An improved ant colony optimization algorithm and its application to electromagnetic devices designs

Nodes in the Wireless sensor network (WSN) have the limited power, memory and battery capacity. In this work, we propose an efficient routing algorithm called Cluster Based Wireless Sensor Network using Ant Colony Optimization (CBACO). The Cluster Heads (CH) are selected based on the cost derived from node parameters remaining energy, number of neighbours and distance to base station. The weighted average method is used to compute cost. The routing processes are established in two levels. The cluster member to cluster head data transmission is handled at level one and in the second level path finding process between cluster head to base station handled by Ant Colony Optimization (ACO) method which is the biologically inspired optimization technique. All the cluster head nodes are participate in the second level inter-cluster routing operation. The performance of the CBACO algorithm in terms of delay is minimized by using the clustering method and ACO method. The efficiency of the proposed algorithm is analysed by compare with existing routing algorithm which uses LEACH and ACO method. The results indicate that our proposed work achieves low energy consumption and high throughput.

Water delivery scheduling was investigated in this study using fuzzy state, action, reward, state, action (SARSA) learning (FSL) and ant colony optimization (ACO) methods to find the advantages of a new robust model (FSL) over a conventional model (ACO) in both normal and emergency conditions. The mathematical models of these methods were developed. Three water shortages of 10%, 20%, and 30% were considered in the East Aghili canal, Iran, for the simulation process. Water depth and delivery indicators were used for evaluating the performance of the developed models. The results revealed that the FSL and ACO methods offered almost the same performance for the normal operation condition with high and acceptable indicators. However, the FSL method outperformed the ACO method in terms of performance in three considered emergency operations. It can be concluded that the FSL, as a new method, can schedule water delivery efficiently, adequately, equitably, and dependably. Furthermore, the FSL method is likely to lead to less maximum absolute error (MAE) and integral of absolute magnitude of Error (IAE) in comparison to the ACO method and is therefore recommended.

Ant Colony Optimization is the optimization method used for the analysis of the cluster. This method performs clustering data well. This method has limitations on computation time. This paper proposed a method of pattern reduction enhanced ant colony optimization to resolve the problem. Functions reduction pattern used to bypass loop system. This function ensures pheromone value if it meets certain iteration, so the next ant agent definitely choose the path that has been determined. The test is done through testing parameters and test accuracy. accuracy test compared with ant colony optimization method(ACO) with the results of the proposed method has the advantage of computing time. The test results show that the proposed method has 46 second faster than ACO method (73 second) for iris dataset, 141 second faster than ACO method (272 second) for wine dataset, and 150 second faster than ACO method (204 second) for synthetic dataset.

Inverse transient radiation analysis in one-dimensional participating slab using improved Ant Colony Optimization algorithms

Ant colony optimization (ACO) methods, which imitate a mechanism of pheromone secretion when ants carry food to their nest, are one of efficient heuristic search methods for combinational optimization problems such as traveling salesman problems (TSPs) and so on. In this paper, we analyze the queen ant strategy AS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> that is one of ACO methods more in detail by applying it to six kinds of city configurations included in the TSPLIB. Furthermore, in order to improve searching ability of the AS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> , we propose a new method named "stimulative queen ant strategy AS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> ". As experimental results, we have clarified that the AS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> shows better performance than the conventional AS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">queen</sub> in the viewpoint of both "discovery rate of optimal solution" and "average number of iterations"

A simulation study was carried out to develop an alternative method of selecting animals to be genotyped. Simulated pedigrees included 5000 animals, each assigned genotypes for a bi-allelic single nucleotide polymorphism (SNP) based on assumed allelic frequencies of 0.7/0.3 and 0.5/0.5. In addition to simulated pedigrees, two beef cattle pedigrees, one from field data and the other from a research population, were used to test selected methods using simulated genotypes. The proposed method of ant colony optimization (ACO) was evaluated based on the number of alleles correctly assigned to ungenotyped animals (AK(P)), the probability of assigning true alleles (AK(G)) and the probability of correctly assigning genotypes (APTG). The proposed animal selection method of ant colony optimization was compared to selection using the diagonal elements of the inverse of the relationship matrix (A(-1)). Comparisons of these two methods showed that ACO yielded an increase in AK(P) ranging from 4.98% to 5.16% and an increase in APTG from 1.6% to 1.8% using simulated pedigrees. Gains in field data and research pedigrees were slightly lower. These results suggest that ACO can provide a better genotyping strategy, when compared to A(-1), with different pedigree sizes and structures.

