Methods of Cognitive Modeling and Hybrid Evolutionary Multi-Criteria Algorithms in a Multi-Agent Information-Analytical System

The paper proposes an approach to multi-criteria decision support based on a cognitively oriented multi-agent information-analytical system. Cognitive modeling methods are developed, including a formal ontological representation of knowledge about production planning and a coalition–holonic agent architecture that ensures adaptability and transparency of computations. A hybrid evolutionary multi-criteria algorithm is introduced, in which agents generate alternative plans at the local level using a parallel genetic algorithm that optimizes a combination of several criteria. At the global level, a multi-stage selection of alternatives is implemented with filtering of resource overloads and similar solutions, followed by final aggregation using the PROMETHEE and ELECTRE multi-criteria decision-making methods. An experimental study is carried out comparing manual planning with planning supported by the developed system, as well as analyzing the impact of dynamic adaptation of the genetic algorithm parameters. The results show that the use of the system makes it possible to reduce plan generation time by a factor of 20–30 while maintaining or improving solution quality. At the same time, resource overloads are completely eliminated, and early termination of evolutionary computations is ensured without loss of solution quality. The system and proposed algorithms are intended for use in planning project activities at manufacturing enterprises.