Parallelization of random search global optimization algorithms

Global optimization problems are relevant in various fields of research and industry, such as chemistry, biology, biomedicine, operational research, etc. Normally it is easier to solve optimization problems having some specific properties of objective function such as linearity, convexity, differentiability, etc. However, there are a lot of practical problems that do not satisfy such properties or even cannot be expressed in an adequate mathematical form. Therefore, it is popular to use random search optimization methods in solving such optimization problems. The dissertation deals with investigation of random search global optimization algorithms, their parallelization and application to solve practical problems. The work is focused on modification and parallelization of particle swarm optimization and genetic algorithms. The modification of particle swarm optimization algorithm, based on reduction of the search area is proposed, and several strategies to parallelize the algorithm are investigated. The algorithm is applied to solve Multiple Gravity Assist problem using parallel computing system. A hybrid global multi-objective optimization algorithm is developed by modifying single agent stochastic search strategy, and incorporating it into multi-objective optimization genetic algorithm. Several strategies to parallelize multi-objective optimization genetic algorithm is proposed. Parallel algorithms are experimentally investigated by solving competitive facility location problem using high performance computing systems