Abstract: The subject of study is a new segmentation algorithm that allows improving the quality and speed of image processing in comparison with known algorithms. The authors consider the problem of segmentation of medical images and existing approaches to its solution. It is noted that segmentation is the most difficult part in the processing and analysis of medical images of biological tissue, since it is necessary select areas that correspond to different objects or structures on histological specimens: cells, organelles and artifacts. Particular attention is paid to algorithms of particle swarms and k-means. In solving the problem, authors use swarm intelligence methodology, cluster analysis, the theory of evolutionary computation, mathematical statistics, computer modeling and programming. The article suggests a new hyper-heuristic algorithm and its modification to solve the problem of segmentation of medical images in order to improve image quality and processing speed. Authors present experimental results obtained on the basis of test data from a known set of medical MRI images using the software developed by the authors. The optimal values of coefficients that determine the behavior and efficiency hyper heuristics that reduces the number of iterations of the algorithm are defined. The results demonstrate the advantage and confirm the efficiency of hyper heuristics algorithms in systems of digital medical imaging solutions to the problem of segmentation of medical images.

Abstract: The subject of the article is the current state, problematic issues and promising field of research of bio heuristics for solving optimization problems. Bio heuristics are mathematical transformations of the input stream to the output data based on simulation mechanisms of evolution, natural analogies, on a statistical approach to the study of situations and iterative approximation to the desired solution. Currently, bio heuristics have become an important tool for finding close to optimal solutions of problems which earlier were considered unsolvable. The methodological and theoretical bases of the scoping study are optimization techniques and decision making support methods, artificial intelligence, evolutionary computation theory. The article analyzes the fundamental results obtained in the field of bio-heuristic optimization algorithms: Holland theorem and TAD-theorem. The article establishes patterns and structure of bio heuristics, especially coding solutions, basic cycle of bio heuristics algorithms. The study reviews a promising direction in the analysis time of the biological cognitive heuristics - drift analysis.