Optimal placement of distributed generation (DG) is an essential task for distribution companies in order to operate the distribution network in good operating conditions. Optimal placement of DG units is an optimization problem where minimization of active power losses in the network is considered as an objective function. In this paper, hybrid genetic dragonfly algorithm is used as an optimization technique to find the optimal location and size of distributed generation units. The proposed algorithm is implemented on IEEE 15 and PG & E 69 bus distribution systems in MATLAB environment. Based on the simulation results it has been observed that with proper placement and size of DG units, distribution network can be operated with less active power losses.Keywords Distributed generation • Optimal placement • Hybrid genetic dragonfly algorithm • Active power loss Nomenclature ΔY t+1 Step vector for dragonflies A iAlignment value of dragonfly i C i Cohesion value of dragonfly i E iEnemy source of dragonfly i F iFood source of dragonfly i I k i Current at bus 'i' and iteration 'k' I k LR Current injecting at receiving node of line 'L' at iteration 'k' I l Current flowing through line l I k L Current through line 'L' at iteration 'k' I max l

Optimal placement of Distributed Generation (DG) is an important activity for distribution network operator to operate the network with more optimal in terms of real power losses (RPL). In this paper, a meta-heuristic algorithm called firefly algorithm has been used find optimal location and size of DG units based on RPL. The proposed approach is implemented on IEEE 15 bus and PG & E 69 bus distribution systems in MATLAB environment. Based on simulation results, it has been observed that firefly algorithm is performing well to identify optimal location and size with minimum RPL.

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The optimal placement of distributed generation (DG) is a critical task for distribution companies in order to keep the distribution network running smoothly. The optimal placement of DG units is an optimization problem. In this paper, minimization of the voltage deviation from flat voltage is considered as an objective function. The self-adaptive Lévy flight-based Jaya algorithm is used as an optimization technique to determine the best location and size of distributed generation units. In the MATLAB environment, the proposed algorithm was implemented on IEEE 15 and PG and E 69 bus distribution systems. According to the simulation results, distribution networks can supply more quality power to customers by minimizing the voltage deviation from the flat voltage profile if the DG units are properly placed and sized.

The optimal location of distributed generation (DG) is a critical challenge for distribution firms in order to keep the distribution network running smoothly. The optimal placement of DG units is an optimization challenge in which the objective function is to maximize distribution firms’ financial benefit owing to reduced active power losses and emissions in the network. Bus voltage limits and feeder thermal limits are considered as constraints. To overcome the problem of trapping the solution toward the local optimal point and to achieve strong local and global searching capabilities, a new hybrid Jaya–Red Deer optimizer is proposed as an optimization approach in this study to determine the best placement and size of distributed generating units. In the MATLAB environment, the suggested method is implemented on IEEE 15 and PG & E 69 bus distribution systems and validated with Red Deer Optimizer, Dragonfly Algorithm, Genetic Algorithm, Particle Swarm Optimization, Jaya Algorithm and Black Widow Optimizer. Based on the simulation results, distribution firms may operate their networks with the greatest financial advantage by properly positioning and sizing their DG units.

Parallel processing in Distributed Computing refers to the concept of running several tasks simultaneously on different processors. Load balancing and scheduling are very important tasks to optimally utilize the available resources and processor utilization. These problems are NP-Complete. In this paper, we introduce two methods which are genetic algorithms and Honey bee algorithms for scheduling and load balancing in parallel heterogeneous multiprocessor systems. Genetic algorithm is a meta-heuristic algorithm which provides optimization by performing genetic operations on the running tasks and Honey bee algorithm is a Bio-inspired algorithm which provides optimization based on foraging behaviour of honey bees. Finally these two types of algorithms provides better optimization than the normal CPU scheduling algorithms, it is studied by comparing the results of meta-heuristic and bio-inspired algorithms with CPU scheduling algorithms, such as Longest Processing Time (LPT) and Shortest Processing Time (SPT) [2]. The results of simulations indicate meta-heuristic and bio-inspired algorithms for scheduling and load balancing provides better total response time and system utilization. Simulations results indicate Genetic Algorithm and Honey bee algorithm reduces overall response time and also increases resource utilization in load balancing of distributed systems.

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