Improved MOPSO based on ε-domination

Authors:
Yanmin Liu;Ben Niu;Changling Sui;Minhui Liu
Affiliations:
Department of Math, Zunyi Normal College, Zunyi, China;College of Management, Shenzhen University, Shenzhen, China,Hefei Institute of Intelligent Machines, CAS, Hefei, China;Biology Department, Zunyi Normal College, Zunyi, China;College of Management, Shenzhen University, Shenzhen, China
Venue:
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Year:
2012

Citing 7
Cited 0

Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems: Research Articles

International Journal of Intelligent Systems
Multiobjective optimization using dynamic neighborhood particle swarm optimization

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
MOPSO: a proposal for multiple objective particle swarm optimization

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A clustering multi-objective evolutionary algorithm based on orthogonal and uniform design

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A non-dominated sorting particle swarm optimizer for multiobjective optimization

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Symbiotic multi-swarm PSO for portfolio optimization

ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Multi-objective optimization using BFO algorithm

ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Designing efficient algorithms for multi-objective optimization problems (MOPs) is a very challenging problem. In this paper, based on the previously proposed εDMOPSO, an improved multi-objective PSO with orthogonal design and crossover is proposed. Firstly, the orthogonal design is used to generate the initial swarm, which makes the algorithm evenly scan the feasible solution space to find good points (solution) for the further exploration in subsequent iterations. Secondly, to explore the search space efficiently and get the good solutions in objective space, a new crossover operator is designed. Finally, Simulation experiments on the disabled benchmark problems of εDMOPSO show the proposed strategies are efficient.