Methods of Cognitive Modeling and Hybrid Evolutionary Multicriteria Algorithms in a Multiagent Information-Analytical System

This paper proposes an approach to multicriteria decision support that is based on a cognitively oriented multiagent information-analytical system. Cognitive modeling methods are developed, including a formal ontological representation of knowledge concerning production planning and a coalition–holonic agent architecture that ensures adaptability and transparency of computations. A hybrid evolutionary multicriteria algorithm is introduced, where agents generate alternative plans at the local level using a parallel genetic algorithm that optimizes a combination of several criteria. At a global level, a multistage selection of alternatives is implemented with filtering of resource overloads and similar solutions, followed by final aggregation using the PROMETHEE and ELECTRE multicriteria decision-making methods. An experimental study is carried out that compares manual planning with planning supported by the developed system, as well as analyzing the impact of the 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 the 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.