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In this paper, a novel approach Modified Monkey optimization (MMO) IntroductionReactive power optimization plays a key role in optimal operation of power systems. Many numerical methods [1][2][3][4][5][6][7] have been applied to solve the optimal reactive power dispatch problem. The problem of voltage stability plays a strategic role in power system planning and operation [8]. So many Evolutionary algorithms have been already proposed to solve the reactive power flow problem [9][10][11]. In [12,13], Hybrid differential evolution algorithm and Biogeography Based algorithm has been projected to solve the reactive power dispatch problem. In [14,15], a fuzzy based technique and improved evolutionary programming has been applied to solve the optimal reactive power dispatch problem. In [16,17] nonlinear interior point method and pattern based algorithm has been used to solve the reactive power problem. In [18][19][20], various types of probabilistic algorithms utilized to solve optimal reactive power problem. This paper introduces a novel Modified Monkey optimization for solving optimal reactive power dispatch power problem. Monkey Optimization algorithm [21] is fresh entry in class of swarm intelligence. This Monkey Optimization algorithm is enthused by fission fusion social structure based on foraging behaviour of monkeys when searching for quality food source and for mating. Alike to any other population based optimization techniques, artificial bee colony (ABC) consists of a population of intrinsic solutions. The intrinsic solutions are food sources of honey bees. The fitness is decided in terms of the quality of the food source that is nectar amount. Artificial bee colony is comparatively a direct, quick and population based stochastic exploration technique in the field of nature inspired algorithms. Monkey Optimization algorithm is also alike to ABC in nature. There are two fundamental processes which drive the swarm to modernize in ABC: the deviation process, which empowers exploring different fields of the exploration space, and the selection process, which guarantees the exploitation of the preceding experience. However, it has been shown that the ABC may infrequently stop moving toward the global optimum even though the population has not meeting to a local optimum [22]. It can be observed that the solution search equation of ABC algorithm is good at exploration but poor at exploitation [23]. Therefore, to uphold the good equilibrium between exploration and exploitation behaviour of ABC, it is extremely expected to develop a local exploration method in the basic ABC to strengthen the exploration region. The proposed MMO algorithm has been evaluated in standard IEEE 30 bus test system & the simulation results show that our proposed approach outperforms all reported algorithms in minimization of real power loss.

This paper presents a new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA) for solving the multi-objective reactive power dispatch problem. Wolf Search algorithm is a new bio -inspired heuristic algorithm which based on wolf preying behaviour. The way wolves search for food and survive by avoiding their enemies has been imitated to formulate the algorithm for solving the reactive power dispatches. And the speciality of wolf is possessing both individual local searching ability and autonomous flocking movement and this special property has been utilized to formulate the search algorithm. The proposed (WSA) algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the good performance of the proposed algorithm.Keywords: modal analysis, optimal reactive power, transmission loss, wolf search algorithmWhere,J is called the reduced Jacobian matrix of the system.

The paper presents an (ACSA) Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents’ approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical) test bus system. The proposed approach is tested and compared to genetic algorithm (GA), Adaptive Genetic Algorithm (AGA)

This paper presents Improved Great Deluge Algorithm (IGDA) for solving the multi-objective reactive power dispatch problem. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Like other local search approaches, this approach also replaces common solution (New_Config) with best results (Best_Config) that have been found by then. This action continues until stop conditions is provided. In this algorithm, new solutions are selected from neighbours. Selection strategy is different from other approaches. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms reported those before in literature. Results show that IGDA is more efficient than others for solution of single-objective ORPD problem.  Index Terms-modal analysis, optimal reactive power, transmission loss, improved great deluge algorithm (IGDA), optimization

This paper presents, an Adaptive Cat Swarm Optimization (ACSO) for solving reactive power dispatch problem. Cat Swarm Optimization (CSO) is one of the new-fangled swarm intelligence algorithms for finding the most excellent global solution. Because of complication, sometimes conventional CSO takes a lengthy time to converge and cannot attain the precise solution. For solving reactive power dispatch problem and to improve the convergence accuracy level, we propose a new adaptive CSO namely 'Adaptive Cat Swarm Optimization' (ACSO). First, we take account of a new-fangled adaptive inertia weight to velocity equation and then employ an adaptive acceleration coefficient. Second, by utilizing the information of two previous or next dimensions and applying a new-fangled factor, we attain to a new position update equation composing the average of position and velocity information. TheprojectedACSO has been tested on standard IEEE 57 bus test system and simulation results shows clearly about the highquality performance of the plannedalgorithm in tumbling the real power loss.

This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. This paper introduces, an evolutionary algorithm based on the hybrid genetic algorithm (GA) and particle swarm optimization (PSO), denoted by HGAPSO is used to solve reactive power dispatch problem. 