I. INTRODUCTIONThe ongoing research in solving optimization problems has developed new optimization approaches that achieved advantages over more traditional techniques. The need for getting new optimization techniques stemmed from the fact that the traditional techniques such as mathematical programming approaches have become inefficient for solving real-life applications.The large scale of systems, numerous design variables and practical requirements of designs in actual applications make the present day problems difficult to be handled using traditional optimization methods [1].Recently, metaheuristic techniques are used to get a robust solution in solving complex problems. These algorithms tend to produce different solutions even when their initial conditions remain constant at each run because of their random nature. They are preferred for these functions which have several local optima as they can escape from local minima easily in spite of their slow convergence speed [2].The basic idea behind these techniques is to simulate biological and physical systems in nature, such as natural evolution, immune system, swarm intelligence, annealing process, etc., in a numerical algorithm. These algorithms differ in the way the moves in the search space based on an associated nature-inspired strategy [3].One of the promising metaheuristic methods, namely, the Bat Algorithm (BA), depends on simulating the echolocation behavior of bats. The capability of echolocation of micro bats is fascinating as these bats can find their prey and discriminate different types of insects even in complete darkness. They achieve this by emitting calls out to the environment and listening to the echoes that bounce back from them. They can identify the location of other objects and instinctively measure how far they are away from them by following delay of the returning sound [4].From a quick literature review, it is found a set of researches that make a set of modifications or hybridizations on the standard BA to enhance its performance. Iztok F. made hybridization between the differential evolution strategies and the standard BA and named it as a hybrid bat algorithm. It has shown that this algorithm improves significantly the original version of BA [5].Yilmaz S enhanced exploration and exploitation mechanisms of BA by three modifications, the first one it analyzed the structure of velocity with the inertia weighted update process. The second one it takes into consideration the difference between the solution and the global best solution to get the closest and the farthest dimension of the solution. The third modification is making hybridization between Artificial Bee Colony and BA to improve the exploration capability of BA [6].Chen Z. removed the velocity parameter and added the inertia weight of location in BA which is determined using normal distribution and then the frequency of the micro bats emitted pulses adjust to the change of random position and optimal position of the micro bats [7].Amir H. introduced chaos into BA to increa...

This study presents a proposed fuzzy approach for solving three level linear programming problems. This approach does not increase the complexities of original problems and usually solves a multilevel programming problem in less number of iterations. Numerical examples are used to compare the proposed approach with several approaches in the literature.