Energy sources that are often used to generate electricity are non-renewable energy sources which, if used continuously, will run out, such as petroleum, natural gas and coal. So, renewable energy sources are needed which are in abundant supply and do not run out quickly, one of which is water energy. Picohydro power plants (PLTPH) are an alternative small-scale power plant that can be applied in rural areas where there is a river flow that has a continuous water discharge and a relatively low water fall to drive turbines that can produce electrical power. To be able to produce electrical power with such potential, a pico-hydro power plant is needed. To optimize the performance of a picohydro power plant, a controller called PID (Proportional Integral Derivative) is needed. Then this PID is combined with the ACO (Ant Colony Optimization) method. ACO is a method for optimizing PID control parameters in a system adapted from the ability of an ant colony to find the shortest path to a food source from its nest

Cutter head is one of the most important components for the full-face rock tunnel boring machine (TBM), in which the layout design of the disc cutters is a key issue. Considering the complex engineering technical requirements, this paper studied an ant colony optimization (ACO) algorithm to solve the disc cutters plane layout problem. The searching space of the design variables (disc cutters’ position) were dispersed by setting several different search steps, then an ACO algorithm was adopted to search the best searching step of each design variable dynamically during the whole optimization process. Finally, the disc cutters layout design instance was solved by the ACO method to demonstrate its feasibility and effectiveness. The computational results showed that that ACO method can provide various disc cutters layout schemes for engineers choosing from.KeywordsFull-face tunnel boring machineDisc cutterslayoutAnt Colony Optimization

Routing is a critical issue in the Delay Tolerant Network as finding the most optimized path, with minimum loss of data and in the shortest span of time is a challenge. Various researchers have proposed different strategies for detection of the effective and efficient path.In this paper we apply a meta-heuristic method of ant colony optimization (ACO) to propose a routing mechanism for Delay Tolerant Networks to find the optimized path with maximum throughput. Modifications have been carried out in ACO to incorporate characteristics of another meta-heuristic technique known as particle swarm optimization for building up effective routing scheme. Experiments on simulated networks using agent-based modeling tool named NetLogo shows that our technique performed better as compared to the original ACO. Here the throughput has been specially taken as the performance factor of the network.

Due to the emerging of modern evolution of the Fifth Generation (5G) wireless technology and the growth of data traffic to meet various communication requirements of future applications. Routing and path planning in 5G wireless networks is one of the challenges brought about by the variation of network features like traffic load and topology which may differ in stochastic and time varying manner. However, as the mobile users' growth, there is a need to efficient routing strategy for shortest path to forward user requests to the servers to reach services. To meet this aim, an adaptive Meta-heuristic Ant Colony Optimization (ACO) algorithm has proposed in this paper. ACO based on swarm intelligence techniques that has considered inspired algorithm by imitating the sociological attitude related to ants crowd. ACO like the other evolutionary algorithms based on population; it initiates with random solutions of the population. This work was implemented using NS3 simulation tool with different scenarios and parameters. The computational results have shown that the ACO method is better to find the optimal solution in 5G networks than the traditional inelastic way. The simulation outcome indicates the efficiency of the proposed method where the packet delivery ratio increased while the drop packets ratio and the transmission delay reduced.

