УДК 519.854.33 Alexander Antamoshkin, Igor Masich HEURISTIC SEARCH ALGORITHMS FOR MONOTONE PSEUDO-BOOLEAN FUNCTION CONDITIONAL OPTIMIZATION The heuristic algorithms for solving conditional pseudo-Boolean optimization problems have been developed and investigated for special problem classes. <...> The algorithm of adaptive random search of boundary points that combines random search and greedy heuristic has been created. 1. <...> At the same time a part of functions or all ones in many realworld problems are given by an algorithm. <...> As for exact search algorithms, let us note the regular algorithms [2, 3, 4] developed for chosen pseudoBoolean function classes. <...> These algorithms of pseudoBoolean optimization are essentially unimprovable in the sense that they realize informational complexity of the problem classes. <...> But capability of exact methods is very limited especially for solving of high dimensionality problems. <...> The exact polynomial algorithms for conditional optimization problem classes considering in this article are not known. <...> Most known heuristic search algorithms are adaptive search algorithms like simulated annealing, genetic and evolutionary algorithms [9, 13]. <...> In this work heuristic algorithms are created for monotone pseudo-Boolean function optimization problems with various types of constraints. <...> The set k-neighboring to X is called level k of point X. Definition k 3. <...> PROPERTIES OF THE FEASIBLE SOLUTION SET solution set of the problem (1) [2]. <...> A point A∈Y a set A if there exists X O (Y)1 Definition 11. <...> A point ∈ Carry out research into properties of the feasible is a boundary point of limiting point of a set A with a basic point ∈0 X ∉ A holds for . <...> Later on we will consider case when the optimal solution of the conditional optimization problem doesn’t coincide with the optimal solution of the corresponding optimization problem without constraints, i.e. the constraint (or the set of constraints) defining the feasible area of the binary variables space is an active. <...> It is necessary to notice that if the constraint is active then the optimal solution belongs to the set of boundary points. <...> If the objective function is a monotone unimodal function and the constraint is an active one Проблемы машиностроения и автоматизации <...>