods
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Optimal components assignment problem subject to system reliability, total lead-time, and total cost constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the presented problem. The optimal solution found by the proposed approach is characterized by maximum reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different examples taken from the literature to illustrate its efficiency in comparison with other previous methods.
A h k clustering algorithm for high dimensional data using ensemble learningijitcs
Advances made to the traditional clustering algorithms solves the various problems such as curse of
dimensionality and sparsity of data for multiple attributes. The traditional H-K clustering algorithm can
solve the randomness and apriority of the initial centers of K-means clustering algorithm. But when we
apply it to high dimensional data it causes the dimensional disaster problem due to high computational
complexity. All the advanced clustering algorithms like subspace and ensemble clustering algorithms
improve the performance for clustering high dimension dataset from different aspects in different extent.
Still these algorithms will improve the performance form a single perspective. The objective of the
proposed model is to improve the performance of traditional H-K clustering and overcome the limitations
such as high computational complexity and poor accuracy for high dimensional data by combining the
three different approaches of clustering algorithm as subspace clustering algorithm and ensemble
clustering algorithm with H-K clustering algorithm.
TRiBE! brings together seven acclaimed shows in one volume. It ranges from raucous stand up poetry to the sophisticated, hard hitting satire that has become the hallmark of Monkey Poet’s performances.
Matt Panesh fuses comedy into poetry writing and performing under the stage name "Monkey Poet". He is has taken his shows across North America and is is a regular performer at the Edinburgh Festival Free Fringe where he won the Edinburgh Fringe Editors' Award 2014.
Интерактивный урок "Введение в алгебру"Delight2000
Урок для интерактивной доски по алгебре подготовлен в программе WizTeach. Скачать урок в формате WizTeach можно на сайте delight2000.com
http://www.delight2000.com/succes.html?id_rub=423716&obj=catalog
6197 Sayılı Eczacılar ve Eczaneler Hakkında Kanun İle 2313 Sayılı Uyuşturucu Maddelerin Murakabesi Hakkında Kanunda Değişiklik Yapılmasına Dair Kanun Teklifinin 03.05.2012 tarihinde TBMM Sağlık, Aile, Çalışma ve Sosyal İşler Komisyonunda kabul edilerek TBMM Genel Kurulunda görüşülecek olan son hali
Terapia cognitivo conductual según Aaron Beck. Biografía, conceptos básicos, distorsiones cognitivas, tipos de pensamientos automáticos, abordaje terapéutico, áreas de utilidad.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...cscpconf
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
Interactive Fuzzy Goal Programming approach for Tri-Level Linear Programming ...IJERA Editor
The aim of this paper is to present an interactive fuzzy goal programming approach to determine the preferred
compromise solution to Tri-level linear programming problems considering the imprecise nature of the decision
makers’ judgments for the objectives. Using the concept of goal programming, fuzzy set theory, in combination
with interactive programming, and improving the membership functions by means of changing the tolerances of
the objectives provide a satisfactory compromise (near to ideal) solution to the upper level decision makers.
Two numerical examples for three-level linear programming problems have been solved to demonstrate the
feasibility of the proposed approach. The performance of the proposed approach was evaluated by using of
metric distance functions with other approaches.
An optimal design of current conveyors using a hybrid-based metaheuristic alg...IJECEIAES
This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing a hybrid algorithm named DE-ACO, which is derived from the combination of differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea of this hybridization is to apply the DE algorithm for the ACO algorithm’s initialization stage. Benchmark test functions were used to evaluate the proposed algorithm’s performance regarding the quality of the optimal solution, robustness, and computation time. Furthermore, the DE-ACO has been applied to optimize the CCII+ performances. SPICE simulation is utilized to validate the achieved results, and a comparison with the standard DE and ACO algorithms is reported. The results highlight that DE-ACO outperforms both ACO and DE.
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.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
MULTI-OBJECTIVE ENERGY EFFICIENT OPTIMIZATION ALGORITHM FOR COVERAGE CONTROL ...ijcseit
Many studies have been done in the area of Wireless Sensor Networks (WSNs) in recent years. In this kind of networks, some of the key objectives that need to be satisfied are area coverage, number of active sensors and energy consumed by nodes. In this paper, we propose a NSGA-II based multi-objective algorithm for optimizing all of these objectives simultaneously. The efficiency of our algorithm is demonstrated in the simulation results. This efficiency can be shown as finding the optimal balance point among the maximum coverage rate, the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
Data clustering is a common technique for statistical data analysis; it is defined as a class of
statistical techniques for classifying a set of observations into completely different groups. Cluster analysis
seeks to minimize group variance and maximize between group variance. In this study we formulate a
mathematical programming model that chooses the most important variables in cluster analysis. A nonlinear
binary model is suggested to select the most important variables in clustering a set of data. The idea of the
suggested model depends on clustering data by minimizing the distance between observations within groups.