Mass customization necessitates increased product variety at the customers’ end but comparatively lesser part variety at the manufacturer’s end. Product platform concepts have been successful to achieve this goal at large. One of the popular methods for product platform formation is to scale one or more design variables called the scaling variables. Effective optimization methods are needed to identify proper values of the scaling variables. This paper presents a graph-based optimization method called the scalable platforms using ant colony optimization (SPACO) method for identifying appropriate values of the scaling variables. In the graph-based representation, each node signifies a sub-range of values for a design variable. This application includes the concept of multiplicity in node selection because there are multiple nodes corresponding to the discretized values of a given design variable. In the SPACO method, the overall decision is a result of the cumulative decisions, made by simple computing agents called the ants, over a number of iterations. The space search technique initially starts as a random search technique over the entire search space and progressively turns into an autocatalytic (positive feedback) probabilistic search technique as the solution matures. We use a family of universal electric motors, widely cited in the literature, to test the effectiveness of the proposed method. Our simulation results, when compared to the results reported in the literature, prove that SPACO method is a viable optimization method for determining the values of design variables for scalable platforms.

&lt;span&gt;The increasing demand of electricity and number of distributed generations connected to power system greatly influence the level of power service reliability. This paper aims at improving the reliability in an electric power distribution system by optimizing the number and location of sectionalizers using the Ant Colony Optimization (ACO) and Simulated Annealing (SA) methods. Comparison of these two methods has been based on the reliability indices commonly used in distribution system: SAIFI, SAIDI, and CAIDI. A case study has been taken and simulated at a feeder of Pujon, a place in East Java province of Indonesia, to which some distributed generators were connected. Using the existing reliability indices condition as base reference, the addition of two distributed plants, which were micro hydro and wind turbine plants, has proven to lower the indices as much as 0.78% for SAIFI, 0.79% for SAIDI, and 2.32% for CAIDI. The optimal relocation of the existing 16 sectionalizers in the network proved to decrease further the reliability indices as much as 43.96% for SAIFI, 45.52% for SAIDI, and 2.8% for CAIDI, which means bringing to much better reliability condition. The implementation of the SA method on the considered data in general resulted in better reliability indices than using the ACO method.&lt;/span&gt;

In this paper, we present an ant colony optimization algorithm for load shedding minimization in smart grids. Smart grid technologies offer efficient solutions to the supply and distribution of power. However, its a great challenge to maintain the proper flow in the grid and minimize the load shedding. In reality, the situation might change dynamically and the behavior of the grid network is also stochastic. We applied our algorithm to solve the load shedding minimization problem in a deterministic setting. Our algorithm is a hybrid ant colony optimization with a constructive ant-based method followed by a local search phase. We have tested the performance of our algorithm on datasets generated using statistical data. Moreover our algorithm shows potentials for exploration in terms of the runtime and the significantly better solutions have been found.

The Spiking Neural Network (SNN) model is a third-generation neural network model that uses spike pulse trains to detect and classify electrical problems in the electrical equipment under consideration. To diagnose the thermal issue in the early stages, it is required to evaluate and monitor the electrical components. The detection method in Infrared Thermography (IRT) is hybrid optimization, which is a Dragonfly Algorithm (DA)–Ant Colony Optimization (ACO) technique that produces a higher exploration and exploitation rate while moving toward an optimal solution with a higher convergence rate. The innovative hybrid DA–ACO method used in this study aims to find the best weights for the SNN model while also extracting the most useful characteristics for defect detection in electrical equipment. To improve classification accuracy, the optimum features picked using the created hybrid DA–ACO are provided to the SNN model. Based on conventional and other optimization methods, the proposed method provided superior results from the execution results.

The importance of machine maintenance has been gradually recognized especially with the great attention in industrial sector. A company was named M is a manufacturing company which engaged in the industrial manufacturer of body pail cans. Previously, the process of machine maintenance at company M is to repair the machine when a problem occurs. This causes several machines to break down frequently and disrupt the production process. Furthermore, the purpose of this research is to determine the optimum and well-planned maintenance scheduling that can reduce the risk of-or prevent machine failures that may ruin the production process by doing the preventive maintenance in right time. Ant Colony Optimization (ACO) method was used in this research as maximizing the interval time between preventive maintenance periods before the trouble occurs based on previous breakdown data period as minimizing frequency of the task. In the principle of ACO, the required parameters are α, β, m, e, el. As a result of using ACO with the combination of parameters above, the optimal well-planned maintenance scheduling was obtained by using α=2, β=5, e=0.3, e1=0.96, and a number of ants needed. Finally, the optimizing of schedule maintenance has proposed in daily for next year period.