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• MULTI-STAGE ANT ALGORITHM OF ONE-DIMENSIONAL PACKING BASED ON EFFICIENT DECISION ENCODING METHODS AND TWO-LEVEL EVOLUTIONARY MEMORY

  М.А. Ganzhur , B.К. Lebedev , О.B. Lebedev

  21-37

  2025-10-01

  Abstract ▼

  The aim of the work is to develop and study bioinspired search methods for solving problems of one-dimensional packaging in identical containers based on effective algorithms for encoding and decoding solutions, composite criteria and a two-level structure of evolutionary memory. The paper proposes the structure of an ordered code for packing one-dimensional elements into identical containers, the main advantage of which is that one packaging solution corresponds to one code and vice versa. The search procedure is based on the modified metaheuristics of the ant algorithm. At each iteration, the one-dimensional packing algorithm has a multistep structure. The stages are performed sequentially one after the other, starting from the first one. Each stage of the С_k includes procedures performed by the z_k agent. The number of stages is equal to the number of agents in the population plus the final iteration stage.
  The main task solved by the constructive algorithm at the С_k stage is to construct the R_k code for packing a set of X elements into identical containers. The stage is divided into periods according to the number of lists X_jk generated by the agent z_k. The period is divided into stages. In each period, the following tasks are solved sequentially in stages: agent z_k constructively generates a set Rk of ordered lists Xjk of onedimensional packaging in identical containers; fjk estimates of the packaging of each container O_j by elements of the list <X_jk> are calculated; the amount of λ_jk pheromone proportional to the f_jk estimate is calculated; the estimate W_k=∑_i(f_jk)  is calculated one-dimensional packing of a set of elements X into H identical containers; pheromone is deposited on the edges of graph G corresponding to the list X_jk in the cells of the accumulative memory matrix E of the second level. After all agents of the z_k population Z have formed ordered lists of R_k, the accumulated pheromone is added to the main memory matrix Φ of the first level. For each R_k, the total F_k indicator of the packaging quality of the set of X elements is calculated. The final operation in the iteration is pheromone evaporation on the edges of graph G and fixation of z_k with the best F_k. Experimental studies have been conducted to determine the quality of the method's operation on large-dimensional test sets. To compare the developed algorithm with known methods and approximate algorithms, the authors selected several groups of benchmarks from various sources

• SOLUTION OF THE PROBLEM OF INTELLECTUAL DATA ANALYSIS BASED ON BIOINSPIRED ALGORITHM

  E.V. Kuliev, D.Y. Zaporozhets, Y.A. Kravchenko, М.М. Semenova

  2022-01-31

  Abstract ▼

  The article discusses a bioinspired algorithm for solving the problems of intellectual analysis.
  The integration of bioinspired algorithms for solving data mining problems is a promising
  area of research. As a bioinspired algorithm, an algorithm based on the adaptive behavior of an
  ant colony is considered. The ant colony algorithm allows for a high-quality search for promising
  solutions to obtain optimal and quasi-optimal solutions. The algorithm has the ability to search for
  suitable logical conditions. The ant colony algorithm is based on the example of the behavior of
  living ants in nature. Ants are able to find the shortest solution by adapting to changes in the environment.
  The authors proposed a modified ant colony algorithm for solving the problem of data
  mining. The clustering problem was chosen as the task of data mining. Clustering is a combining
  of similar objects into groups, is one of the fundamental tasks in the field of data analysis and
  Data Mining. The list of application areas where it is applied is wide: image segmentation, marketing,
  anti-fraud, forecasting, text analysis and many others. The solution to this problem is of particular relevance in the context of the constantly growing volume of generated, transmitted and
  processed data. Classical clustering methods are optimized by combining with the proposed
  bioinspired optimization algorithm - the ant algorithm. The proposed method is a model in which
  ants are represented as agents that randomly move in the solution space with some restrictions
  (for example, obstacles in their path). To determine the effectiveness of the developed modified ant
  algorithm (ALA) with the clustering algorithm, the authors carried out a series of computational
  experiments. For comparison, we took the genetic algorithm, the monkey algorithm and the wolf
  algorithm. The simulation results prove that the clustering-based ant algorithm gives better results
  than other proposed algorithms.

• BIOINSPIRED ALGORITHM FOR SOLVING INVARIANT GRAPH PROBLEMS

  О.B. Lebedev, А.А. Zhiglatiy

  2022-11-01

  Abstract ▼

  A bioinspired method for solving a set of invariant combinatorial-logical problems on
  graphs is proposed: the formation of a graph matching, the selection of an internally stable set of
  vertices, and the selection of a graph clique. A modified paradigm of the ant colony is described,
  which uses, in contrast to the canonical method, the mechanisms for generating solutions on the
  search space model in the form of a star graph. The problem of forming an internally stable set of
  vertices in a graph can be formulated as a partitioning problem. At the initial stage, the same
  (small) amount of pheromone ξ/m, where m=|E|, is deposited on all edges of the star graph H.
  The process of finding solutions is iterative. Each iteration l includes three stages. Agents have
  memory. At each step t, the memory of the agent ak contains the amount of pheromone фj(t) deposited
  on each edge of the graph H. At the first stage, each agent ak of the population uses a constructive
  algorithm to find the solution Ur 0k, calculates the estimate of the solution ξk(Ur
  0k) and the value of the degree of suitability of the solution obtained by the agent φk (the amount of pheromone corresponding to the estimate). At the second stage, after the complete formation of solutions
  by all agents at the current iteration, the pheromone ωj accumulated in the j-th cell in the
  CEPб buffer array is added to each j-th cell of the main array Q2={qj|j=1,2,…,m} of the CEP0
  collective evolutionary memory. At the third stage, the general evaporation of the pheromone occurs
  on the set of edges E of the star graph H. The time complexity of the algorithm, obtained experimentally,
  coincides with theoretical studies and for the considered test problems is O(n2).