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Improving NSGA-II with an Adaptive Mutation Operator  [PDF]
Arthur Carvalho,Aluizio F. R. Araujo
Computer Science , 2013, DOI: 10.1145/1570256.1570387
Abstract: The performance of a Multiobjective Evolutionary Algorithm (MOEA) is crucially dependent on the parameter setting of the operators. The most desired control of such parameters presents the characteristic of adaptiveness, i.e., the capacity of changing the value of the parameter, in distinct stages of the evolutionary process, using feedbacks from the search for determining the direction and/or magnitude of changing. Given the great popularity of the algorithm NSGA-II, the objective of this research is to create adaptive controls for each parameter existing in this MOEA. With these controls, we expect to improve even more the performance of the algorithm. In this work, we propose an adaptive mutation operator that has an adaptive control which uses information about the diversity of candidate solutions for controlling the magnitude of the mutation. A number of experiments considering different problems suggest that this mutation operator improves the ability of the NSGA-II for reaching the Pareto optimal Front and for getting a better diversity among the final solutions.
Modified NSGA-II for a Bi-Objective Job Sequencing Problem  [PDF]
Susmita Bandyopadhyay
Intelligent Information Management (IIM) , 2012, DOI: 10.4236/iim.2012.46036
Abstract: This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation algorithm and has been applied on a bi-objective job sequencing problem. The objectives are the minimization of total weighted tardiness and the minimization of the deterioration cost. The results of the proposed algorithm have been compared with those of original NSGA-II. The comparison of the results shows that the modified NSGA-II performs better than the original NSGA-II.
Multi-Objective Optimization Problems with Arena Principle and NSGA-II  [PDF]
Wang Dong-Feng,Xu Feng
Information Technology Journal , 2010,
Abstract: Existing test problems for multi-objective optimization are mainly criticized for high computational complexity. In this study, we introduce a new non- dominated sorting algorithm based on Pareto optimal solutions which alleviates the problem of high computational complexity in NSGA-II. We use the Arena Principle in NSGA-II to retain the non-dominated solutions found during the evolutionary process. The main goal of this work is to keep the fast convergence exhibited by Arena Principle in global optimization when extending this heuristic to multi-objective optimization. The algorithm’s computational complexity is O(rmN). We adopt two standard test functions and simulation results show that the Arena Principle is able to find more useful and better spread of solutions.
Optimization of axial compressor stage using NSGA-II technique  [PDF]
G. Chaitanya,J. Suresh Kumar,K Srinivas
Journal of Engineering and Applied Sciences , 2010,
Abstract: Efficiency and Stage Weight [Inlet stage specific Area] are two important design issues which demand specific attention in the design of aero space compressors. In this paper these two objectives were optimized using elitist multi objective genetic algorithm, otherwise known as NSGA-II (Non dominated sorted Genetic Algorithm-II) which was developed by Kalyan Moy Deb [2002]. Lingen Chen and Fengrui Sun (2005) implemented optimum design of a subsonic axial flow compressor stage using mean line prediction method and taking 12 design variables and three objective functions. In the present approach two objective functions were formulated taking 5 design variables into account. The results showing optimal front for the two objectives problem is presented and the sensitivity analysis results of influencing design variables are shown.
Optimization of ECM Process Parameters Using NSGA-II  [PDF]
Chinnamuthu Senthilkumar, Gowrishankar Ganesan, Ramanujam Karthikeyan
Journal of Minerals and Materials Characterization and Engineering (JMMCE) , 2012, DOI: 10.4236/jmmce.2012.1110091
Abstract: Electrochemical machining (ECM) could be used as one of the best non-traditional machining technique for machining electrically conducting, tough and difficult to machine material with appropriate machining parameters combination. This paper attempts to establish a comprehensive mathematical model for correlating the interactive and higher-order influences of various machining parameters on the predominant machining criteria, i.e. metal removal rate and surface roughness through response surface methodology (RSM). The adequacy of the developed mathematical models has also been tested by the analysis of variance (ANOVA) test. The process parameters are optimized through Nondominated Sorting Genetic Algorithm-II (NSGA-II) approach to maximize metal removal rate and minimize surface roughness. A non-dominated solution set has been obtained and reported.
