The paper proposes a hybrid multi-agent solution search algorithm containing procedures that simulate the behavior of a bee colony, a swarm of agents and co-evolution methods, with a reconfigurable architecture. The developed hybrid algorithm is based on a hierarchical multi-population approach, which allows, using the diversity of a set of solutions, to expand the areas of search for solutions. Formulations of metaheuristics for a bee colony and a swarm of agents of a canonical species are presented. As a measure of the similarity of two solutions, affinity is used - a measure of equivalence, relatedness (similarity, closeness) of two solutions. The principle of operation and application of the directed mutation operator is revealed. A description of the modified chromosome swarm paradigm is given, which provides the ability to search for solutions with integer parameter values, in contrast to canonical methods. The time complexity of the algorithm is O(n2)-O(n3).

Keywords: swarm of agents, bee colony, co-evolution, search space, hybridization, reconfigurable architecture

The paper proposes the composite architecture of a multi-agent bionic search system based on swarm intelligence and genetic evolution for solving the problem of covering sets. The modified paradigm of the particle swarm is described, which provides, unlike the canonical method, the possibility of using positions with integer parameter values in the affine space. Mechanisms for moving particles in affine space to reduce the weight of affine bonds are considered. The developed position structures (chromosomes) are focused on the integration of swarm intelligence and genetic evolution. The time complexity of the algorithm, obtained experimentally, coincides with the theoretical studies and for the test problems considered is О(n2)- О(n3).

Keywords: covering with sets, a swarm of particles, genetic evolution, affine space, integer parameters, integration

We consider the problem of drawing up the implementation plan of the complex programs in multiprocessor computer systems (MCS). MCS is composed of several processors working in parallel. On MCS is input multiple independent streams of applications (programs) to be distributed among the processors. The computing system may consist of identical or different from the performance of processors. Taken into account when switching between different classes of applications received by the processor. The solution is presented as a job application distribution planning problem for processors and determining a queue of requests for service processor. Optimization of planning in the case of multi-level stage is to minimize the execution time of all applications. The basis of the work of the algorithm put the mechanisms of adaptive behavior of an ant colony. The time complexity of this algorithm depends on the lifetime of colonies (number of iterations) and the number of works and performers.

Keywords: multiprocessor system, planning, multi-level part, distribution task optimization, ant algorithm