Non-Dominated Sorting Genetic Algorithm

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Enhanced non-dominated sorting genetic algorithm for test case optimization

Enhanced non-dominated sorting genetic algorithm for test case optimization

iii. How to evaluate the effectiveness of the identified technique in reducing the number of test cases? 1.4 Research Aim and Objectives This research aims to enhance existing optimization technique in regression GUI testing using Non-Dominated Sorting Genetic Algorithm (NSGA II) by implementing fitness scaling approach alongside with Pareto-Ranking approach to the algorithm which may produce more optimize GUI test cases. Based on the research aim mentioned, several objectives are generated as guidance for this research. The objectives of this research are:
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Hybrid non-dominated sorting genetic algorithm with adaptive operators selection

Hybrid non-dominated sorting genetic algorithm with adaptive operators selection

Multiobjective optimization entails minimizing or maximizing multiple objective functions subject to a set of constraints. Many real world applications can be formulated as multi-objective optimization problems (MOPs), which often involve multiple con- flicting objectives to be optimized simultaneously. Recently, a number of multi-objective evolutionary algorithms (MOEAs) were developed suggested for these MOPs as they do not require problem specific information. They find a set of non-dominated so- lutions in a single run. The evolutionary process on which they are based, typically relies on a single genetic operator. Here, we suggest an algorithm which uses a basket of search operators. This is because it is never easy to choose the most suitable operator for a given problem. The novel hybrid non-dominated sorting genetic algorithm (HNSGA) introduced here in this paper and tested on the ZDT (Zitzler-Deb-Thiele) and CEC’09 (2009 IEEE Conference on Evolutionary Computations) benchmark prob- lems specifically formulated for MOEAs. Numerical results prove that the proposed algorithm is competitive with state-of-the-art MOEAs.
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A New Non-dominated Sorting Genetic Algorithm for Multi-Objective Optimization

A New Non-dominated Sorting Genetic Algorithm for Multi-Objective Optimization

Multi-objective optimization (MO) is a highly demanding research topic because many real- world optimization problems consist of contradictory criteria or objectives. Considering these competing objectives concurrently, a multi-objective optimization problem (MOP) can be formulated as finding the best possible solutions that satisfy these objectives under different tradeoff situations. A family of solutions in the feasible solution space forms a Pareto-optimal front, which describes the tradeoff among several contradictory objectives of an MOP. Generally, there are two goals in finding the Pareto-optimal front of a MOP: 1) to converge solutions as near as possible to the Pareto-optimal front; and 2) to distribute solutions as diverse as possible over the obtained non-dominated front. These two goals cause enormous search space in MOPs and let deterministic algorithms feel difficult to obtain the Pareto-optimal solutions. Therefore, satisfying these two goals simultaneously is a principal challenge for any algorithm to deal with MOPs (Dias & Vasconcelos, 2002). In recent years, several evolutionary algorithms (EAs) have been proposed to solve MOPs. For example, the strength Pareto evolutionary algorithm (SPEA) (Zitzler et al., 2000) and the revised non-dominated sorting genetic algorithm (NSGA-II) (Deb et al., 2002) are two most famous algorithms. Several extensions of genetic algorithms (GAs) for dealing with MOPs are also proposed, such as the niche Pareto genetic algorithm (NPGA) (Horn et al., 1994), the chaos-genetic algorithm (CGA) (Qi et al., 2006), and the real jumping gene genetic algorithm (RJGGA) (Ripon et al., 2007).
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Non Dominated Sorting Genetic Algorithm Based Energy Aware Clustering Protocol

Non Dominated Sorting Genetic Algorithm Based Energy Aware Clustering Protocol

Non Dominated Sorting Genetic Algorithm Based Energy Aware Clustering Protocol Roubaldeep Kaur, Supreet Kaur Abstract: Energy conservation is defined as an ill posed problem in wireless sensor networks. Many protocols have been proposed improve the energy conservation. But, it has been found that the actual most of the existing methods has got neglected very first debris difficulty with particle swarm optimization based protocols. As inadequately selected debris bring about poor results. Genetic algorithm based protocols would not ensure that the world global optimized final results however wealthy for the mutation and also crossover operators. . The use of the Non dominated sorted genetic algorithm (NSGA) is ignored to efficiently elect the inter cluster data aggregation path selection. Therefore, in order to remove these issues NSGA based inter cluster data aggregations proposed in this work Principle betterment has become produced by changing the actual the particle swarm optimization with NSGA based optimization technique for energy efficient routing. Also, the actual utilization of the compressive stinking additionally raises the functionality further. A compressive stinking makes use of details union to eradicate well not required details via sensor nodes. Ultimately, comparison research show that the proposed technique significantly reduce the energy consumption and therefore improve the network lifetime.
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Efficient Constellation Design Based on Improved Non-dominated Sorting Genetic Algorithm-II

