Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
Application areas of AIS: The past, the present and the future
Applied Soft Computing
A Novel Artificial Immune System for Multiobjective Optimization Problems
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
WBMOAIS: A novel artificial immune system for multiobjective optimization
Computers and Operations Research
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With a comparison to the random selection approach used in the weight-based multiobjective immune algorithm (WBMOIA), this paper proposes a new selection approach based on the truncation algorithm with similar individuals (TASI). Then the effect of the proposed selection approach is examined on the performance of WBMOIA. On one hand, the performance is compared between WBMOIA with the random selection approach and WBMOIA with the proposed selection approach. On the other hand, simulation results on a number of problems are presented to investigate if there exists any value of the reduction rate where WBMOIA performs well. Experiment results show that the performance of WBMOIA can be improved by the proposed selection approach and a better reduction rate can be obtained for each test problem.