NATURAL SOLVERS IN PROBLEMS OF SEARCHING FOR THE BEST SOLUTION

Abstract

In the paper we present a new method, which can be used as a natural solver for searching the best solution in the multidimensional and multimodal parameter space. The method is based on

a well-known simulation techniąue, i.e., molecular dynamics. To show advantages and disadvanta- ges of the particie method in comparison to the standard genetic algorithm, we analyse efficiency of the methods in finding the global minimum of multi-dimensional and multi-modal test-bed functions and we calculate the evaluation indices. We analyse also the ways the solution space is explored and the parameters of algorithms adjusted. The optimal heuristics are proposed. The tests carried out show that the choice of the most appriopriate optimization method depends on type of a problem considered. We show that the particie method is morę efficient for finding the optimal solution for multi-modal problems with distinct global extreme, while the genetic algo­ rithm is better for deceptive functions with several locals extreme, which are placed far away from the global optimum. This comes from the different ways in which the particie method and genetic algorithm explore the solution space. The particie method can be used for initial analysis of functions, which character is unknown.