Journal of Advances in Computer Research
Volume:12 Issue: 1, Winter 2021

Recently, Combined Heat and Power (CHP) systems have been utilized increasingly in power systems. With the addition penetration of CHP-based co-generation of electricity and heat, the determination of economic dispatch of power and heat becomes a more complex and challenging issue. The optimal operation of CHP-based systems is inherently a nonlinear and non-convex optimization problem with a lot of local optimal solutions. In this paper, the Improved Shuffled Frog Leaping Algorithm (ISFLA) is used for solution of the problem. ISFLA is an improved version of shuffled frog leaping algorithm in which new solutions are produced in respect to global best solution. The ISFLA is well able to attain the optimal solutions even in the case of non-convex optimization problems. To evaluate the efficiency of the proposed method, it has been implemented on the standard test system. The obtained results have been compared with other heuristic methods. The numerical results show that the ISFLA is faster and more precise than other methods.

Due to the growing number of videos available on the web, it seems necessary to have a system that can extract users' favorite videos from a huge amount of information that is increasing day by day. One of the best ways to do this is to use referral systems. In this research, a method is provided to improve the recommender systems in the field of film recommendation to the user. In this research, DBSCAN clustering algorithm is used for data clustering. Then we will optimize our data using the cuckoo algorithm, then the genetic algorithm is used to predict the data, and finally, using a recommender system based on participatory refinement, a list of different movies that can be of interest to the user is provided. The results of evaluating the proposed method indicate that this recommender system obtained a score of 99% in the accuracy of the system and a score of 95% in the call section Suggest the user's favorite videos correctly to the user.

Fog computing is an new approach to evolving the cloud computing platform and extending the Internet of Things to the edge of the network. In this type of computing, service providers can control signals by assigning specific tasks to users.By the substantial increase in the Internet of Things (IoT)the classic centralized cloud computing method has faced several challenges such as high delay, low capacity and network defects.Fog computing brings cloud waves closer to IoT devices to face with these challenges. The fog provides local processing and storage of IoT data on IoT devices, instead of sending them to the cloud, and provides faster response and better quality in comparison to the cloud The present study was conducted to investigate the allocation of common radio and computing resources to optimize system performance and user satisfaction.in which The effects of cache parameters, CPU clock frequency performance, bandwidth, CPU cycle, and IPC (instructions per cycle / hour) on IoT-Fog users are evaluated to provide a model for allocating radio and computing resources in IoT computing Distribution of computational and radio resource allocation solutions uses a compatible game framework called (SPA). The results of the proposed optimized framework can bring the system performance closer to the desired level from the users' point of view