Ant colony search algorithm for optimal reactive power optimization

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)

OPTIMAL DESIGN PSS-PID CONTROL ON SINGLE MACHINE INFINITE BUS USING ANT COLONY OPTIMIZATION

Optimization of the controller in a generator can improve system performance. The right parameter optimization is needed to get the optimal performance from the controller. The application of the artificial intelligence method as a parameter optimization method is proposed in this study. By using the smart method based on Ant Colony, the optimal PSS-PID parameters are obtained. With optimal tuning, the system gets optimal Single Machine Infinite Bus (SMIB) system frequency and rotor angle response, indicated by the minimum overshot system response. The SMIB system's stability will be tested. A case study of adding and reducing loads is used, with the proposed control method PSS-PID being optimized using Ant Colony. Based on the analysis using the proposed PSS-PID control, we get the minimum overshoot for the frequency response and rotor angle of the SMIB system. When the load changes at 20 seconds, using the PSS-PID control scheme, the minimum overshoot is -4.316e-06 to 7.598e-05 pu with a settling time of 22.01s. For the rotor angle overshoot response, using the PSS-PID control scheme, the minimum overshoot is -0.01113 to -0.009669 pu with a settling time of 22.36s

An ant colony based model to optimize parameters in industrial vision

Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful

Multiobjective multicast routing with Ant Colony Optimization

This work presents a multiobjective algorithm for multicast traffic engineering. The proposed algorithm is a new version of MultiObjective Ant Colony System (MOACS), based on Ant Colony Optimization (ACO). The proposed MOACS simultaneously optimizes the maximum link utilization, the cost of the multicast tree, the averages delay and the maximum endtoend delay. In this way, a set of optimal solutions, known as Pareto set is calculated in only one run of the algorithm, without a priori restrictions. Experimental results obtained with the proposed MOACS were compared to a recently published Multiobjective Multicast Algorithm (MMA), showing a promising performance advantage for multicast traffic engineering.5th IFIP International Conference on Network Control & Engineering for QoS, Security and MobilityRed de Universidades con Carreras en Informática (RedUNCI

DESIGN OF OPTIMAL PID CONTROLLER FOR THREE PHASE INDUCTION MOTOR BASED ON ANT COLONY OPTIMIZATION

Speed control of an induction motor is an important part of the operation of an induction motor. One method of regulating motor speed is the addition of a PID controller. PID parameters must be tuned properly to get the optimal speed. In this study, the PID controller tuning method uses an artificial intelligence method based on Ant Colony Optimization (ACO). ACO algorithm in an intelligent algorithm that is inspired by the behavior of ants looking for food sources in groups with traces of feromone left behind. In this study, food sources are represented as optimal parameters of PID. From the computational results obtained optimal parameters respectively, P (Proportional) 0.5359, I (Integral) 0.1173, D (Derivative) 0.0427. ACO computing found the optimal parameters in the 21st iteration with a minimum fitness function of 11.8914. Case studies are used with two variations of the speed of the induction motor input. With optimal tuning, the performance of the induction motor is increasing, marked by a minimum overshoot of 1.08 pu and a speed variation of both overshoots of 1,201 pu, whereas without control 1.49 pu and 1.28 pu, as well as with PID trial control of 1.22 pu and 1.23 pu respectively. The benefits of this research can be used as a reference for the operation of induction motors, by tuning the Ant Colony intelligent method for the PID controller in real-time with the addition of microcontroller components