Indicator variables are used to select the most important variables in the cluster analysis.
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.
A HYBRID COA/ε-CONSTRAINT METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of
this method is the high accuracy and the dispersion of its Pareto frontier. In order to testing the efficiency of the suggested method, a lot of test problems have been solved using this method. Comparing the results of this method with the results of other similar methods shows that the Cuckoo algorithm is more suitable for solving the multi-objective problems.
Hybrid method for achieving Pareto front on economic emission dispatch IJECEIAES
In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multiobjective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.
A HYBRID COA/ε-CONSTRAINT METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed
method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem
transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will
optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of
this method is the high accuracy and the dispersion of its Pareto frontier. In order to testing the efficiency
of the suggested method, a lot of test problems have been solved using this method. Comparing the results
of this method with the results of other similar methods shows that the Cuckoo algorithm is more suitable
for solving the multi-objective problems.
1. Variant of Differential Evolution Algorithm
Richa Shukla, Bramah Hazela, Shashwat Shukla, Ravi Prakash*,
Krishn Kumar Mishra*
Computer Science and Engineering Department
Amity University , Lucknow India, 226028 and
Computer Science and Engineering Department
MNNIT Allahabad, Allahabad India, 211004*
Email: richamity22@gmail.com, bhazela@lko.amity.edu, sshukla1@lko.amity.edu,
raviprakashguddu@gmail.com, kkm@mnnit.ac.in
Abstract. Differential Evolution is a nature inspired optimization tech-
nique. It has been achieved best solutions on large area of test suits.
DE algorithm is efficient in programming and it has broad applicability
in engineering. This paper presents modified mutation vector genera-
tion strategy of basic DE for solving stagnation problem. A new vari-
ant of differential evolution that is DE New has been proposed and the
performance of DE New is tested in COmparing Continuous Optimis-
ers(COCO) framework on 24 benchmark functions and found DE New
has better exploration performance in search space in comparison to
GA, DE- PSO, DE-AUTO on Black-Box Optimization Benchmarking
(BBOB) 2015 devised by COCO.
Keywords DE New, Exploitation, Robot Kidnapping, Differential Evolution,
mean-cost.
1 Introduction
Optimization problem can be solved with the help of different types of algo-
rithms[1][12]. In this scope reputation of Evolutionary Algorithm(EA) is un-
beatable[2][3][4]. DE is an EA that is used for solving many- modal optimization
problem in continuous search space[5][7]. DE algorithm is efficient and based
on natural selection with globally strong optimization technique in continu-
ous search space[8]. DE logic works with the help of DE operators (mutation,
crossover and selection) and associated parameters[9][10][11]. Myths related to
GA and DE creates due to similarity of the operators. In GA, crossover is the
main operator but in DE mutation operator plays leader role for solving global
optimization problem[13][14]. DE is better in compare to GA because it dose not
use binary encoding[15][4] and probability density function[16]. DE variants are
very helpful for enhancing the vector position, resolve the stagnation problem
and increase the convergence speed. DE basically works on global search space.
So for better exploration of solution, here DE New navigates to investigate the
local optimum problem for solving blocked solutions. Further content of this
2. paper has been ordered as below section 2 defines Differential Evolution, sec-
tion 3 states existing and modified algorithm, section 4 presents proposed work,
practical approach of result analysis has been elaborated in section 5 and finally
conclusions are drawn in section 6.
2 Differential Evolution
Differential Evolution is a randomized, population based, meta-heuristic, nature
inspired[17] and global optimization technique[18] given by Storn and Price in
1995[6]. Mutation, crossover and selection are the DE operators[19] but DE algo-
rithm is fully depend on mutation operator. The role of mutation operator is to
generate a mutant vector and the role of crossover is to develop trial vector and
the aim of selection operator is to select best between trial vector and mutant
vector for each generation.
Best example of DE is that it uses in mobile robot systems[20] for determining
the position and orientations of the robot. The way of initialization of the robot
position and track the position is the concept of Robot kidnapping(particle kid-
napping)[21][22][23]. Robot kidnapping understands the position and movement
of robot path without telling the new position[18].
Brief description of DE operators are given below:
1.Mutation: In this operator generate a mutant vector. Select three vectors
randomly from the search space and multiply the difference of two vectors with
a constant value ’F’(range of F is in between 0-2) and add with third vector that
produces a mutant vector mi,g , this process is called mutation. mutant vector
mi,g for each generation is stated as:
xi,g=xp+F(xq-xr) (1)
Notations:
1. i=1,2,......population size.
