A Hybrid Multi-objective Immune Algorithm for Numerical Optimization
Chris S. K. Leung, Henry Y. K. Lau
2016
Abstract
With the complexity of real world problems, optimization of these problems often has multiple objectives to be considered simultaneously. Solving this kind of problems is very difficult because there is no unique solution, but rather a set of trade-off solutions. Moreover, evaluating all possible solutions requires tremendous computer resources that normally are not available. Therefore, an efficient optimization algorithm is developed in this paper to guide the search process to the promising areas of the solution space for obtaining the optimal solutions in reasonable time, which can aid the decision makers in arriving at an optimal solution/decision efficiently. In this paper, a hybrid multi-objective immune optimization algorithm based on the concepts of the biological evolution and the biological immune system including clonal selection and expansion, affinity maturation, metadynamics, immune suppression and crossover is developed. Numerical experiments are conducted to assess the performance of the proposed hybrid algorithm using several benchmark problems. Its performance is measured and compared with other well-known multi-objective optimization algorithms. The results show that for most cases the proposed hybrid algorithm outperforms the other benchmarking algorithms especially in terms of solution diversity.
References
- Ataser, Z. A Review of Artificial Immune Systems. Ijcci (Ecta), 2013 Algarve, Portugal. 128-135.
- Burnet, F. M. 1959. The Clonal Selection Theory of Acquired Immunity, Nashville, Vanderbilt University.
- Coelho, G. & Von Zuben, F. 2006. Omni-Ainet: An Immune-Inspired Approach for Omni Optimization.
- Coello Coello, C., Lamont, G. B. & Veldhuizen, D. A. V. 2007. Evolutionary Algorithms For Solving MultiObjective Problems, New York, Springer.
- Coello Coello, C. A. & Cortés, N. C. 2005. Solving Multiobjective Optimization Problems Using an Artificial Immune System. Genetic Programming and Evolvable Machines, 6, 163-190.
- Corne, D. W., Jerram, N. R., Knowles, J. & Oates, M. J. Pesa-Ii: Regionbased Selection in Evolutionary Multiobjective Optimization. The Genetic and Evolutionary Computation Conference (Gecco 2001), 2001 San Francisco, California. 283-290.
- Cutello, V., Narzisi, G. & Nicosia, G. 2006. A MultiObjective Evolutionary Approach to the Protein Structure Prediction Problem. Journal of the Royal Society Interface, 3, 139-151.
- D. A. Van Veldhuizen & G. B. Lamont 1998. Multiobjective Evolutionary Algorithm Research: A History and Analysis. Technical Report Tr-98-03. Department Of Electrical And Computer Engineering, Graduate School Of Engineering, Air Force Institute Of Technology, Wright-Patterson Afb.
- Deb, K. 2001. Multi-Objective Optimization Using Evolutionary Algorithms, Chichester, Uk, John Wiley & Sons, Inc. .
- Deb, K., Agrawal, S., Pratap, A. & Meyarivan, T. A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimisation: Nsga-Ii. Proceedings of The 6th International Conference On Parallel Problem Solving From Nature, 2000. 668937: Springer-Verlag, 849-858.
- Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. 2002. A Fast And Elitist Multiobjective Genetic Algorithm : Nsga-Ii. Ieee Transactions On Evolutionary Computation, 6, 182-197.
- Fonseca, C. M. & Fleming, P. J. 1995. An Overview of Evolutionary Algorithms in Multiobjective Optimization. Evolutionary Computation, 3, 1-16.
- Goldsby, R. A., Tj, K., Ba, O. & J, K. 2003. Immunology, San Francisco, W. H. Freeman And Company.
- Gong, M., Jiao, L., Du, H. & Bo, L. 2008. Multiobjective Immune Algorithm with Nondominated NeighborBased Selection. Evolutionary Computation, 16, 225- 255.
- Jerne, N. K. 1974. Towards A Network Theory of the Immune System. Ann Immunol (Paris), 125(C), 373- 389.
- Knowles, J. & Corne, D. The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimisation. 1999 Congress on Evolutionary Computation (Cec 1999), 1999 1999. 105.
- Luh, G.-C., Chueh, C.-H. & Liu, W.-W. 2003. Moia: Multi-Objective Immune Algorithm. Engineering Optimization, 35, 143-164.
- Pulido, G. T. & Coello Coello, C. A. 2003. The Micro Genetic Algorithm 2: Towards Online Adaptation in Evolutionary Multiobjective Optimization. In: Fonseca, C., Fleming, P., Zitzler, E., Thiele, L. & Deb, K. (Eds.) Evolutionary Multi-Criterion Optimization. Springer Berlin / Heidelberg.
- Qiu, X. & Lau, H. Y. K. 2014. An Ais-Based Hybrid Algorithm for Static Job Shop Scheduling Problem. Journal of Intelligent Manufacturing, 25, 489-503.
- Schaffer, J. D. 1984. Some Experiments In Machine Learning Using Vector Evaluated Genetic Algorithms (Artificial Intelligence, Optimization, Adaptation, Pattern Recognition). Vanderbilt University.
- Schaffer, J. D. Multiple Objective Optimization With Vector Evaluated Genetic Algorithms. 1st International Conference on Genetic Algorithms, 1985. 657079: L. Erlbaum Associates Inc., 93-100.
- Schott, J. 1995. Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. Massachusetts Institute of Technology.
- Van Veldhuizen, D. A. 1999. Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. Air Force Institute of Technology.
- Wong, E. Y. C., Yeung, H. S. C. & Lau, H. Y. K. 2009. Immunity-Based Hybrid Evolutionary Algorithm for Multi-Objective Optimization in Global Container Repositioning. Engineering Applications of Artificial Intelligence, 22, 842-854.
- Zhang, Z. 2011. Artificial Immune Optimization System Solving Constrained Omni-Optimization. Evolutionary Intelligence, 4, 203-218.
- Zitzler, E., Deb, K. & Thiele, L. 2000. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8, 173-195.
- Zitzler, E., Laumanns, M. & Thiele, L. 2001. Spea2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103. Zurich: Computer Engineering and Communication Networks Lab (Tik), Swiss Federal Institute Of Technology (Eth).
Paper Citation
in Harvard Style
Leung C. and Lau H. (2016). A Hybrid Multi-objective Immune Algorithm for Numerical Optimization . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 105-114. DOI: 10.5220/0006014201050114
in Bibtex Style
@conference{ecta16,
author={Chris S. K. Leung and Henry Y. K. Lau},
title={A Hybrid Multi-objective Immune Algorithm for Numerical Optimization},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)},
year={2016},
pages={105-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006014201050114},
isbn={978-989-758-201-1},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)
TI - A Hybrid Multi-objective Immune Algorithm for Numerical Optimization
SN - 978-989-758-201-1
AU - Leung C.
AU - Lau H.
PY - 2016
SP - 105
EP - 114
DO - 10.5220/0006014201050114