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Using swarm intelligence algorithms to determine the composition of a multi-project

One of the promising ways to reduce the dependence of domestic industry on the supply of critical goods, components and raw materials necessary for the construction and effective functioning of multi-stage production and technological chains is to intensify processes for their import substitution, including through various measures of state support. However, the critical need for a wide range of products requires the selection of the most “promising” projects for inclusion in program-target documents using a set of criteria (sometimes even non-financial). As a result, there arises an urgent scientific and practical task of developing approaches to the formation of multi-projects (a set of projects) that can qualify for state support under various programs to reduce import dependence and overcome the technological backwardness of Russian industry, based on the use of modern economic and mathematical methods. In the application to this task, a multi-project can be represented as a “set” (unrelated projects), a “chain” (rigid sequence of projects) or a “network” (projects with complex logical-temporal interrelations). The specifics of each type determine the conditions and impose restrictions on the processes of selecting components for inclusion in their composition, which consists of finding the best combination of projects and/or programs, i. e., it is reduced to a task of conditional multidimensional optimization. In the absence of a requirement to find a “strictly optimal” composition, one can use metaheuristic methods that are capable of finding solutions close to these in an “acceptable” time. Among them, the largest and most well-known class are swarm intelligence algorithms based on the principles of collective behavior of a population of living organisms. To form the composition of multi-projects, the article proposes to use algorithms inspired by the collective behavior of a pack of wolves (Grey Wolf Optimizer) and a school of fish (Fish School Search) to satisfy their food needs. To increase the efficiency of their use for solving the task of finding the best composition of a “set” and “network” of projects, their hybridization with fuzzy logic methods (in particular, fuzzy clustering and fuzzy-logical inference) was proposed. Read more...

Model for assessing energy and resource efficiency of process recycling small-ore raw material based on a tree of fuzzy inference systems

The article presents a software model of complex processes of small-scale ore raw material processing based on a trainable tree of fuzzy logical inference systems. Processing of such raw materials not only provides a valuable end product, such as yellow phosphorus, but also helps to solve the problem of ore waste disposal, the fine fractions of which create significant environmental damage to the territories adjacent to mining and processing plants. The technological system of small-scale ore raw material processing consists of energy-intensive units, so even a slight relative reduction in resource and energy costs leads to large savings in absolute figures. Such a reduction can be achieved by optimizing the control of units, the synthesis of which requires the availability of process models, so improving the methods and tools for modeling is an urgent research task. A feature of the proposed model is that its inputs are not only variables describing resource transformations, but also variables reflecting the energy costs of individual technological units. This allows using the model to calculate the energy and resource efficiency of small-scale ore raw material processing. The hierarchical structure of the fuzzy-logical tree is capable of reflecting the interrelationship of processes of various natures accompanying the processing of small-scale raw materials, and also contributes to increasing the efficiency of its training by dividing the feature space of large dimension into several groups on which individual tree nodes are trained. The program developed in the MatLab environment and implementing the proposed model showed high regression accuracy on a synthetic set of input data, which may indicate the feasibility of using the proposed model in problems of optimizing control systems for processing small-scale raw materials according to the criterion of energy and resource efficiency. Read more...