An improved immune genetic algorithm for multiobjective optimization

Authors:
Guixia He;Jiaquan Gao;Luoke Hu
Affiliations:
Zhijiang College, Zhejiang University of Technology, Hangzhou, China;Zhijiang College, Zhejiang University of Technology, Hangzhou, China;College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2010

Citing 8
Cited 0

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms

Proceedings of the 1st International Conference on Genetic Algorithms
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy

Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms

Evolutionary Computation
A Novel Weight-Based Immune Genetic Algorithm for Multiobjective Optimization Problems

ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach

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

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

The study presents a novel weight-based multiobjective immune genetic algorithm(WBMOIGA), which is an improvement of its first version In this proposed algorithm, there are distinct characteristics as follows First, a randomly weighted sum of multiple objectives is used as a fitness function, and a local search procedure is utilized to facilitate the exploitation of the search space Second, a new mate selection scheme, called tournament selection algorithm with similar individuals (TSASI), and a new environmental selection scheme, named truncation algorithm with similar individuals (TASI), are presented Third, we also suggest a new selection scheme to create the new population based on TASI Simulation results on three standard problems (ZDT3, VNT, and BNH) show WBMOIGA can find much better spread of solutions and better convergence near the true Pareto-optimal front compared to the elitist non-dominated sorting genetic algorithm (NSGA-II).