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597,156
A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II
, 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
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Cited by 1707 (58 self)
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to solve constrained multi-objective problems eciently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint non-linear problem, are compared with another constrained multi-objective optimizer and much better performance of NSGA
Constrained Test Problems for Multi-Objective Evolutionary Optimization
- FIRST INTERNATIONAL CONFERENCE ON EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
, 2000
"... Over the past few years, researchers have developed a number of multi-objective evolutionary algorithms (MOEAs). Although most studies concentrated on solving unconstrained optimization problems, there exists a few studies where MOEAs have been extended to solve constrained optimization problem ..."
Abstract
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Cited by 29 (3 self)
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Over the past few years, researchers have developed a number of multi-objective evolutionary algorithms (MOEAs). Although most studies concentrated on solving unconstrained optimization problems, there exists a few studies where MOEAs have been extended to solve constrained optimization
A Multi-Objective Approach To Constrained . . .
- EVOLUTIONARY COMPUTING: AISB WORKSHOP", ED: T.C. FOGARTY, SPRINGER-VERLAG LNCS 993, PP166-180, 1995
, 1995
"... This paper presents a new technique for handling constraints within evolutionary algorithms, and demonstrates its effectiveness on a real-world, constrained optimisation problem that arises in the design of gas-supply networks. The ..."
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This paper presents a new technique for handling constraints within evolutionary algorithms, and demonstrates its effectiveness on a real-world, constrained optimisation problem that arises in the design of gas-supply networks. The
How Evolutionary Multi-objective
"... Multi-objective evolutionary algorithms for optimization have received much attention in recent literature. In this paper we propose a new Pareto-based Multi-objective Evolutionary Algorithm to solve the vector optimization problem. This algorithm uses a variant of the preselection scheme as implici ..."
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Multi-objective evolutionary algorithms for optimization have received much attention in recent literature. In this paper we propose a new Pareto-based Multi-objective Evolutionary Algorithm to solve the vector optimization problem. This algorithm uses a variant of the preselection scheme
with Multi-Objective Evolutionary Algorithms
"... Optimising the flow of experiments to a Robot Scientist ..."
Scalable Test Problems for Evolutionary Multi-Objective Optimization
- Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH
, 2001
"... After adequately demonstrating the ability to solve di#erent two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must now show their e#cacy in handling problems having more than two objectives. In this paper, we have suggested three di#erent approaches for systema ..."
Abstract
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Cited by 150 (22 self)
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After adequately demonstrating the ability to solve di#erent two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must now show their e#cacy in handling problems having more than two objectives. In this paper, we have suggested three di#erent approaches
A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II
, 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
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Cited by 634 (15 self)
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to find much better spread of solutions in all problems compared to PAES---another elitist multi-objective EA which pays special attention towards creating a diverse Pareto-optimal front. Because of NSGA-II's low computational requirements, elitist approach, and parameter-less sharing approach
Constrained/Bound Constrained Multi-Objective Optimization
"... Abstract—Many real-world optimization problems involve multiple conflicting objectives. Therefore, multi-objective optimization has attracted much attention of researchers and many algorithms have been developed for solving multi-objective optimization problems in the last decade. In this paper the ..."
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the multiple trajectory search (MTS) is presented and successfully applied to thirteen unconstrained and ten constrained multi-objective optimization problems. These problems constitute a test suite provided for competition in the Special
DNA Sequence Optimization Using Constrained Multi-Objective Evolutionary Algorithm
"... Abstract- Generating a set of the good DNA sequences needs to optimize multiple objectives and to satisfy several constraints. Therefore, it can be regarded as an instance of constrained multi-objective optimization problem. We apply the controlled elitist non-dominating sorting genetic algorithm wi ..."
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Abstract- Generating a set of the good DNA sequences needs to optimize multiple objectives and to satisfy several constraints. Therefore, it can be regarded as an instance of constrained multi-objective optimization problem. We apply the controlled elitist non-dominating sorting genetic algorithm
3 Evolutionary Multi-Objective Algorithms
"... The versatility that genetic algorithm (GA) has proved to have for solving different problems, has make it the first choice of researchers to deal with new challenges. Currently, GAs are the most well known evolutionary algorithms, because their intuitive principle of operation and their relatively ..."
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The versatility that genetic algorithm (GA) has proved to have for solving different problems, has make it the first choice of researchers to deal with new challenges. Currently, GAs are the most well known evolutionary algorithms, because their intuitive principle of operation and their relatively
Results 1 - 10
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597,156