Multi-thread evolutionary computation for design optimization

The paper presents multi-thread calculations using parallel evolutionary algorithms (EA) for single and multicriteria design optimization. This approach was implemented to avoid a negative influence of incorrectly chosen initial and EA’s control parameters for the accuracy of generated solutions and thereby to improve the effectiveness of the EA’s use. Parallel computation for single optimization problems relies just on running n threads with different randomly chosen parameters in order to find the best final solution. For multicriteria optimization problems, each thread generates a set of Pareto optimal solutions and at the end these sets are combined together, giving a real set of Pareto optimal solutions. During the run of the algorithm, random interactions between threads were applied. The experiments were carried out using ten-thread processes for different examples of single and multicriteria design optimization problems, two of which are presented in the paper.