ABSTRACT

This dissertation focuses on developing a novel immune algorithm called for
finding Pareto-optimal solutions simultaneously maintaining diversity to
single- and multi-objective optimization problems (SOOPs and MOOPs) based
fully on the features of a biological immune system. The applications in
this dissertation include unconstrained/constrained test functions and 
truss-structure sizing multi-objective optimization, structural topology
single-objective with multi-modally optimization, and single-objective
job-shop scheduling optimization problems. The use of proposed immune
algorithm as opposed to the evolutionary algorithm (e.g., genetic algorithm,
GA, evolution strategy, ES) provides this methodology with superior diversification
and local search abilities. Inter-relationships within the proposed algorithm
resemble antibody-antigen relationships in terms of specificity and adaptiveness,
antibody clonal proliferation, antigen discrimination, and the antibody
memory characteristics of adaptive immune responses. Besides, the features
for producing antibodies in biological immune system such as gene fragment
rearrangement and several antibody diversification schemes (including somatic
recombination, somatic mutation, gene conversion, gene reversion, gene drift,
and nucleotide addition) are incorporated into the proposed immune algorithm
in order to improve the balance between exploitation and exploration. Moreover
the concept of cytokines is also combined to algorithm for constraint handling.
By using several performance metrics and comparison with the other approaches,
the effectiveness of proposed immune algorithm are evaluated by unconstrained/
constrained test functions and several engineering applications (truss sizing,
structural topology, and scheduling). The simulated results demonstrated that 
the proposed immune algorithm provides better effect than other methods and
suitable for searching in optimizations.