Multi-Pareto-Ranking evolutionary algorithm

Authors:
Wahabou Abdou;Christelle Bloch;Damien Charlet;François Spies
Affiliations:
FEMTO-ST Institute, University of Franche-Comté, Montbéliard, France;FEMTO-ST Institute, University of Franche-Comté, Montbéliard, France;FEMTO-ST Institute, University of Franche-Comté, Montbéliard, France;FEMTO-ST Institute, University of Franche-Comté, Montbéliard, France
Venue:
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Year:
2012

Citing 15
Cited 1

Genetic algorithms: A Survey

Computer
Efficient and Accurate Parallel Genetic Algorithms

Efficient and Accurate Parallel Genetic Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
A Multi-objective Approach to Constrained Optimisation of Gas Supply Networks: the COMOGA Method

Selected Papers from AISB Workshop on Evolutionary Computing
Evolutionary algorithms for constrained parameter optimization problems

Evolutionary Computation
Tuning an evolutionary algorithm with taguchi methods and application to the dimensioning of an electrical motor

CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
The multiobjective evolutionary algorithm based on determined weight and sub-regional search

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multiple trajectory search for unconstrained/constrained multi-objective optimization

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Performance assessment of DMOEA-DD with CEC 2009 MOEA competition test instances

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
FastPGA: a dynamic population sizing approach for solving expensive multiobjective optimization problems

EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Experimental Methods for the Analysis of Optimization Algorithms

Experimental Methods for the Analysis of Optimization Algorithms
jMetal: A Java framework for multi-objective optimization

Advances in Engineering Software
Using an evolutionary algorithm to optimize the broadcasting methods in mobile ad hoc networks

Journal of Network and Computer Applications
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
A New Evolutionary Algorithm for Solving Many-Objective Optimization Problems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Adaptive multi-objective genetic algorithm using multi-pareto-ranking

Proceedings of the 14th annual conference on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a new multi-objective genetic algorithm, called GAME, to solve constrained optimization problems. GAME uses an elitist archive, but it ranks the population in several Pareto fronts. Then, three types of fitness assignment methods are defined: the fitness of individuals depends on the front they belong to. The crowding distance is also used to preserve diversity. Selection is based on two steps: a Pareto front is first selected, before choosing an individual among the solutions it contains. The probability to choose a given front is computed using three parameters which are tuned using the design of experiments. The influence of the number of Pareto fronts is studied experimentally. Finally GAME's performance is assessed and compared with three other algorithms according to the conditions of the CEC 2009 competition.