The simultaneous optimization of multiple responses is an important problem in the design of industrial processes in order to achieve improved quality. In this paper, we present a new metaheuristic approach including Simulated Annealing and Particle Swarm optimization to optimize all responses simultaneously. For the purpose of illustration and comparison, the proposed approach is applied to two problems taken from the literature. The results of our study show that the proposed approach outperforms the other approaches and can find better solutions.Finally, in both cases, we present the results of a sensitivity analysis incorporating experimental design.

Metaheuristic optimization algorithms are a relatively new class of optimization algorithms that are widely used for difficult optimization problems in which classic methods cannot be applied and are considered as known and very broad methods for crucial optimization problems. In this study, a new metaheuristic optimization algorithm is presented, the main idea of which is inspired by models in kinematics. This algorithm obtains better results compared to other optimization algorithms in this field and is able to explore new paths in its search for desirable points. Hence, after introducing the projectiles optimization (PRO) algorithm, in the first experiment, it is evaluated by the determined test functions of the IEEE congress on evolutionary computation (CEC) and compared with the known and powerful algorithms of this field. In the second try out, the performance of the PRO algorithm is measured in two practical applications, one for the training of the multi-layer perceptron (MLP) neural networks and the other for pattern recognition by Gaussian mixture modeling (GMM). The results of these comparisons are presented in various tables and figures. Based on the presented results, the accuracy and performance of the PRO algorithm are much higher than other existing methods.

Metaheuristics have proved highly effective in addressing optimization challenges. Various algorithms address the clustering problem to find optimal centers for the clusters. One of the disadvantages of some of these algorithms is stagnation in local optima, especially for big data. If this problem is not properly solved, the clustering process will suffer. This research introduces a new hybrid method by merging the capabilities of two metaheuristic algorithms: Harris hawks optimization algorithm (HHO) and slime mould algorithm (SMA). These metaheuristic methods are employed to determine the best location for the cluster centers. optimization aims to reduce intra-cluster distance. In other words, the data points of each cluster should be close to its cluster center and also to avoid local optima. The effectiveness of these techniques is assessed and contrasted with the SMA and HHO algorithms on Iris, Vowel and Wine data sets. Compared to mentioned algorithms, our proposed method exhibits significantly improved convergence speed. The results also proved this method can properly find the optimal centers for clustering which finally improves the performance of the proposed method.

Various algorithms have proposed during the last decade for solving different complex optimization problems. The Meta-heuristic algorithms have been highly noted among researchers. In this paper, a new algorithm, known as the Buzzards optimization Algorithm (BUZOA), is introduced. Marvelous and special lifestyle of buzzards and their competition characteristics for prey has been the basic motivation for this new optimization algorithm. The algorithm performance has been compared with newest and well-known Meta-heuristics on some benchmark problems and test functions. Results have shown the high performance of the proposed BUZOA compared to the other well known algorithms.

For designing forest roads that in addition to minimize the cost of constructing, satisfy environmentalists, it is necessary to provide designers and contractors accurate data from surface and subsurface conditions of the road area due to the large-scale of data using of optimization methods (metaheuristic algorithms) with the help of computers is necessary. For this purpose, this research was carried out on a proposed forest road whit length of 1 km in the forests of the district 4 of Babakoh in the city of Siahkal in Guilan province. The cycle of all earthwork work was studied by continuous time study method and the information of the subsurface layers terms of digging was carefully recorded. The results of comparing the method of the ant colony with the hand-made design scenario showed that the multi-objective algorithm presented in this study has great potential for reducing the cost of earthwork operations and its volume. Based on the results, the algorithm is able to reduce 58. 55 and 64 percent the cost of earthwork operations and the volume of operations, respectively. Finally, the results of this research can be used as a guide to better management of forest areas, especially forest roads with the development of efficient methods in all forestry forests.

Using advanced techniques of econometrics and a metaheuristic optimization approach, this study attempts to evaluate the potential advantages of international portfolio diversification for East Asian international investors when investing in the Middle Eastern emerging markets. Overall, the results of both econometric and the metaheuristic optimization methods are supporting each other. Findings of this study highlight the potential role of the Middle Eastern equity markets in providing international portfolio diversification benefits for East Asian investors. It is also found that the long and the short-term efficient frontiers in any of the intra or inter-regionally diversified portfolios do not provide similar benefits.

1. Introduction: One of the main concerns in optimization methods is reduction the number of function evaluation which is mentioned in many studies like (Gholizadeh et al, 2018). The present study focuses on a method and series of actions designed to achieve the answer with the aim of minimization the total number of analysis,and so the time,needed for global and local search. Although the suggested elite particles method (EPM) can be used for any population based optimization method, but here it is applied to one of the fundamental and widely developed metaheuristic algorithms,namely particle swarm optimization (Eberhart et al, 1995) to handle the truss structures optimization with discrete design variables. As the original version of the assumed method suffers from the slow convergence rate,specially when dealing with the discrete optimization problems,the elite particles modification algorithm,which can be used in almost all population based metaheuristic optimization methods,will be implemented for that method. Here the elite particles method is attached with the particle swarm optimization method and the gained metaheuristic algorithm is named modified particle swarm optimization (MPSO). MPSO utilizes two computational strategies named ‘, Regeneration’,and ‘, Mutation’, . . .