A parallel genetic algorithm in multi-objective optimization

Authors:
Zhi-Xin Wang;Gang Ju
Affiliations:
School of Energy and Environment, Southeast University, Nanjing, China;School of Energy and Environment, Southeast University, Nanjing, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 7
Cited 1

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

PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A Variant of Evolution Strategies for Vector Optimization

PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations

Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems: Research Articles

International Journal of Intelligent Systems
Multi-objective genetic algorithms: Problem difficulties and construction of test problems

Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

A PTS-PGATS based approach for data-intensive scheduling in data grids

Frontiers of Computer Science in China

Quantified Score

Hi-index	0.00

Visualization

Abstract

Based on the combination of NSGA-II algorithm and parallel genetic algorithm, this paper presents a parallel genetic algorithm for multi-objective optimization (PNSGA). At the evolving process of this new algorithm, an individual migration to improve the parallel searching speed is applied to improve the efficiency of this algorithm and the accuracy of Pareto optimal set; at the same time, an individual update strategy is introduced to keep the diversity of Pareto optimal set. Data show that the Pareto optimal solutions or the solution candidates output by PNSGA that are scattered extensively and uniformly.