Efficient Constellation Design Based on Improved Non-dominated Sorting Genetic Algorithm-II

mutation, with diversity of population, make the algorithm get better behaviors in convergence and diversity of finding solutions. Based on the methods, an improvement NSGA-II is presented to design constellation in the paper. The algorithm uses fixed length chromosome representation. Real coding is adopted for that the problem has both integer continuous variables. Combining the coverage assessment criterions, an orbit parameters optimization framework based on non-dominated sorting genetic algorithm (NSGA-II) was proposed. This method is applied to a detailed example, and result shows a group of Pareto solutions with good spread can be achieved, which gives strong support to constellation scheme determination.
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Multi-objective single agent stochastic search in non-dominated sorting genetic algorithm

Multi-objective single agent stochastic search in non-dominated sorting genetic algorithm

Keywords: multi-objective optimization, Pareto set, non-dominated sorting genetic algorithm, single agent stochastic search. 1 Introduction Global optimization problems can be found in various fields of science and industry, i.e. mechanics, economics, operational research, control engineering, project management, etc. In general, global optimization is a branch of applied mathematics that deals with finding “the best available” (usually minimum or maximum) values of a given objective function, according to a single criterion. Without reducing the generality further we will focus on the case of minimization, since any maximization problem can be easily trans- formed to a minimization one.
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Solving a New Multi-objective Inventory-Routing Problem by a Non-dominated Sorting Genetic Algorithm

Solving a New Multi-objective Inventory-Routing Problem by a Non-dominated Sorting Genetic Algorithm

6. CONCLUSION This paper has addressed the multi-period, multi-product inventory-routing problem with the aim of minimizing the total system costs and transportation risks. For this problem, it is assumed that numerous products are transported to a set of retailers, through direct distribution using a fleet of heterogeneous vehicles with limited capacities. Due to the high computational complexity of the problem in this paper, a non-dominated sorting genetic algorithm (NSGA-II) has been used along with - constraint method for sample problems. The efficiency of the proposed NSGA-II has been compared to the - constraint method using several randomly generated sample problems. In small-sized problems, the multi- objective meta-heuristic algorithm, namely NSGA-II, has roughly found good Pareto-optimal solutions than the
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Weight and deflection optimization of Cantilever Beam using a modified Non-Dominated sorting Genetic Algorithm

Weight and deflection optimization of Cantilever Beam using a modified Non-Dominated sorting Genetic Algorithm

In the present paper, an improved methodology for the multi-objective optimization of cantilever beam structure. A modified form of multi-objective genetic algorithm, based on the elitist non-dominated sorting genetic algorithm (NSGA-II), is implemented to obtain Pareto-optimal designs for the chosen conflicting objectives. It explores the optimal design of a cantilever beam for minimization of weight and deflection, with the constraint that the developed maximum stress σ is less than the allowable strength S y and the end deflection δ is smaller than a specified limit δ max .
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MULTI-OBJECTIVE OPTIMIZATION OF LAMINATED COMPOSITE PLATE USING A NON-DOMINATED SORTING GENETIC ALGORITHM

MULTI-OBJECTIVE OPTIMIZATION OF LAMINATED COMPOSITE PLATE USING A NON-DOMINATED SORTING GENETIC ALGORITHM

Abstract Laminated composite constructions of panels and other structural elements are currently being used for many applications in aerospace, automotive, civil and defence industries. Laminated composites have general advantages over more traditional materials such as greater specific strength, specific stiffness, corrosion and fatigue resistance among others. Optimization of composite laminates with respect to ply angels to maximize the strength is necessary to realize the full potential of fiber reinforced materials. In this paper a modified Non- Dominated sorting Genetic Algorithm is used to obtain pareto-optimal design of composite laminate square plate. The objectives are to minimize the weight and deflection of graphite/epoxy square plate subjected to the constraint that the Tsai-Wu failure factor (ζ) should be less than or equal to one. The multi-objective optimization algorithm used in this paper has better sorting, incorporates elitism and no sharing parameter needs to be chosen a priori.
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Self-coach: an intelligent WBAN system for heart disease prediction using non-dominated sorting genetic algorithm

