Results 1 - 10 of 439

A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II

by Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, T Meyarivan , 2000
"... Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) -4 computational complexity (where is the number of objectives and is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing ..."
Abstract - Cited by 662 (15 self) - Add to MetaCart
sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algorithm (we called it the Non-dominated Sorting GA-II or NSGA-II) which alleviates all the above three difficulties. Specifically, a fast non-dominated sorting approach with computational

A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II

by Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, T. Meyarivan , 2000
"... Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing param ..."
Abstract - Cited by 1815 (60 self) - Add to MetaCart
parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algorithm (we called it the Non-dominated Sorting GA-II or NSGA-II) which alleviates all the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN ) computational complexity

Mechanical component design for multiple objectives using elitist non-dominated sorting GA

by Kalyanmoy Deb, Amrit Pratap, Subrajyoti Moitra - Proceedings of the Parallel Problem Solving from Nature VI Conference , 2000
"... Abstract. In this paper, we apply an elitist multi-objective genetic algorithm for solving mechanical component design problems with multiple objectives. Although there exists a number of classical techniques, evolutionary algorithms (EAs) have an edge over the classical methods in that they can fin ..."
Abstract - Cited by 11 (1 self) - Add to MetaCart
find multiple Pareto-optimal solutions in one single simulation run. The proposed algorithm (we call NSGA-II) is a much improved version of the originally proposed nondominated sorting GA (NSGA) in that it is computationally faster, uses an elitist strategy, and it does not require fixing any niching

Fast Implementation of the Steady-State NSGA-II Algorithm for Two Dimensions Based on Incremental Non-Dominated Sorting

by Maxim Buzdalov , Ilya Yakupov , Andrey Stankevich
"... ABSTRACT Genetic algorithms (GAs) are widely used in multi-objective optimization for solving complex problems. There are two distinct approaches for GA design: generational and steadystate algorithms. Most of the current state-of-the-art GAs are generational, although there is an increasing intere ..."
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interest to steady-state algorithms as well. However, for algorithms based on non-dominated sorting, most of steady-state implementations have higher computation complexity than their generational counterparts, which limits their applicability. We present a fast implementation of a steady-state version

Approximating the nondominated front using the Pareto Archived Evolution Strategy

by Joshua D. Knowles, David W. Corne - EVOLUTIONARY COMPUTATION , 2000
"... We introduce a simple evolution scheme for multiobjective optimization problems, called the Pareto Archived Evolution Strategy (PAES). We argue that PAES may represent the simplest possible nontrivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm, in its ..."
Abstract - Cited by 321 (19 self) - Add to MetaCart
of the Niched Pareto Genetic Algorithm and the Nondominated Sorting Genetic Algorithm over a diverse suite of six test functions. Results are analyzed and presented using techniques that reduce the attainment surfaces generated from several optimization runs into a set of univariate distributions. This allows

Optimization of Location Allocation of Web Services Using A Modified Non-dominated Sorting Genetic Algorithm

by Boxiong Tan, Hui Ma, Mengjie Zhang
"... Abstract. In recent years, web services technology is becoming increas-ingly popular because of the convenience, low cost and capacity to be composed into high-level business processes. The service location-allocation problem for a web service provider is critical and urgent, because some factors su ..."
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objective genetic algorithm (GA). It shows NSGA-II based algorithm can provide a set of best solutions that outperforms genetic algorithm. 1

Automatic Generation Control of Multi-Area Power System Using Multi-Objective Non-Dominated Sorting Genetic Algorithm-II.

by Sidhartha Panda , Narendra Kumar , Yegireddy - International Journal of Electrical Power & Energy Systems, , 2013
"... a b s t r a c t Controllers design problems are multi objective optimization problems as the controller must satisfy several performance measures that are often conflicting and competing with each other. In multi-objective approach a set of solutions can be generated from which the designer can sel ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
select a final solution according to his requirement and need. This paper presents the design and analysis Proportional Integral (PI) and Proportional Integral Derivative (PID) controller employing multi-objective Non-Dominated Shorting Genetic Algorithm-II (NSGA-II) technique for Automatic Generation

An Efficient Non-dominated Sorting Method for Evolutionary Algorithms

by Qian Wang, Yi-cheng Tu, Mark F. Horstemeyer
"... We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN 2) in generating non-dominated fronts in one ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN 2) in generating non-dominated fronts

A PDE-based approach to non-dominated sorting∗

by Jeff Calder, Alfred O. Hero , 2013
"... Non-dominated sorting is a fundamental combinatorial problem in multiobjective op-timization, and is equivalent to the longest chain problem in combinatorics and random growth models for crystals in materials science. In a previous work [4], we showed that non-dominated sorting has a continuum limit ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Non-dominated sorting is a fundamental combinatorial problem in multiobjective op-timization, and is equivalent to the longest chain problem in combinatorics and random growth models for crystals in materials science. In a previous work [4], we showed that non-dominated sorting has a continuum

A continuum limit for non-dominated sorting

by Jeff Calder, Alfred O. Hero Iii
"... Abstract — Non-dominated sorting is an important combinato-rial problem in multi-objective optimization, which is ubiquitous in many fields of science and engineering. In this paper, we overview the results of some recent work by the authors on a continuum limit for non-dominated sorting. In particu ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Abstract — Non-dominated sorting is an important combinato-rial problem in multi-objective optimization, which is ubiquitous in many fields of science and engineering. In this paper, we overview the results of some recent work by the authors on a continuum limit for non-dominated sorting
Results 1 - 10 of 439