02481nas a2200229   4500000000100000000000100001008004100002260001200043653001500055653003100070653003700101653003300138100001700171700002500188700002000213245011600233856010000349300001200449490000600461520177000467022001402237       2019                            d  c12/201910aPower Loss10aRadial Distribution System10aRenewable Distributed Generation10aGenetic-Moth Swarm Algorithm1 aEmad Mohamed1 aAl-Attar Ali Mohamed1 aYasunori Mitani00aGenetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems  uhttps://www.ijimai.org/journal/sites/default/files/files/2019/10/ijimai20195_7_11_pdf_41934.pdf  a105-1170 v53 aThis paper presents a hybrid approach based on the Genetic Algorithm (GA) and Moth Swarm Algorithm (MSA), namely Genetic Moth Swarm Algorithm (GMSA), for determining the optimal location and sizing of renewable distributed generation (DG) sources on radial distribution networks (RDN). Minimizing the electrical power loss within the framework of system operation and under security constraints is the main objective of this study. In the proposed technique, the global search ability has been regulated by the incorporation of GA operations with adaptive mutation operator on the reconnaissance phase using genetic pathfinder moths. In addition, the selection of artificial light sources has been expanded over the swarm. The representation of individuals within the three phases of MSA has been modified in terms of quality and ratio. Elite individuals have been used to play different roles in order to reduce the design space and thus increase the exploitation ability. The developed GMSA has been applied on different scales of standard RDN of the (33 and 69-bus) power systems. Firstly, the most adequate buses for installing DGs are suggested using Voltage Stability Index (VSI). Then the proposed GMSA is applied to reduce real power generation, power loss, and total system cost, in addition, to improve the minimum bus voltage and the annual net saving by selecting the DGs size and their locations. Furthermore, GMSA is compared with other literature methods under several power system constraints and conditions, in single and multi-objective optimization space. The computational results prove the effectiveness and superiority of the GMSA with respect to power loss reduction and voltage profile enhancement using a minimum size of renewable DG units.  a1989-1660