Self-coach: an intelligent WBAN system for heart disease prediction using non-dominated sorting genetic algorithm

Abstract Abstract Wireless Body Area Network (WBAN) is a new technology based on an advanced healthcare system and Wireless Sensor Network (WSN). This domain has been designed for monitoring patients using their physical signals by providing low- cost, wearable, unobtrusive solutions for the continuous monitoring of cardiovascular health and physical activity status. Recent studies have addressed the use of WBAN, by means of real-time comprehensive health monitoring systems such as the Personal Health Monitoring system (PHM). The aim of utilizing these systems is to provide a fast and early diagnosis; however, WBAN has not fulfilled its potential while using the existing methods of machine learning and data mining techniques. The proposed method is a new framework for early heart failure prediction systems based on an intelligent WBAN named Self-Coach. Self-Coach is an intelligent monitoring system due to the detection/prediction method it uses, which provides real-time health status monitoring using the Non-Dominated Sorting Genetic Algorithm.
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A novel approach to multi objective OPF 
		by a new parallel non dominated Sorting Genetic Algorithm considering diverse constraints

A novel approach to multi objective OPF by a new parallel non dominated Sorting Genetic Algorithm considering diverse constraints

Transient stability constrained optimal power flow (TSCOPF) is able to reduce costs while keeping the operation point away from the stability boundary. While especially useful in modern power system operations, TSCOPF problems are practically very hard to solve; unacceptable computational time is considered to be one of the largest barriers in applying TSCOPF-based solutions. The basic idea of the proposed method is to model transient stability as an objective function rather than an inequality constraint and consider classic Transient Stability Constrained OPF (TSCOPF) as a tradeoffs procedure using Pareto ideology. Second, a parallel elitist Non-dominated Sorting Genetic Algorithm II (NSGA- II) is used to solve the proposed multi-objective optimization problem; the parallel algorithm shows an excellent acceleration effect and provides a set of Pareto optimal solutions for decision makers to select. Case study results demonstrate the proposed multi-objective algorithm in bus system is quite strategic.
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The objective design of triangular bucket for dam's spillway using Non-dominated Sorting Genetic Algorithm II: NSGA-II

The objective design of triangular bucket for dam's spillway using Non-dominated Sorting Genetic Algorithm II: NSGA-II

Ski jump; NSGA-II algorithm; Karoon III dam. Abstract. Ski jump is one of the most eective structures in energy dissipation over spillways. Spillways have long been of practical importance to safety of dams. The major criteria in hydraulic design are based on the analytical and empirical methods. In the current study, in order to increase chute spillway eciency, a multi-objective evolutionary algorithm known as the Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been utilized to design the optimal triangular bucket angle and chute width. In design method, two separate objective functions have been used. In the rst objective function, equations of dynamic pressure of the bucket, the jet length after bucket, and the scour depth have been used. The second objective function is related to construction volume of chute spillway.
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Multi-Objective Optimization of a Spring Diaphragm Clutch on an Automobile Based on the Non-Dominated Sorting Genetic Algorithm (NSGA-II)

Multi-Objective Optimization of a Spring Diaphragm Clutch on an Automobile Based on the Non-Dominated Sorting Genetic Algorithm (NSGA-II)

3 School of Mechanical and Vehicular Engineering, Beijing Institute of Technology, Beijing 100081, China; junjun_zhu_88@163.com * Correspondence: zhou1987g@suse.edu.cn Abstract: The weight coefficients of the diaphragm spring depend on experiences in the traditional optimization. However, this method not only cannot guarantee the optimal solution but it is also not universal. Therefore, a new optimization target function is proposed. The new function takes the minimum of average compress force changing of the spring and the minimum force of the separation as total objectives. Based on the optimization function, the result of the clutch diaphragm spring in a car is analyzed by the non-dominated sorting genetic algorithm (NSGA-II) and the solution set of Pareto is obtained. The results show that the pressing force of the diaphragm spring is improved by 4.09%by the new algorithmand the steering separation force is improved by 6.55%, which has better stability and steering portability. The problem of the weight coefficient in the traditional empirical design is solved. The pressing force of the optimized diaphragm spring varied slightly during the abrasion range of the friction film, and the manipulation became remarkably light.
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Digital Image Steganography using Non Dominated Sorting Genetic Algorithm (NSGA) within Discrete Wavelet Transform (DWT)

