Authors:
Chris S. K. Leung
and
Henry Y. K. Lau
Affiliation:
The University of Hong Kong, China
Keyword(s):
Artificial Immune Systems, Artificial Intelligence, Multi-objective Optimization, Hybrid Algorithm, Genetic Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Evolutionary Multiobjective Optimization
;
Hybrid Systems
;
Soft Computing
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 t
he 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.
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