2. p, q, r are the three random vectors of the search space for ’g’ genera-
tion[24][27].
2.Crossover: After generating of mutant vector, create trial vector with the
help of mutant operator by recombining of two different vectors.
ti,g+1=t1i,g,t2i,g,t3i,g...........................tni,g (2)
The overall equation of trial vector for crossover operator is divided in two
phases , compare randomly generated number with crossover constant value
If random number generated is ≤ CR then we will use mutant vector mi,g
for each generation otherwise choose target vector without altering parent vec-
tor[25][26][28].
3.Selection: Selection of new member is totally depend on selection operator.
Comparison between trial vector and mutant vector, must select best between
these two vector ti,g and xi,g is the aim of selection operator.
3. 3 Existing and Modified DE Algorithms
Algorithm 1 Basic Differential Evolution Algorithm
1: for Each particle i do
2: for each vectors xi,g of population N do
3: MUTATION
4: select three random vectors from search space for mutation
5: xi,g=xp+F(xq-xr)
6: CROSS-OVER
7: if random number generated is ≤ CR then select mi,g
8: else select target vector
9: end if
10: SELECTION
11: evaluate fitness of xi,g and ti,g+1
12: choose best fitness as solution and discard previous
13: End if termination conditions met
14: end for
15: end for
Algorithm 2 Proposed Differential Evolution Algorithm
1: for Each particle i do
2: for each vectors xi,g of population N do
3: MUTATION
4: select xp, xq, xr
5: Fix first vector xp
6: where xq > mean-cost and xr< mean-cost
7: xi,g=xp+F(xq-xr)
8: CROSS-OVER
9: if random number generated is ≤ CR then
10: select mi,g for each generation
11: else select target vector
12: end if
13: SELECTION
14: evaluate fitness of xi,g and ti,g+1
15: choose best fitness as solution and discard previous
16: End if termination conditions met
17: end for
18: end for
4 Proposed Work
In DE algorithm mutation and associated parameters maintain the performance
of search space. There are many solutions in search space that are blocked,
4. stagnated, trapped and unable to give better solution for multi-modal prob-
lems in global search space. So for solving this problem DE NEW algorithm has
been proposed. In this algorithm, strategy of basic mutation operator has been
changed and a new variant of DE algorithm (DE NEW) has been proposed in
this paper. The logic of proposed DE NEW algorithm is to select three vectors
from the search space xp, xq, xr. Calculate the mean-cost and on the basis of
mean-cost, search space has been divided into two parts. Select xpvector ran-
domly from the total population size and stored in index number c, it will be the
fixed vector in each iterations. Then select next two vectors xq and xr. Choose
xq randomly from the above mean cost and stored in index number a and select
xr randomly from the below mean cost and stored in index number b.The new
strategy of mutation operator has been used for providing better exploration of
solution and greater diversity of solutions in search space in compare to GA, DE-
PSO, DE-AUTO on Black-Box Optimization Benchmarking (BBOB)[29] 2015
devised by COCO.
Fig. 1: DE NEW
5 Result Analysis
The three popular algorithm’s have been compared with tested performance of
DE New. In below table the rank has been alloted for proposed variant and
noiseless results have been attached. Its performance decreased might be due
to lower value of randomized F, Cr etc. But in this paper, DE New has been
succeeded to give better results for each modal in 24 benchmark functions in
2D, 3D, 5D, 10D, 20D and 40D.
6. Table 1: Ranking of Proposed Variant
DIMENSIONS hcond lcond mult2 multi separable
2D 2nd 2nd 2nd 1st 1st
3D 2nd 2nd 3rd 2nd 1st
5D 2nd 2nd 2nd 2nd 2nd
10D 2nd 2nd 2nd 2nd 2nd
20D 2nd 2nd 2nd 1st 2nd
40D 2nd 2nd 4th 1st 2nd
6 Conclusion
In this paper, proposed variant of DE algorithm has been based on modified mu-
tation strategy. It gives best performance for unimodal and multi-modal prob-
lems and increase the convergence rate and resolve the stagnation problems.
In DE New unlike basic DE and other algorithms, it can get best optimization
performance. DE New is able to give satisfactory performance for DE-AUTO
and best performance for DE-PSO, GA in 2D, 3D, 5D, 10D, 20D and 40D on
various benchmark functions, however there are a lot of ways for improving it.
In future work, DE algorithm can be used in cost estimation of the softwares
and implemented in many applications. Also merge two or more Evolutionary
Algorithm for achieving desired results.
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