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Abstract:
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Hard optimization problems that cannot be solved within acceptable computational
time by deterministic mathematical methods have been successfully solved in recent
years by population-based stochastic metaheuristics, among which swarm intelligence
algorithms represent a prominent class. This thesis investigates improvements
of the swarm intelligence metaheuristics by hybridization. During analysis of the
existing swarm intelligence metaheuristics in some cases de ciencies and weaknesses
in the solution space search mechanisms were observed, primarily as a consequence
of the mathematical model that simulates natural process as well as inappropriate
balance between intensi cation and diversi cation.
The thesis examines whether existing swarm intelligence algorithms for global optimization
could be improved (in the sense of obtaining better results, faster convergence,
better robustness) by hybridization with other algorithms. A number
of hybridized swarm intelligence metaheuristics were developed and implemented.
Considering the fact that good hybrids are not created as a random combination of
individual functional elements and procedures from di erent algorithms, but rather
established on comprehensive analysis of the functional principles of the algorithms
that are used in the process of hybridization, development of the hybrid approaches
was preceded by thorough research of advantages and disadvantages of each involved
algorithm in order to determine the best combination that neutralizes disadvantages
of one approach by incorporating the strengths of the other.
Developed hybrid approaches were veri ed by testing on standard benchmark sets for
global optimization, with and without constraints, as well as on well-known practical
problems. Comparative analysis with the state-of-the-art algorithms from the literature
demonstrated quality of the developed hybrids and con rmed the hypothesis
that swarm intelligence algorithms can be successfully improved by hybridization. |