Distributed Generation (DG) can help in reducing the cost of electricity to the costumer, relieve network congestion and provide environmentally friendly energy close to load centers. Its capacity is also scalable and it provides voltage support at distribution level. Hence, DG placement and penetration level is an important problem for both the utility and DG owner. The Optimal Power Flow (OPF) has been widely used for both the operation and planning of a power system. The OPF is also suited for deregulated environment. Four different objective functions are considered in this study: (1) Improvement voltage profile (2) minimization of active power loss (3) maximum capacity of conductors (4) maximization of reliability level. The site and size of DG units are assumed as design variables. The results are discussed and compared with those of traditional distribution planning and also with Imperialist competitive algorithm (ICA). Key words: Distributed generation, distribution network planning, multi-objective optimization, and Imperialist competitive algorithm.
The necessity for an efficient algorithm for resource allocation is highly urgent because of increased demand for utilizing the available spectrum of the wireless communication systems. This paper proposes an Enhanced Bundle-based Particle Collision Algorithm (EB-PCA) to get the optimal or near optimal values. It applied to the Orthogonal Frequency Division Multiple Access (OFDMA) to evaluate allocations for the power and subcarrier. The analyses take into consideration the power, subcarrier allocations constrain, channel and noise distributions, as well as the distance between user's equipment and the base station. Four main cases are simulated and analyzed under specific operation scenarios to meet the standard specifications of different advanced communication systems. The sum rate results are compared to that achieved with employing another exist algorithm, Bat Pack Algorithm (BPA). The achieved results show that the proposed EB-PAC for OFDMA system is an efficient algorithm in terms of estimating the optimal or near optimal values for both subcarrier and power allocation.
With the substantial growth of mobile applications and the emergence of cloud computing concepts, therefore mobile Cloud Computing (MCC) has been introduced as a potential mobile service technology. Mobile has limited resources, battery life, network bandwidth, storage, and processor, avoid mobile limitations by sending heavy computation to the cloud to get better performance in a short time, the operation of sending data, and get the result of computation call offloading. In this paper, a survey about offloading types is discussed that takes care of many issues such as offloading algorithms, platforms, metrics (that are used with this algorithm and its equations), mobile cloud architecture, and the advantages of using the mobile cloud. The trade-off between local execution of tasks on end-devices and remote execution on the cloud server for minimizing delay time and energy saving. In the form of a multi-objective optimization problem with a focus on reducing overall system power consumption and task execution latency, meta-heuristic algorithms are required to solve this problem which is considered as NP-hardness when the number of tasks is high. To get minimum cost (time and energy) apply partial offloading on specific jobs containing a number of tasks represented in sequences of zeros and ones for example (100111010), when each bit represents a task. The zeros mean the task will be executed in the cloud and the ones mean the task will be executed locally. The decision of processing tasks locally or remotely is important to balance resource utilization. The calculation of task completion time and energy consumption for each task determines which task from the whole job will be executed remotely (been offloaded) and which task will be executed locally. Calculate the total cost (time and energy) for the whole job and determine the minimum total cost. An optimization method based on metaheuristic methods is required to find the best solution. The genetic algorithm is suggested as a metaheuristic Algorithm for future work.