Self-adaptive Firefly Algorithm Research Articles

Self-Adaptive Firefly-Algorithm-Based Unified Power Flow Controller Placement with Single Objectives

The selection of positions for unified power flow controller (UPFC) placement in transmission network is an essential factor, which aids in operating the system in a more reliable and secured manner. This paper focuses on strengthening the power system performance through UPFC placement employing self-adaptive firefly algorithm (SAFA), which selects the best positions along with parameters for UPFC placement. Three single objectives of real power loss reduction, voltage profile improvement, and voltage stability enhancement are considered in this work. IEEE 14, 30, and 57 test systems are selected to accomplish the simulations and to reveal the efficacy of the proposed SAFA approach; besides, solutions are compared with two other algorithms solutions of honey bee algorithm (HBA) and bacterial foraging algorithm (BFA). The proposed SAFA contributes real power loss reduction, voltage profile improvement, and voltage stability enhancement by optimally choosing the placement for UPFC.

Self-Adaptive Firefly Algorithm for solving Unit Commitment Problem in Power System

Unit Commitment is one of the crucial challenges in power system for selecting generation units to operate the system economically. It is the necessary to propose efficient solution techniques to solve unit commitment problem. In this research work a new Self-Adaptive Firefly Algorithm is planned to solve unit commitment problem. The SAFA identifies the best generating units to be functioning and offers the amount of load distribution among the units to minimize the operating cost. In order to validate SAFA an IEEE ten unit system is considered. The simulation is carried out using Matlab software and the solutions are presented. The solutions indicates that the efficiency of proposed SAFA than other solution techniques.

Self-Adaptive Optimization for Improved Data Sanitization and Restoration

Nowadays, Data Sanitization is considered as a highly demanded area for solving the issue of privacy preservation in Data mining. Data Sanitization, means that the sensitive rules given by the users with the specific modifications and then releases the modified database so that, the unauthorized users cannot access the sensitive rules. Promisingly, the confidentiality of data is ensured against the data mining methods. The ultimate goal of this paper is to build an effective sanitization algorithm for hiding the sensitive rules given by users/experts. Meanwhile, this paper concentrates on minimizing the four sanitization research challenges namely, rate of hiding failure, rate of Information loss, rate of false rule generation and degree of modification. Moreover, this paper proposes a heuristic optimization algorithm named Self-Adaptive Firefly (SAFF) algorithm to generate the small length key for data sanitization and also to adopt lossless data sanitization and restoration. The generated optimized key is used for both data sanitation as well as the data restoration process. The proposed SAFF-based algorithm is compared and examined against the other existing sanitizing algorithms like Fire Fly (FF), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution algorithm (DE) algorithms and the results have shown the excellent performance of proposed algorithm. The proposed algorithm is implemented in JAVA. The data set used are Chess, Retail, T10, and T40.

Self-Adaptive Firefly Algorithm with Pole Zero Cancellation Method for Controlling SMIB Power System

The accurate analysis and controller design for high-order systems are important issues. In this paper, a design of controller is suggested to Single Machine Infinite Bus (SMIB) System in which its order is reduced by the proposed self-adaptive Firefly Algorithm (FA). First, the dynamic adaption to the dominant parameters of the standard FA is proposed for overcoming its disadvantages and improving its ability in searching the global optimum solution with application to reduce the model order of SMIB. The parameters which are proposed to self-adaption are both of the light absorption based on the mean distance of fireflies’ positions and the step setting based on the fitness information to the status of preceding firefly and the current fireflies. Second, proportional–integral–derivative (PID) controller is suggested to be designed by pole zero cancellation method via the step response characteristics of the reduced-order SMIB to be applied to its high order. The efficiency of the proposed methods is tested on high-order SMIB to get its corresponding low order and to control it. The experimental results prove the efficacy of self-adaptive FA compared to the standard FA and the methods in the literature in terms of step response characteristics using error indices. The proposed methods assure the stability of the reduced order and the ability of the designed controller to handle both high and low-order model.

DNA fingerprinting of Marwari horses, an indigenous Indian breed

In this paper, a model-based optimization strategy for a liquid desiccant cooling and dehumidification (LDCD) system is proposed to improve system energy efficiency. The energy models of the LDCD system are established to predict system energy consumption under different operating conditions. To minimize the system energy consumption while maintaining the system thermal performance, the system energy consumption and thermal performance indicators are normalized by introducing a weight factor in cost function, then an optimization problem considering the interactions between components and system constraints is formulated. An improved self-adaptive firefly algorithm with fast convergence rate is proposed to solve the optimization problem and obtain the optimal set-points for control settings. Tests on an experimental apparatus are carried out to verify the energy saving potential of optimal control strategy under different weight factors and operating conditions. The results indicate that the energy consumption of LDCD system in the proposed optimization strategy is reduced by 12.49% over the conventional strategy. Meanwhile, the energy saving potential of the optimal control strategy is more remarkable for high cooling and dehumidification load. The proposed optimal control strategy can work well for applications in control and energy efficiency improvement of the existing dehumidification systems.

