Fireflyalgorithm (FA) is a newly proposed swarm intelligence optimization algorithm. Like many other general swarm intelligence optimization algorithms, the original version of FA is easy to trap into local optima. In order to overcome this drawback, the chaoticfireflyalgorithm (CFA) is proposed. The methods of chaos initialization, chaos population regeneration and linear decreasing inertia weight have been introduced into the original version of FA so as to increase its global search mobility for robust global optimization. The CFA is calculated in Matlab and is examined on six benchmark functions. In order to evaluate the engineering application of the algorithm, the reactivepower optimization problem in IEEE 30 bus system is solved by CFA. The outcomes show that the CFA has better performance compared to the original version of FA and PSO.
The optimal RPD is a constrained, nonlinear, discrete optimization problem. The traditional approach is to formulate this problem as a single- objective optimization problem with constraints. In this approach, the objective may consist of a single term [3-7] or it may consist of multiple terms [8, 9]. One may use dynamic programming or genetic algorithm to solve. However, this approach only results in a single optimal solution where tradeoffs between different components of the objective function must be fixed in advance of solution. In order to provide a means to assess tradeoffs between two conflicting objectives, one may formulate the RPD problem as a multi-objective problem. In this case, solution requires a multi-objective algorithm such as NSGA-II. Srinivas and Deb developed NSGA in which a ranking selection method emphasizes current non-dominated solutions and a niching method maintains diversity in the population . Multi-objective evolutionary algorithms including NSGA that use non-dominated sorting and sharing have been criticized mainly for i) computational complexity, ii) non-elitism approach and iii) the need for specifying a sharing parameter. Deb et al. alleviated these difficulties in NSGA-II [11, 12].
Reactivepower optimization plays a vital job in optimal operation of power systems. Many papers by various authors has been projected to solve the optimalreactivepowerdispatch (ORPD) problems such as, gradient based optimization algorithm [1,2], quadratic programming, non linear programming  and interior point method [4-7]. In recent years standard genetic algorithm (SGA)  and the adaptive genetic algorithm (AGA) , Partial swarm optimization PSO [10-11] have been applied for solving ORPD problems. The inability of the power system to meet the demand for reactivepower to preserve regular voltage profile in stressed situations acts very significant role in causing voltage collapse. In the past many innovative algorithms such as Evolutionary Algorithm [12-13], Genetic algorithm [14-15], Evolutionary strategies [16-18], Differential Evolution [19-20], Genetic programming  and Evolutionary programming  are used to solve many rigid problems in optimization. In this research paper Grand Salmon Run [23-24] is used to solve the ORPD Problem. This algorithm (GSR) is applied to obtain the optimal control variables so as to improve the voltage stability level of the system. The performance of the proposed method has been tested on IEEE 30 bus system and the results are compared with the standard GA and PSO method.
The proper selection of HS parameter values is considered as one of the challenging task not only for HS algorithm but also for other metaheuristic algorithms. This difficulty is a result of different reasons, and the most important one is the absence of general rules governing this aspect. Actually, setting these values is problem dependant and therefore the experimental trials are the only guide to the best values. However, this matter guides the research into new variants of HS. These variants are based on adding some extra components or concepts to make part of these parameters dynamically adapted. The proposed algorithm includes dynamic adaptation for both pitch adjustment rate (PAR) and bandwidth (bw) values. The PAR value is linearly increased in each iteration of HS by using the following equation:
while the one with highest score will win and output as the final solution. In the experiments, social emotional optimization algorithm (SEOA) has a remarkable superior performance in terms of accuracy and convergence speed [22-26]. In this research paper social emotional optimization algorithm has been utilized to solve the ORPD Problems. This algorithm (SEOA) is applied to obtain the optimal control variables so as to improve the voltage stability level of the system. The performance of the proposed method has been tested on IEEE 30 bus system and the results are compared with the standard GA and PSO method.
