gray wolf optimization algorithm

With the expansion of human activities, the volume of waste and hazardous waste produced has increased dramatically. Increasing the volume of waste has created challenges such as transportation hazards, cleanup, disposal, energy consumption, and most impor tant environmental problems. The difficulty of unsafe waste control is one of the critical studies topics. Finding the o ptimal location of hazardous waste disposal is one of the issues that, if done properly, can significantly reduce the aforementioned challenges. The increasing volume of information, the complexity of multivariate decision criteria, have led to the lack of conventional methods for finding the optimal location. Machine learning methods have proven to be effective and superior in many areas. In this paper, a new method based on machine learning for finding the optimal location of hazardous waste disposal is p resented . In the proposed method, after applying clustering in the separation of the desired areas, the gray wolf algorithm optimization is used to find the optimal location of waste disposal . In order to apply the gray wolf optimization algorithm, a multi variate target function is defined . Cluster centers as were chosen as location of waste disposal . Proposed method is performed on collected data from the study area in Iran, Tehran province . Proposed clustering method is evaluated and compared withs some metaheuristics algorithm. The simulation results of the proposed method show cost reduction in finding the desired locations compared to similar researches . Also, Xi and Separation index used for evaluation of proposed clustering method to select the best location. The number of best locations using Xi and Separation index claim the superiority of the proposed method

Resistance spot welding process of AISI 1060 steel has been experimentally investigated by studying the effects of welding current, electrode force, welding cycle and cooling cycle on tensile-shear strength. Using the response surface methodology, experimental tests are performed. An adaptive neural-fuzzy inference system is applied to model and predict the behavior of tensile-shear strength. Additionally, the optimal parameters of adaptive neural-fuzzy inference systems are obtained by the gray wolf optimization algorithm. For modeling the process behavior, the results of experiments have been employed for training (70% of data) and testing (30% of data) of the inference system. The results show that the applied network has been very successful in predicting the tensile-shear strength and the coefficient of determination and mean absolute percentage error for the test section data are 0.96 and 6.02%, respectively. This indicates the considerable accuracy of the employed model in the approximation of the desired outputs. After that, the effect of each input parameter on tensile-shear strength is quantitatively evaluated with the Sobol sensitivity analysis method. The results show that the tensile-shear strength of the joint rises by increasing the welding current and welding cycle and also decreasing the electrode force and cooling cycle.