Digital Image Steganography using Non Dominated Sorting Genetic Algorithm (NSGA) within Discrete Wavelet Transform (DWT)

data in different format such as text, images, audio, video etc. Image steganography is widely used technique and various methods have been developed to hide the secret information in images. Conventional techniques used for steganography LSB and DCT possess high hiding capacity and imperceptibility but these are not secure. In this thesis, a new approach is proposed in which discrete wavelet transform (DWT) is applied on cover image to get wavelet coefficient and singular value decomposition (SVD) is applied to get singular values. Secret image is scrambled using chaos and then embedded into these singular values using non dominated sorting genetic algorithm. The main goal of the presented method is to increase the embedding capacity and improve the image quality of the stego-image. Scrambling is used to make the algorithm more secure. The experimental results show better performance of the proposed method compared to the corresponding methods, in terms of PSNR. The effectiveness of the model is estimated from the viewpoint of both the amount of data hidden and the image quality of the cover image.
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Multi objective constrained algorithm 
		(MCA) and non dominated sorting genetic algorithm (NSGA ii) for solving 
		multi objective crop planning problem

Multi objective constrained algorithm (MCA) and non dominated sorting genetic algorithm (NSGA ii) for solving multi objective crop planning problem

Non-dominated sorting genetic algorithm (NSGA-II) The non-dominated sorting genetic algorithm is one of the well-known multi-objective optimization algorithms. The difference between the conventional single objective GA and NSGA-II lies with the assignment of fitness of an individual. There have been several improvements to the original algorithm and the latest form is referred as NSGA-II [15]. The fitness of an individual in NSGA-II is based on the non-domination level of an individual within a population size of M. Selection, recombination, and mutation operators are used to create a child population of size M. Thereafter, the total population (M parents and M children) is sorted according to non- domination. The new parent population is formed by adding solutions from the first front and continuing to other fronts successively till the size exceeds the population size of M. The crowded comparison operator comes into play if the number of solutions at a particular non-domination level exceeds the number that can be accommodated in the new parent population. Diversity is preserved by the use of crowded comparison criterion in the selection and in the phase of population reduction.
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Optimal Placement of an Unified Power Flow Controller in a Transmission Network by Unified Non Dominated Sorting Genetic Algorithm-III and Differential Evolution Algorithm

Optimal Placement of an Unified Power Flow Controller in a Transmission Network by Unified Non Dominated Sorting Genetic Algorithm-III and Differential Evolution Algorithm

Oloulade Arouna, Moukengue Imano Adolphe, Fifatin François-Xavier, Ganye Sewlan Amedee Arcadius, Badarou Ramanou, Vianou Antoine, Tamadaho Herman. Optimal Placement of an Unified Power Flow Controller in a Transmission Network by Unified Non Dominated Sorting Genetic Algorithm-III and Differential Evolution Algorithm. International Journal of Electrical Components and Energy Conversion. Vol. 5, No. 1, 2019, pp. 10-19. doi: 10.11648/j.ijecec.20190501.13

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Modeling and optimization of spinning conditions for polyethersulfone hollow fiber membrance fabrication using non-dominated sorting genetic algorithm-II

Modeling and optimization of spinning conditions for polyethersulfone hollow fiber membrance fabrication using non-dominated sorting genetic algorithm-II