This research presents a Hybrid Particle Swarm Optimization with Bat Algorithm (HPSOBA) based approach to solve OptimalReactivePowerDispatch (ORPD) problem. The primary objective of this project is minimization of the active power transmission losses by optimally setting the control variables within their limits and at the same time making sure that the equality and inequality constraints are not violated. Particle Swarm Optimization (PSO) and Bat Algorithm (BA) algorithms which are nature-inspired algorithms have become potential options to solving very difficult optimization problems like ORPD. Although PSO requires high computational time, it converges quickly; while BA requires less computational time and has the ability of switching automatically from exploration to exploitation when the optimality is imminent. This research integrated the respective advantages of PSO and BA algorithms to form a hybrid tool denoted as HPSOBA algorithm. HPSOBA combines the fast convergence ability of PSO with the less computation time ability of BA algorithm to get a better optimal solution by incorporating the BA’s frequency into the PSO velocity equation in order to control the pace. The HPSOBA, PSO and BA algorithms were implemented using MATLAB programming language and tested on three (3) benchmark test functions (Griewank, Rastrigin and Schwefel) and on IEEE 30- and 118-bus test systems to solve for ORPD without DG unit. A modified IEEE 30-bus test system was further used to validate the proposed hybrid algorithm to solve for optimal placement of DG unit for active power transmission line loss minimization. By comparison, HPSOBA algorithm results proved to be superior to those of the PSO and BA methods.
Abstract— Development of Metaheuristic Algorithm in engineering problems grows really fast. This algorithm is commonly used in optimization problems. One of the metaheuristic algorithms is called FireflyAlgorithm (FA). FireflyAlgorithm is a nature-inspired algorithm that is derived from the characteristic of fireflies. FireflyAlgorithm can be used to solve optimalpower flow (OPF) problem in power system. To get the best performance, fireflyalgorithm can be combined with fuzzy logic. This research presents the application of hybrid fuzzy logic and fireflyalgorithm to solve optimalpower flow. The simulation is done using the MATLAB environment. The simulations show that by using the fuzzy-fireflyalgorithm, the power losses, as well as the total cost, can be reduced significantly.
genetic algorithms have been proposed to solve the reactivepower optimization problem . Genetic algorithm is a random search technique based on the mechanics of natural selection. But in the recent research some insufficiencies are distinguished in the GA performance. This abasement in efficiency is apparent in applications with highly hypostasis objective functions i.e. where the parameters being optimized are extremely correlated. In addition, the untimely convergence of GA degrades its performance and reduces its search capability. In addition to this, these algorithms are found to take more time to reach the optimal result.
This research presents a Teaching Learning Based Optimization (TLBO) algorithm to solve OptimalReactivePowerDispatch (ORPD) problem. The aim of the power system is to ensure safe and reliable power is delivered to consumers. Reactivepowerdispatch although contributes little or no cost in power systems, it is important in sustaining the voltages of the power system and ensuring efficiency of the transmission system and all electromagnetic equipment. Excess reactivepower in the power system can contribute to losses in the transmission grid. Therefore, reactivepower sources and sinks need to be provided to ensure balance. The primary objective of this paper is to minimize the active power transmission losses by the optimal settings of the control variables (generator set point voltages, tap changers on transformers and reactivepower shunt compensators) within their limits and avoiding violations on the constraints. TLBO is a population- based algorithm and requires few algorithm specifications to compute making it a recommended option to solve various degrees of optimization problems. The TLBO algorithm was implemented using MATLAB programming and by
In the fireflyalgorithm there are two important issues of the variation of light intensity and the formulation of the attractiveness. For simplicity, it is assumed that the attractiveness of a firefly is determined by its brightness which in turn is associated with the encoded objective function of the optimization problems. On the attractiveness of the FFA the main form of attractiveness function or β(r) can be any monotonically decreasing functions such as the following generalized form of
The results obtained with the proposed approach are presented and compared favorably with results of other approaches, like the Linear Programming (LP) method. All data used this analysis is taken from the Iraqi Operation and Control Office, which belongs to the ministry of electricity . An echolocation based algorithm known as the BAT search algorithm inspired by the behaviour of bats to optimalreactivepowerdispatch (ORPD) problem. The minimization of active power transmission losses through controlling a number of control variables is defined as the ORPD problem. The optimalreactivepowerdispatch is then developed as a non-linear optimization problem in regard to power transmission loss, voltage stability and voltage profile . A Genetic Algorithm (GA) - based approach for solving optimalReactivePowerDispatch (RPD) including voltage stability limit in power systems. The monitoring methodology for voltage stability is based on the L-index of load buses. Bus voltage magnitudes, transformer tap settings and reactivepower generation of capacitor banks are the control variables. A binary-coded GA with tournament selection, two point crossover and bit-wise mutation is used to solve this complex optimization problem.
