Real-world optimization problems are often very difficult to solve, and many applications face NP-hard problems [1]. To solve such problems it is necessary to use optimization tools although there is no guarantee that the optimal solution can be obtained. In fact, for NP problems there is no efficient algorithm. As a result, many problems must be solved by trial and error using various optimization techniques [2]. Furthermore, new algorithms are being developed to see if they can cope with these challenging optimization problems. Among these new algorithms, many algorithms such as particle swarm optimization, cuckoo search, and firefly algorithm, have gained popularity due to their high efficiency. In this article we used the IBACH algorithm to solve integer programming problems. Integer programming is an NP-hard problem[3-10]. The name “integer linear programming” refers to the class of constrained combinatorial optimization problems with integer variables, where the objective function is a linear function and the constraints are linear inequalities. " The Integer Linear Programming (also known as LIP) optimization problem can be formulated in the following general form:Max cx (1)st Ax ≤ b, (2) xZn (3) where the solution x∈ Zn is a vector of n integer variables: x = (x1, x2 , …, xn)T and the data are rational and are given by the m×n matrix A, by the 1×n matrix and by the m×1 matrix b equality, because each equality constraint can be represented by two inequality constraints such as those included in eq. (2). Integer programming addresses the problem raised by the non-integer..... . of particle swarm optimization (PSO), from the standard bat standard harmony search algorithm (HS) and from the improved harmony search algorithm (IHS). results of the IBACH algorithm are very close to the exact values ​​of the selected problems under study. If a large number of variables need to be found, then it is difficult to go beyond classical methods. More often, however, users will choose to use the proposed algorithm, to save time and gain reliability. for example, when we solved the test problem number 6 with the proposed algorithm, it took 7 seconds, but when we solved it with branch and bound (exact method) it took 396 seconds. The reason for obtaining better results than the other considered algorithms is that the search power of the bat. In addition to that, the use of chaos improves the performance of the algorithm.