The Swarm Bacterial Algorithm Based on New Attractive Operators and Patterns of Agent Behavior

The work is devoted to solving the scientific problem of decision support in intelligent optimization and design systems. The increased complexity of the tasks solved within the framework of the designated scientific problem is associated with the presence of information uncertainty in the complex accounting of heterogeneous characteristics, which in some cases can’t be normalized and brought to a single measurement scale. The authors give formalized statements of the tasks to be solved. A conceptual data model is proposed. One of the options for formalizing such a data model is the transition to a vector representation of the information space. The criterion for evaluating belonging to a certain class is the argument for minimizing the distance between information elements in the vector space. The procedure for the accumulation by an intelligent system of precedent models set, which is a stage of machine learning, is described. After passing it, the intelligent system becomes capable of assessing the semantic similarity of operationally obtained models with precedents that have already fallen into the category of templates. The criterion for evaluating the effectiveness of an intelligent decision support system is the semantic similarity of the precedent model. A heuristic algorithm for determining semantic similarity was proposed. To optimize the time spent on supporting decision-making on the prevention and elimination of the emergency situations consequences, the authors also propose to use decentralized bioinspired methods, the advantages of which are internal procedures that provide diversification of the search space to exit from local optima and quickly obtain quasi-optimal solutions to the problem. The development of a modified bacterial optimization method (MBOM) was described. A software application has been created to conduct a computational experiment. The results of the conducted studies confirmed the advantages of the bacterial optimization proposed modified method.