Real-world optimization problems are often very difficult to solve, and many applications have to deal with NP-hard problems [1]. To solve such problems, optimization tools must be used even though 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]. Additionally, new algorithms were developed to see if they could solve these complex 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 have used the IBACH algorithm to solve integer programming problems. Integer programming is an NP-hard problem [3-10]. The name "linear integer 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 optimization problem of integer linear programming (also known as LIP) can be stated 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, the 1×nc matrix and the matrix m×1 b. This formulation also includes equality constraints, because each equality constraint can be represented by means of two inequality constraints like those included in the equation. (2).Integer programming solves the problem raised by non-integers...... middle of paper ...... of particle swarm optimization (PSO), standard harmony search algorithm (HS), standard bat algorithm and enhanced harmony search algorithm (IHS). In comparison with the exact values, we find that the 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 traditional methods. Most often, however, users will choose to use the proposed algorithm, to save time and gain reliability. for example, when we solved test problem number 6 with the proposed algorithm, it took 7 seconds, but when we solved it by branch and bound (exact method), it took 396 seconds. The reason why we obtained better results than the other algorithms considered is that the search power of the bat algorithm. Additionally, using chaos improves the performance of the algorithm..