Abstrakt: | In solving complex optimization tasks evolutionary algorithms have a leading position.
Unusual look at the optimization algorithms presented in the thesis, led to the creation of
the new algorithm and work on its development to put its metaphors in a group of artificial
life. The resulting algorithms are still the effective optimization algorithms and the
proposed approach introduces new properties in their operation. The study presents a new
algorithm of observation - as the base algorithm and its metaphors placed in a group of
immune algorithm and particle swarm optimization algorithms. Research on the mechanics
of these algorithms demonstrated new properties, i.e.: behavior resembling observation,
and co-evolution mechanism determines the behavior of independence on influences of the
environment. Implementation of the assumptions imposed the need to develop effective
mechanism of mutation for immune algorithm. The functions of behavior scenarios were
defined for the particle swarm optimization algorithm. A group of immune systems is
proposed which is an equivalent to the multi-population system and methods of information
exchange between systems in the group are defined. The thesis presents a theoretical
background of algorithms’ operation and a simulation study. To check the efficiency of the
algorithms the typical test environment for stationary and non-stationary problems were
applied. In the study, fractal and multifractal analysis was used and its usefulness was
demonstrated in research on behavior of algorithms. Optimization of diagnostic structure
of digital circuit is an issue of multimodal optimization and is a particular kind of challenge.
A comprehensive approach to test multi-module circuit may lead to new solutions,
also in terms of a single module testing. Such concepts are included in this study, basing
on an untypical approach to testing multi-module circuit, the conclusion has a strong
theoretical base. The original achievements in this dissertation are as follows: a proposal
of BIST architecture based on the so-called linear modification, the introduction of the
diagnostic structure description, and determination of the theoretical basis of this concept,
confirmation of the formulated theoretical basement and simultaneously the verification
of the diagnostic efficiency of the proposed solutions by means of simulation methods
basing on modeling with using ISCAS’89 benchmark, the demonstration of permanent
features of modules during testing, the presentation of a formal description of any diagnostic
structure with a description of the optimization framework and the concept of
simulation tools used in the current research. Simultaneously, the study shows the original
use of a genetic algorithm to give a high efficiency optimization. This part of the study
presents a complete system of description of any diagnostic structure with the optimization
method. The solutions presented in the dissertation open the way for the further research.
This dissertation is composed of two parts, despite of the common basis in a form of
evolutionary algorithms, they are present different and closed thematically issues.
Keywords: optimization, multi-criteria optimization, multimodal optimization, evolutionary
algorithms, genetic algorithms, immune algorithms, particle swarm optimization
algorithms, a group of immune system, the algorithm of observation, exchange of genetic
material, fractal analysis, multifractal analysis, beset game algorithm, immune algorithm
with auto-aggression, stationary problems, non-stationary problems, BIST structure, BIST
structure optimization, BIST structure description, multi-modular circuit BIST. |