In this chapter, enhanced symbiotic organisms search (ESOS) algorithm and hydrological cycle (HC) algorithm are projected to solve factual power loss lessening problem. Symbiotic search algorithm is based on the actions between two different organisms in the ecosystem: mutualism, commensalism, and parasitism. Exploration procedure has been initiated arbitrarily, and each organism indicates a solution with fitness value. Quasi-oppositional-based learning and chaotic local search have been applied to augment the performance of the algorithm. In this work, hydrological cycle (HC) algorithm has been utilized to solve the optimal reactive power problem. It imitates the circulation of water form land to sky and vice versa. Only definite number of water droplets is chosen for evaporation, and it is done through roulette-wheel selection method. In the condensation stage, water drops move closer, combine, and also collusion occurs as the temperature decreases.
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Factual power loss decreasing is foremost objective of the paper. Several orthodox methods and Evolutionary algorithms (Azar & Vaidyanathan, 2015a,b,c, 2016; Azar & Zhu, 2015; Azar & Serrano, 2015a,b,c,d, 2016a,b, 2017; Boulkroune et al, 2016a,b; Ghoudelbourk et al., 2016; Meghni et al, 2017a,b,c; Azar et al., 2017a,b,c,d; Azar 2010a,b, 2012; Mekki et al., 2015; Vaidyanathan & Azar, 2015a,b,c,d, 2016a,b,c,d,e,f,g, 2017a,b,c; Zhu & Azar, 2015; Grassi et al., 2017; Ouannas et al., 2016a,b, 2017a,b,c,d,e,f,g,h,i,j; Singh et al., 2017; Vaidyanathan et al, 2015a,b,c; Wang et al., 2017; Soliman et al., 2017; Tolba et al., 2017).are employed to solve the problem. Major problem is handling the constraints and balancing the exploration, exploitation equally. In this chapter First Enhanced Symbiotic Organisms Search (ESOS) Algorithm is applied to solve the problem. Symbiotic organisms search algorithm is based on the actions between two dissimilar organisms in the ecosystem- mutualism, commensalism and parasitism. In the projected Enhanced Symbiotic Organisms Search (ESOS) Algorithm, Quasi-oppositional based learning and Chaotic local search has been applied to augment the performance of the algorithm. Quasi-oppositional based learning and quasi-opposite numbers is very much successful in discovery of global optimal solution. Chaotic local search has been applied to explore close by present solutions to engender a new-fangled candidate solution. This work presents Hydrological Cycle (HC) algorithm for solving optimal reactive power dispatch problem. It imitates the circulation of water form land to sky and vice versa. Through the process of evaporation, precipitation, condensation water attains different conditions. Projected HC algorithm explores the space, water will move from one point to another point by choosing various nodes. In this HC algorithm quality of the solution depend on quantity of the soil carried by each water drop and a good solution is obtained from the water drop which carries more soil, also the quantity of the soil carried by water drop is utilized to modernize the velocity of the water drops subsequently. Only definite number of water droplets is chosen for evaporation and it done through Roulette-wheel selection method. In the condensation stage water drops are move closer, combine and also collusion will occur as the temperature decreases. ESOS and HC algorithms are corroborated in IEEE 30 bus system. Then ESOS and HC algorithms have been corroborated in IEEE 14, 30, 57 and300 bus test systems deprived of voltage stability index. Projected ESOS and HC algorithms abridged the power loss meritoriously and variables are inside the restrictions.