Benchmark analysis of novel multi-agent optimization algorithm using linear regulators for agents motion control. Issue 1 (September 2020) - Karane, M; Panteleev, A
Abstract: The aim of this paper is to develop algorithm and software for a novel multi-agent constrained global optimization method. In multi-agent algorithms, the solution search area is populated with agents. Some transformations occur over them, which lead to a global extremum. It was proposed to take as a basis the use of linear regulators to control the movement of agents group. At each stage of the algorithm, a search is made by closed loop agents control with various types of criteria. Using criteria of various types, a more detailed study of the solution search area and finding a global extremum is provided. This is the main feature and novelty of the algorithm under consideration. The method efficiency is studied on a standard set of test functions of two variables with a complex structure of level lines using the software developed. Efficiency analysis is carried out for Schweffel function and multifunction from the benchmark set. During the study, statistical characteristics were calculated, according to which the most suitable parameters were selected. The results obtained indicate that the algorithm successfully copes with such problems and can be used, for example, for problems in the theory of optimal control of dynamic systems.