Optimisasi Koloni Semut dan Sistem Fuzzy untuk Kendali Lampu Lalu Lintas Pintar

Pola pengaturan lampu lalu lintas waktu tetap yang tidak mempertimbangkan kondisi aktual persimpangan bisa menimbulkan kemacetan. Kemacetan dapat menyebabkan banyak kerugian, diantaranya yaitu banyaknya waktu terbuang dan bahan bakar yang habis dengan sia- sia.  Masalah ini dapat diatasi dengan  pengatur lampu lalu lintas pintar yaitu sebuah sistem pengatur lampu lalu lintas yang mampu beradaptasi dengan kondisi setiap ruas jalan pada persimpangan. Pada penelitian ini telah dilakukan pengembangan sistem pengatur lampu lalu lintas pintar berbasis pada logika fuzzy bertingkat dan algoritma optimisasi koloni semut (Ant Colony Optimization). Pada Logika Fuzzy Bertingkat, keluaran dari sistem logika fuzzy tahap pertama menjadi masukan ke sistem logika fuzzy tahap berikutnya. Keluaran dari Sistem Fuzzy adalah menentukan skala prioritas untuk fase hijau berikutnya. Selanjutnya algoritma optimisasi koloni semut melakukan perhitungan waktu hijau yang optimal pada fase tersebut. Berdasarkan hasil simulasi yang dilakukan diperoleh bahwa dengan menggunakan sistem pengatur lampu lalu lintas pintar dibanding dengan sistem pengatur lampu lalu lintas waktu tetap terjadi pengurangan panjang antrian kendaraan dan waktu tunggu kendaraan.Fixed time traffic light control is a traffic light control system that does not take into account the actual conditions of the intersection, which can cause congestion. Congestion can cause a lot of losses, including a lot of wasted time and wasted fuel. This problem can be solved with a smart traffic light controller, which is a traffic light control system that is able to adapt to the conditions of each road section at the intersection. In this research, the development of smart traffic light control based on multi stage fuzzy logic and ant colony optimization (ACO) algorithm has been carried out. In multi stage fuzzy logic, the output of the first stage of the fuzzy logic becomes the input to the next stage of the fuzzy logic. The output of the fuzzy system is to determine the priority scale for the next green phase. Furthermore, Ant Colony Optimization calculates the optimal green time in that phase. Based on the simulation result, it is found that by using  smart traffic light control system compared to a fixed time traffic light control system, there  is a reduction in queue length and waiting time

New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder-Mead algorithm

[EN] In this paper, an optimal gain tuning method for PID controllers is proposed using a novel combination of a simplified Ant Colony Optimization algorithm and Nelder¿Mead method (ACO-NM) including a new procedure to constrain NM. To address Proportional-Integral-Derivative (PID) controller tuning for the Automatic Voltage Regulator (AVR) system, this paper presents a meta-analysis of the literature on PID parameter sets solving the AVR problem. The investigation confirms that the proposed ACO-NM obtains better or equivalent PID solutions and exhibits higher computational efficiency than previously published methods. The proposed ACO-NM application is extended to realistic conditions by considering robustness to AVR process parameters, control signal saturation and noisy measurements as well as tuning a two-degree-of-freedom PID controller (2DOF-PID). For this type of PID, a new objective function is also proposed to manage control signal constraints. Finally, real time control experiments confirm the performance of the proposed 2DOF-PIDs in quasi-real conditions. Furthermore, the efficiency of the algorithm is confirmed by comparing its results to other optimization algorithms and NM combinations using benchmark functions.This work was supported by the Vanier Canada Graduate Scholarship, the Michael Smith Foreign Study Supplements Program from the Natural Sciences and Engineering Research Council of Canada and by the Ministerio de Economia y Competitividad (Spain), project DPI2015-71443-R. It was also supported by the Bourse Mobilite Etudiante from Ministere de l'Education du Quebec, the CEMF Claudette MacKay-Lassonde Graduate Engineering Ambassador Award and the SWAAC Bourseau merite pour etudiantes de cycles superieurs.Blondin, MJ.; Sanchís Saez, J.; Sicard, P.; Herrero Durá, JM. (2018). New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder-Mead algorithm. Applied Soft Computing. 62:216-229. https://doi.org/10.1016/j.asoc.2017.10.007S2162296

Scheme of Overloaded Truck Control on a Rural Highway

A new working mode of overloaded traffic control for rural highways is presented, and a location-routing model is built to optimize the check base distribution and the control vehicles’ routing schemes. Then, for the location-routing model with a large set of location alternatives and an unknown settable number of check bases, a multiple ant colony optimization algorithm is designed to solve the model. Furthermore, actual data from Guiyang rural highways are used to perform a numerical analysis. The results indicate that the model can be used to obtain the optimal base location-vehicle routing scheme to verify the feasibility of the model and the algorithm. The model and algorithm can help managers to make decisions on locating the check bases and routing the control vehicles