Bio-inspired algorithms applied to dynamic optimization / Algoritmos bio-inspirados aplicados a otimização dinamica




This dissertation proposes bio-inspired algorithms to solve dynamic optimization problems, i.e., problems for which the optimization surface on the search space suffers several changes over time. With such variation of number, position and quality of local optima, mathematical programming techniques may present degradation of performance, because they were usually conceived to deal with static problems. Population-based algorithms, dynamic control of the population size, local search strategies and an efficient memory usage are desirable requirements to a proper treatment of dynamic optimization problems, thus being incorporated into the solution strategies implemented here. The algorithms to be presented, and compared with competitive alternatives available in the literature, are based on functionalities and processing structures of immune systems and ant colonies. Due to the capability of incorporating all the requirements for an efficient search on dynamic environments, the immune-inspired approaches overcome the others in all the performance criteria adopted to evaluate the experimental results


programação não-linear combinatorial optimization pesquisa operacional artificial immune systems otimização matematica dynamic optimization otimização combinatoria nonlinear optimization

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