Papers by Keyword: Ant Colony Optimization (ACO)

Abstract: Emission dispatching is conducted to calculate the lowest amount of emission while generating satisfying output to the load demand. The utilities are restricted by emission regulation that limits the emission level to a certain amount. This paper proposes emission dispatch with multiple fuel option (EDMFO) to determine the optimal emission level. The EDMFO allows the operators to select different type of fuel according to the generation level and requirement. The emission dispatch problem is optimized by using Differential Evolution Immunized Ant Colony Optimization (DEIANT) technique. Validation process is conducted by comparing DEIANT with several optimization approaches including ACO and EP. The comparison took places in IEE 57-Bus RTS. Results indicate that DEIANT is superior in terms of calculating the lowest emission level, lower operating cost and the best selection of fuel according to the generation requirement.

Abstract: In the speed sensorless induction motor drives system, Model Reference Adaptive System (MRAS) is the most common strategy. It suffers from parameter sensitivity and flux pure integration problems which may cause DC drift. As a result, it leads to the deterioration of estimation at low speed. To overcome these problems, an Artificial Neural Networks (ANN) is presented as a Rotor Flux (RF) observer to replace the conventional voltage model used in RF-MRAS speed observer. Simultaneously, in order to solve the trap of local minimum value of algorithm, and enhance the ANN convergence speed, we used the modified Ant Colony Optimization (ACO) to optimize the weights and thresholds value of neural networks. The results of the simulation show that, by this method, the speed of motor can be identified accurately in different situations, and the result is reliable.

Abstract: Nodes in wireless sensor network have limited power supply and wireless channels between them are sensitive to interference. In order to make good use of the limited energy, a routing algorithm is proposed which uses the Ant Colony Optimization Algorithm to balance the load of the network and extend the network life, the proposed algorithm utilizes the dynamic adaptability and optimization capabilities of the ant colony to get the optimum route between the cluster heads．Simulation results show the feasibility and effectiveness of this algorithm．

Abstract: In the field of information technology, data clustering algorithms are widely used. In this paper, we proposed a new data clustering algorithm, named MADS, It is based on ant colony Optimization. MADS can automatically find clusters, depending on a few parameters that are not directly related to the data set. In addition, there are some existence technique was also utilized in our method, such as the density concept and cluster validity index (DB-index). The experiment results verified that MADS is able to discover clusters with varying shapes and is effective when applied to image segmentation.

Abstract: China Post Logistics have been introduced the Data Picking System (DPS) with applied technology for its warehouse in order to meet the increasing requirement, but until now some flaws have been turned up such as the unreasonable path scheming. This paper analyzes the Data Picking System with applied technology of China Post Logistics, and then optimizes the order picking route in view of the current deficiency and combined with the use of Ant Colony Optimization (ACO). Our work is one of the first to introduce the China Post Logistics’ Data Picking System and optimize the order picking route with ACO, and the result shows that this study is effective.The example simulation result shows that this optimizing is effective and the order picking route decrease 8.34% than that as before.

Abstract: This paper proposed an improved ant colony optimization(ACO), to solve the economical operating dispatch of automatic generation control(AGC) units in hydropower station. The improved ant colony algorithm PSO-ACO imported particle swarm optimization is put forward. Both of the global convergence performance and the effectiveness of this algorithm is improved by using self-adaptive parameters and importing PSO to optimize the current ant paths. The mathematical description and procedure of the PSO-ACO are given with the maximum plant generating efficiency model as an example. Finally the superiority of the PSO-ACO is demonstrated by the application of AGC units on right bank of Three Gorges hydropower station. The optimal solution is more accurate and the calculation speed is higher than other methods.

Abstract: Nowadays Integrated Bridge Systems are applied on board a ship to increase safety of navigation. These systems consist of many electronic devices such as radar, ECDIS and autopilot, which aid the deck officer in the process of conducting navigation. Despite that, ship accidents caused by human error still occur. The paper presents new method of safe ship control in collision situations. Ant Colony Optimization is applied to determine safe ship trajectory. Developed algorithm is applicable for situations in restricted waters, where most of collision situations occur. International Regulations for Preventing Collisions at Sea (COLREGs) are taken into consideration in the process of solution construction. The task of collision avoidance at sea is defined as dynamic optimization problem with the use of static and dynamic constraints. Static constraints are represented by lands, canals, shallows, fairways, while other ships constitute dynamic constraints. Described method was implemented in MATLAB programming language. Performed simulation tests results of encounter situations with one target ship as well as with many target vessels are presented. Received solutions confirm successful application of this method to the problem of ships collisions avoidance. Developed algorithm deals also with more complex situations. This new algorithm is planned to be implemented in anti-collision decision support system on board a ship, what would contribute to enhance safety of maritime transport.

Abstract: In this paper, an ant colony optimization based method (AM) is proposed for gene selection. AM consists of two stages. In the first stage, some redundant genes are filtered by information gain (IG). In the second stage, a fuzzy adaptive ant colony optimization is applied to gene selection. We evaluate the performance of AM on five gene expression datasets, which have dimensions varying from 7129 to 12000. We also compare the performance of AM with the results obtained from four existing well-known optimization algorithms. The comparison details show that AM could get better classification accuracy.

Abstract: Ant colony algorithm is an effective algorithm to solve combinatorial optimization problems, it has many good features, and there are also some disadvantages. In this paper, through research on ant colony optimization algorithm, apply it in intrusion detection. Then it gives an improved ant colony optimization algorithm. Tests show that the algorithm improves the efficiency of intrusion detection, reduces false positives of intrusion detection.

Abstract: Ant Colony Optimization (ACO) is based on swarm intelligence and it is a constructive meta-heuristic which was first presented in 1991. Job Shop Scheduling Problem (JSSP) is very important problem of the manufacturing industry. JSSP is a combinatorial optimization problem which is NP-hard. The exact solution of NP-hard problem is very difficult to find. Therefore heuristics approach is the best approach for such problems. This paper shall overview the application of ant colony optimization on JSSP and Flexible Job Shop Scheduling problems (FJSSP). This paper shalll cover the major areas in which researchers have worked and it shall also recommend the future area of research in the light of this overview. This paper will also cover the quantitative analysis of the research papers which are considered in this survey. Based upon this survey some conclusions are drawn in the end.The significance of this paper is that it has covered all the efforts and major researches in the area of ACO application on JSSP and FJSSP through the inception of ACO metaheuristics. This enables the researchers and scheduling experts to overview chronologically the development of ACO on JSSP and FJSSP.