ABSTRACT Optimization of spinning conditions plays a key role in the development of high performance asymmetric hollow fiber membranes. However, from previous studies, in solving these spinning condition optimization problems, they were handled mostly by using an experimentation that varied one of the independent spinning conditions and fixed the others. The common problem is the preparation of hollow fiber membranes that cannot be performed effectively due to inappropriate settings of the spinning conditions. Moreover, complexities in the spinning process have increased where the interaction effects between the spinning conditions with the presence of multiple objectives also affect the optimal spinning conditions. This is one of the main reasons why very little work has been carried out to vary spinning conditions simultaneously. Hence, in order to address these issues, this study focused on a non-dominated sorting genetic algorithm-II (NSGA-II) methodology to optimize the spinning conditions during the fabrication of polyethersulfone (PES) ultrafiltration hollow fiber membranes for oily wastewater treatment to maximize flux and rejection. Spinning conditions that were investigated were dope extrusion rate (DER), air gap length (AGL), coagulation bath temperature (CBT), bore fluid ratio (BFR), and post-treatment time (PT). First, the work was focused on predicting the performance of hollow fiber membranes by considering the design of experiments (DOE) and statistical regression technique as an important approach for modeling flux and rejection. In terms of experiments, a response surface methodology (RSM) and a central composite design (CCD) were used, whereby the factorial part was a fractional factorial design with resolution V and overall, it consisted
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Multi-objective Batch Scheduling in Collaborative Multi-product Flow Shop System by using Non-dominated Sorting Genetic Algorithm

Multi-objective Batch Scheduling in Collaborative Multi-product Flow Shop System by using Non-dominated Sorting Genetic Algorithm

Computer Engineering, Faculty of Electrical Engineering Telkom University, Bandung, Indonesia Abstract—Batch scheduling is a well-known topic that has been studied widely with various objectives, methods, and circumstances. Unfortunately, batch scheduling in a collaborative flow shop system is still unexplored. All studies about batch scheduling that are found were in a single flow shop system where all arriving jobs come from single door. In a collaborative flow shop system, every flow shop handles its own customers although joint production among flow shops to improve efficiency is possible. This work aims to develop a novel batch scheduling model for a collaborative multi-product flow shop system. Its objective is to minimize make-span and total production cost. This model is developed by using non-dominated sorting genetic algorithm (NSGA II) which is proven in many multi objective optimization models. This model is then compared with the non-collaborative models which use NSGA II and adjacent pairwise interchange algorithm. Due to the simulation result, the proposed model performs better than the existing models in minimizing the make-span and total production cost. The make-span of the proposed model is 10 to 17 percent lower than the existing non-collaborative models. The total production cost of the proposed model is 0.3 to 3.5 percent lower than the existing non-collaborative models.
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Multi-Objective Optimal Power Flow Problem Considering Different
              Constraints Based on Parallel Non-Dominated Sorting Genetic Algorithm- 
              II

Multi-Objective Optimal Power Flow Problem Considering Different Constraints Based on Parallel Non-Dominated Sorting Genetic Algorithm- II

IV Simulation Results: IEEE 39 bus system was used for testing Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and genetic Algorithm (GA), the result were obtained. A multi-objective model is proposed in this paper as an effective quantitative analysis tool for Optimal Power Flow (OPF) associated with cost, security and transient stability. The obtained Parento optimal solution set, rather than a single strictly transient stable solution for decision makers to select their ideal schemes according to different preference. This is essentially different from traditional TSCOPF and the proposed method is considered to be able to get a theoretically strictly transient stable solution as well if enough iterations of evolution are carried out. The objective such as minimizing fuel cost and improvement of voltage in power system ware obtained. The parameter used by NSGA-II is shown in Table 1. The results prove the NSGA-II is more efficient than the conventional method.
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Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm

Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm

Feature subset selection is the problem of selecting a subset of features from a larger set of features based on some optimization criteria. Some of the features in the larger set may be irrelevant or mutually redundant. Each feature has an associated measurement cost and risk. So, an irrelevant or redundant feature can increase the cost and risk unnecessarily. The choice of features that represent any data affects several aspects including [15]: Accuracy: The features that describing the data must capture the information necessary for the classification. Hence, regardless of the learning algorithm, the amount of information given by the features limits the accuracy of the classification function learned. Required learning time: The features describing the data implicitly determine the search space that the learned algorithm must explore. An abundance of irrelevant features can unnecessarily increase the size of the search space and hence the time needed for learning a sufficiently accurate classification function. Cost: There is a cost associated with each feature of the data. In medical diagnosis, for example, the data consists of various diagnostic tests. These tests have various costs and risks; for instance, an invasive exploratory surgery can be much more expensive and risky than, say, a blood test. Taking into consideration the above mentioned aspects that are affected by the selection of feature subset, the main objectives for feature subset selection are:
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