Development of the Library for Solving Scalar and Vector Optimization Problems with Stochastic Methods
Mikhail Gennadyevich Grif, Pavel A. Zhurkin · 2020
Different approaches to developing software for solving a wide range of optimization problems are discussed. Applying the particle swarm method and genetic algorithm is proposed. The rationale for stochastic methods is given. A brief description of methods for solving vector optimization problems is given the generalized criterion method and the compromise method being presented. The paper suggests approaches to increasing such universal features of software being developed as cross-platform features and compatibility with several programming languages. The results of the study of the implemented optimization methods effectiveness are presented, a comparative analysis is carried out, and conclusions are drawn on the applicability of the implemented methods.