Voltage Stability Constrained Optimal Power Flow Using NSGA-II  [PDF]
Sandeep Panuganti, Preetha Roselyn John, Durairaj Devraj, Subhransu Sekhar Dash
Computational Water, Energy, and Environmental Engineering (CWEEE) , 2013, DOI: 10.4236/cweee.2013.21001

Voltage stability has become an important issue in planning and operation of many power systems. This work includes multi-objective evolutionary algorithm techniques such as Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm II (NSGA II) approach for solving Voltage Stability Constrained-Optimal Power Flow (VSC-OPF). Base case generator power output, voltage magnitude of generator buses are taken as the control variables and maximum L-index of load buses is used to specify the voltage stability level of the system. Multi-Objective OPF, formulated as a multi-objective mixed integer nonlinear optimization problem, minimizes fuel cost and minimizes emission of gases, as well as improvement of voltage profile in the system. NSGA-II based OPF-case 1-Two objective-Min Fuel cost and Voltage stability index; case 2-Three objective-Min Fuel cost, Min Emission cost and Voltage stability index. The above method is tested on standard IEEE 30-bus test system and simulation results are done for base case and the two severe contingency cases and also on loaded conditions.

Optimal Bespoke CDO Design via NSGA-II  [PDF]
Diresh Jewan,Renkuan Guo,Gareth Witten
Advances in Decision Sciences , 2009, DOI: 10.1155/2009/925169
Abstract: This research work investigates the theoretical foundations and computational aspects of constructing optimal bespoke CDO structures. Due to the evolutionary nature of the CDO design process, stochastic search methods that mimic the metaphor of natural biological evolution are applied. For efficient searching the optimal solution, the nondominating sort genetic algorithm (NSGA-II) is used, which places emphasis on moving towards the true Paretooptimal region. This is an essential part of real-world credit structuring problems. The algorithm further demonstrates attractive constraint handling features among others, which is suitable for successfully solving the constrained portfolio optimisation problem. Numerical analysis is conducted on a bespoke CDO collateral portfolio constructed from constituents of the iTraxx Europe IG S5 CDS index. For comparative purposes, the default dependence structure is modelled via Gaussian and Clayton copula assumptions. This research concludes that CDO tranche returns at all levels of risk under the Clayton copula assumption performed better than the sub-optimal Gaussian assumption. It is evident that our research has provided meaningful guidance to CDO traders, for seeking significant improvement of returns over standardised CDOs tranches of similar rating.
Scientia Et Technica , 2008,
Abstract: Este trabajo presenta la filosofía del algoritmo multiobjetivo elitista NSGA-II, explicando su esquema de funcionamiento y los mecanismos especiales que permiten la preservación y la evolución de soluciones Paretoóptimas. Este algoritmo se aplica sobre el problema de optimización clásico correspondiente al problema de la mochila adaptado para optimización multiobjetivo. También se desarrolla una propuesta alternativa para ser comparada con el esquema básico del NSGA-II.
PUMA 560 Trajectory Control Using NSGA-II Technique With Real Valued Operators  [PDF]
Habiba Benzater,Samira Chouraqui
Mathematics , 2014, DOI: 10.14810/ijscmc.2014.3302
Abstract: In the industry, Multi-objectives problems are a big defy and they are also hard to be conquered by conventional methods. For this reason, heuristic algorithms become an executable choice when facing this kind of problems.The main objective of this work is to investigate the use of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) technique using the real valued recombination and the real valued mutation in the tuning of the computed torque controller gains of a PUMA560 arm manipulator. The NSGA-II algorithm with real valued operators searches for the controller gains so that the six Integral of the Absolute Errors (IAE) in joint space are minimized. The implemented model under MATLAB allows an optimization of the Proportional-Derivative computed torque controller parameters while the cost functions and time are simultaneously minimized.. Moreover, experimental results also show that the real valued recombination and the real valued mutation operators can improve the performance of NSGA-II effectively.
Improved NSGA-II Based on a Novel Ranking Scheme  [PDF]
Rio G. L. D'Souza,K. Chandra Sekaran,A. Kandasamy
Computer Science , 2010,
Abstract: Non-dominated Sorting Genetic Algorithm (NSGA) has established itself as a benchmark algorithm for Multiobjective Optimization. The determination of pareto-optimal solutions is the key to its success. However the basic algorithm suffers from a high order of complexity, which renders it less useful for practical applications. Among the variants of NSGA, several attempts have been made to reduce the complexity. Though successful in reducing the runtime complexity, there is scope for further improvements, especially considering that the populations involved are frequently of large size. We propose a variant which reduces the run-time complexity using the simple principle of space-time trade-off. The improved algorithm is applied to the problem of classifying types of leukemia based on microarray data. Results of comparative tests are presented showing that the improved algorithm performs well on large populations.
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