In general, path-planning problem is one of most important task in the field of robotics. This paper describes the path-planning problem of mobile robot based on various metaheuristic algorithms. The suitable collision free path of a robot must satisfies certain optimization criteria such as feasibility, minimum path length, safety and smoothness and so on. In this research, various three approaches namely, PSO, Firefly and proposed hybrid FFCPSO are applied in static, known environment to solve the global path-planning problem in three cases. The first case used single mobile robot, the second case used three independent mobile robots and the third case applied three follow up mobile robot. Simulation results, which carried out using MATLAB 2014 environment, show the validity of the kinematic model for Nonholonomic mobile robot and demonstration that the proposed algorithm perform better than original PSO and FF algorithms under the same environmental constraints by providing the smoothness velocity and shortest path for each mobile robot.
Directional sensors in wireless visual sensor networks attract growing attention as a promising tool for monitoring the real world; directional sensors consume energy for two main tasks: sensing and communication. Since a VSN contains a number of configurable visual sensors with changeable spherical sectors of restricted angle known as a field of view that is intended to monitor a number of targets located in a random manner over a given area. Therefore maximizing the network lifetime through minimizing power consumption while covering the targets remains a challenge. In this paper, the problem of obtaining a disjoint set cover includes a minimum number of camera sensors is solved. The problem is known to be NP-complete. The sustainable design is improving an existing Iterative Target Oriented Algorithm (ITOA) to cover moving targets move randomly over a given area of deployment starting from entry points reaching to exit ones in a realistic simulation. To evaluate the performance of the modified algorithm, a comparison is provided with three existing algorithms (Iterative centralized Greedy Algorithm (ICGA), Iterative Centralized Forced-directed Algorithm ICFA, and Iterative Target Oriented Algorithm ITOA). Simulation results revealed that the sustainable scheme can find a disjoint set with a minimum number of sensors covers the maximum number of moving targets in an energy-efficient way and extended network lifetime.
The classical job shop scheduling (JSS) problem can be extended by allowing processing of an operation by any machine from a given set. This type of scheduling is known as flexible job shop scheduling (FJSS) problem. It incorporates all the difficulties and complexities of its predecessor classical problem. However, it is more complex as it is required to determine the assignment of operations to the machine. Swarm intelligence techniques proved their effectiveness in solving a wide range of complex NP-Hard real world problems. One of these techniques is the meerkat clan algorithm (MCA) that has been successfully applied to various optimization problems. This paper presents a modified MCA for solving the FJSS problem. The modification is based on using harmony search (HS). The introduction of HS provides more exploitation and intensification. HS generates various solutions, which are provided to the MCA. As a result, the exploitation of the local optimum is increased, which in turn increases the convergence rate. The experimental results show that the improved method achieves higher quality schedules. Additionally, the convergence rate is speeded up compared with the standalone algorithm. This gives the proposed method the superiority over the original algorithm.
A network is defined as a set of nodes that are associated with a way to handle and transfer data and messages from source to destination. The congestion in the network occurs when a lot of traffic occurs, leads to delay, packet loss, bandwidth degradation, and high network overhead. Load balancing algorithms have been designed to reduce congestion in the network. Load Balancing is the redistribution of workload between two or more nodes to be executed at the same time. Two policies of load balancing algorithms: static and dynamic load balancing. This paper proposes a load balancing algorithm based on the hybrid (static and dynamic) policy using Network Simulator (version 2). The hybrid policy is used to improve network performance by redistributing the load between overloaded nodes to other nodes that are under loaded when congestion occurs. The simulation results show that the proposed algorithm used performance of the network with regard to throughput, packet delivery ratio, packet loss and the end-to-end delay.
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