A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II

@inproceedings{Deb2000AFE,
  title={A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II},
  author={Kalyanmoy Deb and Samir Agrawal and Amrit Pratap and T. Meyarivan},
  booktitle={Parallel Problem Solving from Nature},
  year={2000}
}
Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN3) 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 parameter. [] Key Method Specifically, a fast non-dominated sorting approach with O(MN2) computational complexity is presented.

A Multi-objective Genetic Algorithm Based on Quick Sort

It is proved that the individuals of an evolutionary population can be sorted by quick sort, and the time complexity of the construction is O( nlog n), compared to the previous best result of O(n 2) described in the popular NSGA-II.

Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence

By applying an elitist multi-objective EA (NSGA-II) to a number of difficult test problems, it is shown that the NS GA-II with controlled elitism has much better convergence property than the original NSGA- II.

SETNDS: A SET-Based Non-Dominated Sorting Algorithm for Multi-Objective Optimization Problems

This paper proposes a more efficient SET-based non-dominated sorting algorithm, shorted to SETNDS, and shows that the proposed algorithm is feasible and effective and its computational efficiency outperforms other existing algorithms.

Substitute Distance Assignments in NSGA-II for Handling Many-objective Optimization Problems

A study on the performance of the Fast Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) for handling many-objective optimization problems is presented, and substitute distance assignment schemes are proposed that can replace the crowding distance assignment, which is normally used in NSGA- II.

A fast and elitist multi-objective particle swarm algorithm: NSPSO

  • Yang Liu
  • Business
    2008 IEEE International Conference on Granular Computing
  • 2008
A new nondominated sorting particle swarm optimisation (NSPSO) framework is proposed, that combines the operations of a known MOGA NSGA-II and the other advanced operations with a single particle Swarm optimiser (PSO).

A novel diversification strategy for multi-objective evolutionary algorithms

A new archiving strategy based on the Convex Hull of Individual Minima (CHIM), which is intended to maintain a well-distributed set of non-dominated solutions is introduced.

A New Method to Construct the Non-Dominated Set in Multi-Objective Genetic Algorithms

A new method called Dealer's Principle to construct non-dominated sets of MOGA and a clustering algorithm based on the core distance of clusters to keep the diversity of solutions is proposed.

An Improved Elitist Strategy Multi-Objective Evolutionary Algorithm

Simulation results on four difficult test problems show that the improved elitist strategy multi-objective evolutionary algorithm is able to find much better spread of solutions and better convergence near the true Pareto-optimal front than NSGA II.

moPGA: Towards a New Generation of Multi-objective Genetic Algorithms

  • Harold SohM. Kirley
  • Computer Science
    2006 IEEE International Conference on Evolutionary Computation
  • 2006
Comparisons with well-known multi-objective GAs on scalable benchmark problems indicate that the algorithm scales well with problem size in terms of number of function evaluations and quality of solutions found.
...

References

SHOWING 1-10 OF 18 REFERENCES

The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation

  • Joshua D. KnowlesD. Corne
  • Computer Science
    Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
  • 1999
It is argued that PAES may represent the simplest possible non-trivial algorithm capable of generating diverse solutions in the Pareto optimal set, and is intended as a good baseline approach against which more involved methods may be compared.

Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms

Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously are investigated and suggested to be extended to higher dimensional and more difficult multiobjective problems.

Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization

A rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs) and the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM.

A niched Pareto genetic algorithm for multiobjective optimization

The Niched Pareto GA is introduced as an algorithm for finding the Pare to optimal set and its ability to find and maintain a diverse "Pareto optimal population" on two artificial problems and an open problem in hydrosystems is demonstrated.

Comparison of Multiobjective Evolutionary Algorithms: Empirical Results

This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.

Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example

This study illustrates how a technique such as the multiobjective genetic algorithm can be applied and exemplifies how design requirements can be refined as the algorithm runs, and demonstrates the need for preference articulation in cases where many and highly competing objectives lead to a nondominated set too large for a finite population to sample effectively.

Evolutionary algorithms for multiobjective optimization: methods and applications

The basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective and the focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms.

Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems

  • K. Deb
  • Computer Science
    Evolutionary Computation
  • 1999
The problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front are studied to enable researchers to test their algorithms for specific aspects of multi- objective optimization.

Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation

The paper proposes that fitness assignment be interpreted as, or at least related to, a multicriterion decision process, and a suitable decision making framework based on goals and priorities is formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies.

Multiobjective evolutionary algorithms: classifications, analyses, and new innovations

This research organizes, presents, and analyzes contemporary MultiObjective Evolutionary Algorithm research and associated Multiobjective Optimization Problems (MOPs) and uses a consistent MOEA terminology and notation to present a complete, contemporary view of current MOEA "state of the art" and possible future research.