Model-based optimization strategy for a liquid desiccant cooling and dehumidification system

In this paper, a model-based optimization strategy for a liquid desiccant cooling and dehumidification (LDCD) system is proposed to improve system energy efficiency. The energy models of the LDCD system are established to predict system energy consumption under different operating conditions. To minimize the system energy consumption while maintaining the system thermal performance, the system energy consumption and thermal performance indicators are normalized by introducing a weight factor in cost function, then an optimization problem considering the interactions between components and system constraints is formulated. An improved self-adaptive firefly algorithm with fast convergence rate is proposed to solve the optimization problem and obtain the optimal set-points for control settings. Tests on an experimental apparatus are carried out to verify the energy saving potential of optimal control strategy under different weight factors and operating conditions. The results indicate that the energy consumption of LDCD system in the proposed optimization strategy is reduced by 12.49% over the conventional strategy. Meanwhile, the energy saving potential of the optimal control strategy is more remarkable for high cooling and dehumidification load. The proposed optimal control strategy can work well for applications in control and energy efficiency improvement of the existing dehumidification systems.

Self‐adaptive firefly algorithm based multi‐objectives for multi‐type FACTS placement

The efficient operation of the present day power system is an important issue to satisfy the customer needs. To improve the performance of the existing power system, the flexible alternating current transmission system (FACTS) devices have been attracted by an engineering community with the expertise in power system. This article proposes the self-adaptive firefly algorithm (SAFA) for placement of FACTS devices, which identifies the appropriate type, best possible locations and optimal parameters of FACTS devices. Static var compensator, thyristor controlled series compensator and unified power flow controller are considers as FACTS devices for their placement. The objectives are to improve the power system performance by placement of FACTS devices through minimising real power loss, improving voltage profile and enhancing the voltage stability. Effectiveness of the proposed SAFA is tested on standard IEEE 30 and IEEE 57 bus systems with different objectives. The results are compared with other approaches, which clearly indicate the effectiveness and usefulness of the proposed method.

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Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion

In this paper, we investigate feature subset selection problem by a new self-adaptive firefly algorithm (FA), which is denoted as DbFAFS. In classical FA, it uses constant control parameters to solve different problems, which results in the premature of FA and the fireflies to be trapped in local regions without potential ability to explore new search space. To conquer the drawbacks of FA, we introduce two novel parameter selection strategies involving the dynamical regulation of the light absorption coefficient and the randomization control parameter. Additionally, as an important issue of feature subset selection problem, the objective function has a great effect on the selection of features. In this paper, we propose a criterion based on mutual information, and the criterion can not only measure the correlation between two features selected by a firefly but also determine the emendation of features among the achieved feature subset. The proposed approach is compared with differential evolution, genetic algorithm, and two versions of particle swarm optimization algorithm on several benchmark datasets. The results demonstrate that the proposed DbFAFS is efficient and competitive in both classification accuracy and computational performance.

New memetic self-adaptive firefly algorithm for continuous optimisation

The firefly algorithm is a recent nature-inspired algorithm that is receiving increasing attention from the scientific community during the last few years. One of its most promising variants is given by the memetic self-adaptive firefly algorithm (MSA-FFA), recently introduced to solve combinatorial problems. In this paper we propose a modification of the original MSA-FFA for continuous optimisation problems. The most important features of our method are: the problem-dependent selection of control parameters for self-adaptation, a simple population model providing an adequate trade-off between exploration and exploitation, and the use of an adaptive-size Luus-Jaakola random local search. This new method is applied to solve a very difficult real-world continuous optimisation problem arising in geometric modelling and manufacturing. The paper also provides the first reliable, standardised benchmark for this optimisation problem. This benchmark is used for a comparative analysis of our method with respect to some of the most popular nature-inspired algorithms. Our results show that the proposed method outperforms previous approaches (including the standard firefly algorithm) for most of the instances in the benchmark.

New memetic self-adaptive firefly algorithm for continuous optimisation

The firefly algorithm is a recent nature-inspired algorithm that is receiving increasing attention from the scientific community during the last few years. One of its most promising variants is given by the memetic self-adaptive firefly algorithm MSA-FFA, recently introduced to solve combinatorial problems. In this paper we propose a modification of the original MSA-FFA for continuous optimisation problems. The most important features of our method are: the problem-dependent selection of control parameters for self-adaptation, a simple population model providing an adequate trade-off between exploration and exploitation, and the use of an adaptive-size Luus-Jaakola random local search. This new method is applied to solve a very difficult real-world continuous optimisation problem arising in geometric modelling and manufacturing. The paper also provides the first reliable, standardised benchmark for this optimisation problem. This benchmark is used for a comparative analysis of our method with respect to some of the most popular nature-inspired algorithms. Our results show that the proposed method outperforms previous approaches including the standard firefly algorithm for most of the instances in the benchmark.

