Sensor nodes spend the most of their limited energy on communicating with environmental information gathered in receivers. Hence, it is important to determine the optimal monitoring sensor nodes and information flow paths to the destination and sink in order to survive the sensor networks. Additionally, the heavy traffic load for transferring packets in nodes closer to the sink increases energy consumption and reduces battery life. It is desirable to reduce the energy between nodes and sink. The main goal is to extend the network lifetime through extending the lifetime of operating sensors as well transferring gathered data from super node to the sink. In this paper, Bat Algorithm (BA) is used to select the optimum monitoring sensor node and resulted path to reduce energy consumption. Simulation results and comparison with other algorithms show the superiority of the proposed algorithm. The simulation results of the proposed algorithm show that the proposed algorithm has been able to reduce the power consumption of the network and increase the lifetime of the network. Also, the proposed algorithm is able to outperform the comparable algorithms on average by 27%. INDEX TERMS Wireless sensor networks, energy, lifetime, Bat algorithm. I. INTRODUCTION Recent technological advances in microelectromechanical systems and integrated circuits have led to the development of small sensor nodes having high processing power and low power consumption. These sensors are used in various fields such as multimedia, medical, monitoring, military, and domestic fields. As an example we can name Handheld computer pagers and cell phones. A set of these sensors is called Wireless Sensor Networks (WSN) which forms a powerful network capable of sampling local values, processing, and sending them to other sensors, and ultimately to the main observer (user). Quality of service is a Combinatory criterion with extensive usages and is one of the network designers' goals, that evaluates many designed [1], [2]. The main challenge in the design of wireless and mobile systems is twofold: telecommunication bandwidth and The associate editor coordinating the review of this manuscript and approving it for publication was Tie Qiu .

The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

In a real manufacturing environment, the set of tasks that should be scheduled is changing over the time, which means that scheduling problems are dynamic. Also, in order to adapt the manufacturing systems with fluctuations, such as machine failure and create bottleneck machines, various flexibilities are considered in this system. For the first time, in this research, we consider the operational flexibility and flexibility due to Parallel Machines (PM) with non-uniform speed in Dynamic Job Shop (DJS) and in the field of Flexible Dynamic Job-Shop with Parallel Machines (FDJSPM) model. After modeling the problem, an algorithm based on the principles of Genetic Algorithm (GA) with dynamic two-dimensional chromosomes is proposed. The results of proposed algorithm and comparison with meta-heuristic data in the literature indicate the improvement of solutions by 1.34 percent for different dimensions of the problem.

One of the most applicable versions of the Vehicle Routing Problem (VRP) which has been widely studied in logistic services is Capacitated Vehicle Routing Problem (CVRP). There are many algorithms to solve the CVRP to minimize total travelled distance. Some of the most recent and efficient metaheuristic algorithms are capable of generating solutions within 0.5% to 1% gap from the optimum for instance problems adopted from the literature considering hundreds or thousands of demand points. In this contribution, a novel hybrid algorithm is proposed based on Gravitational Emulation Local Search (GELS) and Genetic Algorithm (GA). This algorithm alleviates the weaknesses of the GELS algorithm. The performance of the proposed algorithm, which is called GELSGA, is compared with other meta-heuristics. The obtained results show that the proposed algorithm can compete vigorously with them. In addition, the proposed algorithm could obtain solutions close to the Best Known Solutions (BKS) for many instance problems.

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