Support vector machine with a firefly optimization algorithm for classification of apple fruit disease

Fruit diseases became one of the serious problems that the farmer faced because it could threaten their economic outcome. The main focus of this study is apples. Apple fruit is very susceptible to disease, in general diseases that usually attack the apple are blotch apple, rot apple, and scab apple. In this study, the author is classifying these three apple diseases and normal apples. Classification is a process that we can do manually by human power, which costs a lot of fortune, takes a long time, and it's also very vulnerable to false identification. This study takes advantage of computer vision technology and machine learning to overcome the classification problem. By using the SVM method and parameter FA optimization algorithm, we can get the highest result only in the first experiment and also with 94% accuracy

A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm

Many hard optimization problems have been efficiently solved by two notable swarm intelligence algorithms, artificial bee colony (ABC) and firefly algorithm (FA). In this paper, a collaborative hybrid algorithm based on firefly and multi-strategy artificial bee colony, abbreviated as FA-MABC, is proposed for solving single-objective optimization problems. In the proposed algorithm, FA investigates the search space globally to locate favorable regions of convergence. A novel multi-strategy ABC is employed to perform local search. The proposed algorithm incorporates a diversity measure to help in the switch criteria. The FA-MABC is tested on 40 benchmark functions with diverse complexities. Comparative results with the basic FA, ABC and other recent state-of-the-art metaheuristic algorithms demonstrate the competitive performance of the FA-MABC

Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm

In this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in order to predict the electrical parameters of three different solar cell technologies. The first is a commercial RTC mono-crystalline silicon solar cell with single and double diodes at 33 °C and 1000 W/m2. The second, is a flexible hydrogenated amorphous silicon a-Si:H solar cell single diode. The third is a commercial photovoltaic module (Photowatt-PWP 201) in which 36 polycrystalline silicon cells are connected in series, single diode, at 25 °C and 1000 W/m2 from experimental current-voltage. The proposed constrained objective function is adapted to minimize the absolute errors between experimental and predicted values of voltage and current in two zones. Finally, for performance validation, the parameters obtained through the Firefly algorithm are compared with recent research papers reporting metaheuristic optimization algorithms and analytical methods. The presented results confirm the validity and reliability of the Firefly algorithm in extracting the optimal parameters of the photovoltaic solar cell

Generalized Firefly Algorithm for optimal transmit beamforming

This paper proposes a generalized Firefly Algorithm (FA) to solve an optimization framework having objective function and constraints as multivariate functions of independent optimization variables. Four representative examples of how the proposed generalized FA can be adopted to solve downlink beamforming problems are shown for a classic transmit beamforming, cognitive beamforming, reconfigurable-intelligent-surfaces-aided (RIS-aided) transmit beamforming, and RIS-aided wireless power transfer (WPT). Complexity analyzes indicate that in large-antenna regimes the proposed FA approaches require less computational complexity than their corresponding interior point methods (IPMs) do, yet demand a higher complexity than the iterative and the successive convex approximation (SCA) approaches do. Simulation results reveal that the proposed FA attains the same global optimal solution as that of the IPM for an optimization problem in cognitive beamforming. On the other hand, the proposed FA approaches outperform the iterative, IPM and SCA in terms of obtaining better solution for optimization problems, respectively, for a classic transmit beamforming, RIS-aided transmit beamforming and RIS-aided WPT

Generalized Firefly Algorithm for Optimal Transmit Beamforming

This paper proposes a generalized Firefly Algorithm (FA) to solve an optimization framework having objective function and constraints as multivariate functions of independent optimization variables. Four representative examples of how the proposed generalized FA can be adopted to solve downlink beamforming problems are shown for a classic transmit beamforming, cognitive beamforming, reconfigurable-intelligent-surfaces-aided (RIS-aided) transmit beamforming, and RIS-aided wireless power transfer (WPT). Complexity analyzes indicate that in large-antenna regimes the proposed FA approaches require less computational complexity than their corresponding interior point methods (IPMs) do, yet demand a higher complexity than the iterative and the successive convex approximation (SCA) approaches do. Simulation results reveal that the proposed FA attains the same global optimal solution as that of the IPM for an optimization problem in cognitive beamforming. On the other hand, the proposed FA approaches outperform the iterative, IPM and SCA in terms of obtaining better solution for optimization problems, respectively, for a classic transmit beamforming, RIS-aided transmit beamforming and RIS-aided WPT

Optimal placement of statcom controllers with metaheuristic algorithms for network power loss reduction and voltage profile deviation minimization.

Masters Degree. University of KwaZulu-Natal, Durban.Transmission system is a series of interconnected lines that enable the bulk movement of electrical power from a generating station to an electrical substation. This system suffers from unavoidable power losses and consequently voltage profile deviation which affects the overall efficiency of the system; hence the need to reduce these losses and voltage magnitude deviations. The existing methods of incorporation of static synchronous compensator (STATCOM) controllers to solve these problems suffer from incorrect location and sizing, which could bring about insignificant reduction in transmission network losses and voltage magnitude deviations. Hence, this research aims to reduce transmission network losses and voltage magnitude deviation in transmission network by suitable allocation of STATCOM controller using firefly algorithm (FA) and particle swarm optimization (PSO). A mathematical steady-state STATCOM power injection model was formulated from one voltage source representation to generate new set of equations, which was incorporated into the Newton-Raphson (NR) load flow solution algorithm and then optimized using PSO and FA. The approach was applied to IEEE 14-bus network and simulations were performed using MATLAB program. The results showed that the best STATCOM controller locations in the system after optimization were at bus 11 and 9 with the injection of shunt reactive power of 8.96 MVAr, and 9.54 MVAr with PSO and FA, respectively. The total active power loss for the network under consideration at steady state, with STATCOM only and STATCOM controller optimized using PSO and FA, were 6.251 MW, 6.075 MW, 5.819 MW and 5.581 MW, respectively. The corresponding reactive power were 14.256 MVAr, 13.857 MVAr, 12.954 MVAr and 12.156 MVAr, respectively. In addition, bus voltage profile improvement indicates the effectiveness of metaheuristic methods of STATCOM optimization. However, FA gave a better power loss and voltage magnitude deviations minimizations over PSO. The study concluded that FA is more effective as an optimization technique for suitably locating and sizing of STATCOM controller on a power transmission system.Publications listed on page iii