Optimal placement of SVC for transmission loss minimisation using self-adaptive firefly algorithm

Transmission loss minimisation is an important research issue, which can be minimised by reactive power compensation. Static var compensator (SVC) is a popular flexible alternating current transmission system (FACTS) device for reactive power support in power system. A new strategy has been proposed for optimal placement of SVC in power systems with a view to minimise the transmission loss. The proposed approach uses a self-adaptive firefly algorithm (SAFA) and identifies the optimal location for SVC placement and their parameters. The SVC placement strategy is applied on IEEE 14, IEEE 30 and IEEE 57-bus systems and their results are presented to demonstrate its effectiveness.

Multi-type flexible AC transmission system device placement for voltage constrained loss minimisation using self-adaptive firefly algorithm

This paper presents a new strategy for optimally placing multi-type flexible AC transmission system (FACTS) devices in a power system network using self-adaptive firefl y algorithm. The main objective of this paper is to minimise the transmission loss besides keeping the bus voltage magnitudes within the acceptable range. Three different kinds of FACTS devices such as static var compensator, thyristor controlled series compensator and unified power flow controller are used in this study. The developed strategy determines the optimal location for installing the FACTS device, the type of FACTS device to be placed and their parameters. The results on three IEEE test systems are presented to show the effectiveness of the suggested strategy.

Optimal economic dispatch with valve loading effect using self-adaptive firefly algorithm

Economic dispatch (ED) problem exhibits highly nonlinear characteristics, such as prohibited operating zone, ramp rate limits, and non-smooth property. Due to its nonlinear characteristics, it is hard to achieve the expected solution by the classical methods. To overcome the challenging difficulty, this paper proposes an improved firefly algorithm (FA) to solve economic dispatch (ED) problem. The improved FA employs two strategies to enhance the search ability and avoid the premature usually suffered from in standard FA. The first one is based on the distance information among the fireflies, and it adjusts the light absorption coefficient adaptively. The other one is a decreasing strategy for the randomization parameter. Additionally, a crossover operation is employed to create potential solution with high diversity. The designs are able to enhance the search ability and performance of FA, which have been demonstrated on six benchmark functions. To validate the proposed algorithm, we also use three different systems to demonstrate its efficiency and feasibility in solving ED problem. The experimental results show that the proposed FA method was capable of achieving higher quality solutions in ED problems.

SVC Placement for Voltage Profile Enhancement Using Self-Adaptive Firefly Algorithm

SVC Placement for Voltage Profile Enhancement Using Self-Adaptive Firefly Algorithm

SVC Placement for Voltage Profile Enhancement Using Self-Adaptive Firefly Algorithm

Static VAR Compensator (SVC) is one of the shunt type FACTS devices for providing reactive power support in power systems network and its placement representing the location and size has significant influence on enhancement of voltage profile. This paper presents a firefly algorithm based optimization strategy for placement of SVC in power systems with a view of minimizing voltage deviation at the load buses to enhance the load bus voltages. This method uses a self-adaptive scheme for tuning the firefly parameters. The proposed strategy is tested on three IEEE test systems. The obtained results are promising and show the effectiveness of the proposed approach.

SVC placement for voltage constrained loss minimization using self-adaptive Firefly algorithm

Abstract Static Var Compensator (SVC) is a popular FACTS device for providing reactive power support in power systems and its placement representing the location and size has significant influence on network loss, while keeping the voltage magnitudes within the acceptable range. This paper presents a Firefly algorithm based optimization strategy for placement of SVC in power systems with a view of minimizing the transmission loss besides keeping the voltage magnitude within the acceptable range. The method uses a self-adaptive scheme for tuning the parameters in the Firefly algorithm. The strategy is tested on three IEEE test systems and their results are presented to demonstrate its effectiveness.

Optimal Placement of UPFC for Voltage Constrained Loss Minimization Using Self-Adaptive Firefly Algorithm

This paper presents the identification of optimal location and parameter of Unified Power Flow Controller (UPFC) in power system network. The objective is to minimize the transmission loss besides keeping the voltage magnitude within the acceptable range. Using proposed strategy, the location of UPFCs and their parameter are optimized simultaneously. Self Adaptive Firefly Algorithm (SAFA) is proposed to solve the above optimization problem for better accuracy. The UPFC placement strategy is tested on three IEEE test systems and their results are presented to demonstrate its effectiveness