Hub location problems (HLP) are synthetic optimization problems that appears in telecommunication and transportation networks where nodes send and receive commodities (i.e., data transmissions, passengers transportation, express packages, postal deliveries, etc.) through special facilities or transshipment points called hubs. In this paper, we consider a central mine and a number of hubs (e.g., factories) connected to a number of nodes (e.g., shops or customers) in a network. First, the hub network is designed, then, a raw materials transportation from a central mine to the hubs (i.e., factories) is scheduled. In this case, we consider only one transportation system regarded as single machine scheduling. Furthermore, we use this hub network to solve the scheduling model. In this paper, we consider the capacitated single allocation hub covering location problem (CSAHCLP) and then present the mixed-integer programming (MIP) model. Due to the computational complexity of the resulted models, we also propose two improved meta-heuristic ALGORITHMS, namely a genetic algorithm and a SHUFFLED FROG LEAPING algorithm in order to find a near-optimal solution of the given problem. The performance of the solutions found by the foregoing proposed ALGORITHMS is compared with exact solutions of the mathematical programming model.

The optimal design and construction of sewage networks have always been considered by researchers and experts due to the very high costs of implementing this infrastructure. Being consisted of various variables and subjected to complex constraints, conventional mathematical optimization procedures are unlikely to be able to solve sewage network optimization problems. Thus, utilizing meta-heuristic optimization ALGORITHMS is a must to tackle these problems. The SHUFFLED FROG LEAPING algorithm ((SFL)A) is one of the new meta-heuristic ALGORITHMS which has shown its ability to solve a large number of optimization problems. In this research, the capability of the (SFL)A in solving the problem of optimal design of sewage networks has been investigated. The diameter of the pipes as discrete decision variables and the depth of pipe placement as continuous decision variables were simultaneously considered in this study as unknowns. To this end, three sewage networks with 13, 41, and 65 decision variables have been selected as case studies. Various technical, operational, and hydraulic constraints are controlled by defining appropriate penalty functions. The results showed that for case studies 1 and 3, the (SFL)A decreased the minimum construction costs derived by GA, PSO, and SCE ALGORITHMS by 0.43 and 3.2 percent respectively, and for the second case study, with the less number of function evaluations, (SFL)A has reached the equal objective function compared to other ALGORITHMS.

Various methods have been proposed for constructing and optimizing fuzzy inference systems. This paper first proposes a new method to cre-ate zero-order Sugeno fuzzy inference systems using the SHUFFLED FROG LEAPING Algorithm ((SFL)A). As the second contribution, the paper introduces four improvements over (SFL)A. The resulting version of (SFL)A, called I(SFL)A (Improved (SFL)A), is also applied to create zero-order Sugeno fuzzy inference systems. We conducted experiments to assess I(SFL)A and compare it with the original (SFL)A and three well-known evolutionary ALGORITHMS over five standard classification data sets from the UCI machine learning repository. The experimental results show that I(SFL)A creates fuzzy systems more efficiently than the standard (SFL)A and some other evolutionary ALGORITHMS, i. e., GA, ACO and PSO. Moreover, with respect to the accuracy and the convergence speed criteria, I(SFL)A and PSO outperform other evolutionary ALGORITHMS, while their performance is comparable to each other.

In this paper a new method has been proposed to solve Combined Heat and Power Economic Dispatch (CHPED) based on Modified SHUFFLED FROG LEAPING Algorithm (M(SFL)A). Difficulty on complexity of the problem is related to its constraints. This algorithm can satisfy constraints simply. This optimization algorithm has a wide spread search space and this characteristic helps to achieve optimum solutions. Application of M(SFL)A in CHPED problem has been simulated on two test cases. Numerical results show that M(SFL)A has proposed better solutions in comparison with other existing methods.

Several industrial products such as moulds, dies, engine block, automotive parts, etc., require machining of a large number of holes. Similarly, applications like boiler plates, food-business processing separator's, printed circuit boards, drum and trammel screens, etc., consist of a matrix of a large number of holes. Many machining operations, such as drilling, enlargement, tapping, or reaming, are needed to achieve the final sizes of individual holes, resulting in a variety of possible sequences to complete the hole-making operations. The major issue involved in hole-making operations is the tool travel time. It is often vital to determine the optimal sequence of operations so that the overall processing cost of hole-making operations can be minimized. In this work, thus an attempt is made to minimize the total tool travel of hole-making operations by using a relatively new optimization algorithm known as modified SHUFFLED FROG LEAPING for the determination of the optimal sequence of operations. Modification is made in the present SHUFFLED FROG-LEAPING algorithm by using three parameters with their positive values in order to widen the search capability of the existing algorithm. This paper considers three case studies of a rectangular matrix of holes to explain the proposed procedure. The outcomes of optimization with a modified SHUFFLED FROG-LEAPING algorithm are compared to those obtained with the genetic algorithm and the ant colony algorithm. Additionally, the higher dimensional problem of 20 x 20 rectangular matrix of holes is considered in this work.

The niche methods for search ALGORITHMS are important techniques in optimization. Most niche techniques need some extra tunable parameters to get a better performance. Achieving a good method for multimodal optimization by heuristic ALGORITHMS will be possible if and only if the population diversity is preserved. SHUFFLED FROG LEAPING ((SFL)) algorithm is a new heuristic algorithm that its ability is not proved for solving the multimodal problems. This paper proposes a niche method for (SFL). Several benchmark problems are considered for testing the robustness and effectiveness of the proposed method over the results available in the literature. The results show that the proposed method performs well.