, this paper formulates the reactivepowerdispatch as a multi-objective optimization problem with loss minimization and maximization of static voltage stability margin (SVSM) as the objectives. Voltage stability evaluation using modal analysis is used as the indicator of voltage stability. In recent years, several new optimization techniques have emerged. The evolutionary algorithms (EAs) for reactivepower optimization problem have been extensively studied. Several global optimization algorithms such as differential evolution (DE) (Dib.N et al .2010; Lin. C et al.2010), genetic algorithm (GA) (Zhang et al.2009 ; Vaitheeswaran.S et al.2008) , simulated annealing (SA) (Ferreira, J. A et al.1997 ), Ant colony optimization (ACO) (Hosseini.S. A et al 2008 ), particle swarm optimization (PSO) (Perez Lopez et al.2009; Liu, D et al.2009; Li, W.-T et al.2010 ; Goudos, S et al.2010 ; Shavit, R et al.2005) are used for reactivepower optimization problem. However, these methods present certain drawbacks with the possibility of premature convergence to a local optimum. In this paper, a novel chaotic PSO algorithm (CPSO) is proposed. Based on the ergodicity, regularity and pseudo-randomness of the Chaotic variable, chaotic search is used to explore better solutions. The performance of (CPSO) has been evaluated in standard IEEE 30 bus test system and the results analysis shows that our proposed approach outperforms all approaches investigated in this paper.
We consider a two-tier HSDPA cellular network with one macrocell base station (MBS) and a group of picocell base stations (PBSs) in an enterprise environment which share the same frequency channels. We assume the closed-access mode is used, where a UE will connect to a PBS only if it receives higher pilot power strength from its host PBS than from MBS; otherwise, the UE camps on MBSs.
In , OPF problem formulation has been presented along with considering effects of FACTS devices and power flow constraints. To solve OPF problem a two stage problem formulation has been proposed. This reference , Presents the performance comparison of PSO and GA optimization techniques for FACTS based controller design. In , the main aim is the optimal allocation of TCSC device in the power system so the power loss becomes minimized and also increase power transfer capacity of the transmission line that ultimately yields minimum operating cost with different load conditions. In this, first the locations of the TCSC device is identified by calculating line flows. TCSC is placed in line where reactivepower flows are very high. To solve OPF problem Hybrid GA incorporating TCSC is proposed. To select best control variables to minimize the generation cost and maintain the power flow within the control range Hybrid GA is integrated with conventional OPF .
Treating the bus voltage limits as constraints in ORPD often re- sults in all the voltages toward their maximum limits after optimi- zation, which means the power system lacks the required reserves to provide reactivepower during contingencies. One of the effec- tive ways to avoid this situation is to choose the minimization of the absolute deviations of all the actual bus voltages from their de- sired voltages as an objective function. Minimization of TVD of load buses can allow the improvement of voltage proﬁle  . In this case, the power system operates more securely and there will be an in- crease in service quality. This objective function may be formu- lated as follows
In this paper, a different approach, Enriched particle Swarm optimization (EPSO) Algorithm for solving optimalreactivepowerdispatch problem has been presented. Particle swarm optimization is affected by early convergence, no assurance in finding optimal solution. This paper proposes EPSO using multiple sub swarm PSO in blend with multi exploration space algorithm. The particles are alienated into equal parts and arrayed into the number of sub swarms available. Multi-exploration space algorithm is used to obtain an optimum solution for each sub swarm and these solutions are then arrayed yet into a new swarm to obtain the best of all the solution. The proposed EPSO algorithm has been tested on standard IEEE 30 bus test system and simulation results show the commendable performance of the proposed algorithm in reducing the real power loss.
