whale optimization algorithm

Clustering is a method of data analysis and one of the important methods in data mining that has been considered by researchers in many fields as well as in many disciplines. In this paper, we propose combining WOA with BA for data clustering. To assess the efficiency of the proposed method, it has been applied in data clustering. In the proposed method, first, by examining BA thoroughly, the weaknesses of this algorithm in exploitation and exploration are identified. The proposed method focuses on improving BA exploitation. Therefore, in the proposed method, instead of the random selection step, one solution is selected from the best solutions, and some of the dimensions of the position vector in BA are replaced We change some of the best solutions with the step of reducing the encircled mechanism and updating the WOA spiral, and finally, after selecting the best exploitation between the two stages of WOA exploitation and BA exploitation, the desired changes are applied on solutions. We evaluate the performance of the proposed method in comparison with other meta-heuristic algorithms in the data clustering discussion using six datasets. The results of these experiments show that the proposed method is statistically much better than the standard BA and also the proposed method is better than the WOA. Overall, the proposed method was more robust and better than the Harmony Search Algorithm (HAS), Artificial Bee Colony (ABC), WOA and BA.

Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically disbursed different venders. Energy efficiency in data centers has become a hot topic in recent years as more and larger data centers have been established and the electricity cost has become a major expense for operating them. Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in server consolidation. In the past few years, many approaches to virtual machine placement have been proposed, but existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines. In this paper, we proposed a new approach for placement based on Discrete Chaotic whale optimization Algorithm. First goal of our presented algorithm is reducing the energy consumption in datacenters by decreasing the number of active physical machines. Second goal is decreasing waste of resources and management of them using optimal placement of virtual machines on physical machines in cloud data centers. By using the method, the increase in migration of virtual machines to physical machines is prevented. Finally, our proposed algorithm is compared to some algorithms in this area like FF, ACO, MGGA, GSA, and FCFS.

Following the development of wireless sensor networks, the need to design a low-waste, scalable, and long-life network is felt more than ever. Clustering and routing are widely used to minimize energy consumption and increase network lifetime, as important issues in wireless sensor networks. Since, in these networks, the largest amount of energy is spent on sending and receiving the data, the clustering technique done by collecting data on cluster heads has been found to influence the overall network performance; along with this, routine and efficient routing has also found to improve the network throughput. Therefore, multi-hop routing can increase the network lifetime and reduce the energy consumption by sensor nodes. In this paper, the main approach was using the mobile sinks attached to the public transportation vehicles, such as the bus to collect data in wireless sensor networks. The proposed protocol used multi-hop routing as well as Whale Optimization Algorithm to select cluster heads based on a fitness function, in which the amount of the remaining energy of the sensor nodes and the sum of the remaining energy of the adjacent sensor nodes were taken into account. Adopting this approach created a balance in the amount of energy consumption in sensor nodes. The proposed protocol was studied to validate the results obtained for the network lifetime and energy consumption. Independent and consecutive simulation results and statistical analysis indicates the superiority of the proposed protocol compared to other protocols. Also, the network lifetime improved by averagely 20% and the energy consumption reduced about 25% during the network activity.