The aim of this paper is to solve the optimalreactivepowerdispatch (ORPD) prob lem. Metaheuristic algorithms have b een extensively used to solve optimization problems in a reasonab le time without requiring in-depth knowledge of the treated prob lem. The perform ance of a metaheuristic requires a compromise b etween exploitation and exploration of the search space. However, it is rarely to have the two characteristics in the same search method, where the current emergence of hyb rid methods. This paper presents a hyb rid formulation b etween two different metaheuristics: differential evolution (b ased on a population of solution) and simulated annealing (b ased on a unique solution) to solve ORPD. The first one is characterized with the high capacity of exploration, whil e the second has a good exploitation of the search space. For the control variab les, a mixed representation (continuous/discrete), is proposed. The rob ustness of the method is tested on the IEEE 30 b us test system.
There are two general methods to optimize a function, namely, mathematical programming and Meta heuristic methods. Various mathematical programming methods such as linear programming, homogenous linear programming, integer programming, dynamic programming, and nonlinear programming have been applied for solving optimization problems. These methods use gradient information to search the solution space near an initial starting point. In general, gradient-based methods converge faster and can obtain solutions with higher accuracy compared to stochastic approaches in fulfilling the local search task. However, for effective implementation of these methods, the variables and cost function of the generators need to be continuous. Furthermore, a good starting point is vital for these methods to be executed successfully. In many optimization problems, prohibited zones, side limits, and non-smooth or non-convex cost functions need to be considered. As a result, these non-convex optimization problems cannot be solved by the traditional mathematical programming methods. Although dynamic programming or mixed integer nonlinear programming and their modifications offer some facility in solving non-convex problems, these methods, in general, require considerable computational effort. As an alternative to the conventional mathematical approaches, the meta-heuristic optimization techniques have been used to obtain global or near-global optimum solutions. Due to their capability of exploring and finding promising regions in the search space in an affordable time, these methods are quite suitable for global searches and furthermore alleviate the need for continuous cost functions and variables used for mathematical optimization methods. Though these are approximate methods, i.e., their solution are good, but not necessarily optimal, they do not require the derivatives of the objective function and constraints and employ probabilistic transition rules instead of deterministic ones . Nature has always been a major source of inspiration to engineers and natural philosophers and many meta-heuristic approaches are inspired by solutions that nature herself seems to have chosen for hard problems. The Evolutionary Algorithm (EA) proposed by Fogel et al. , De Jong  and Koza , and the Genetic Algorithm (GA) proposed by Holland  and Goldberg  are inspired from the biological evolutionary process. Studies on animal behavior led to the method of Tabu Search (TS) presented by Glover , Ant Colony Optimization (ACO) proposed by Dorigo et al.  and Particle Swarm Optimizer (PSO) formulated by Eberhart and Kennedy . Also, Simulated Annealing proposed by Kirkpatrick et al. , the Big Bang–Big Crunch algorithm (BB–BC)
Over the years numerous methods with various degrees of near-optimality, efficiency, ability to handle difficult constraints and heuristics, are suggested in the literature for solving the dispatch problems. These problems are traditionally solved using mathematical programming techniques such as lambda iteration method, gradient method, linear programming, dynamic programming method and so on. Many of these methods suffer from natural complexity and converge slowly. However, the classical lambda-iteration method has been in use for a long time. The additional constraints such as line flow limits cannot be included in the lambda iteration approach and the convergence of the iterations is dependent on the initial choice of lambda. In large power systems, this method has oscillatory problems that increase the computation time [1,2].