The Application of Ant Colony Optimization

The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance

Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks

Currently, wireless sensor networks (WSNs) are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC) algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO) is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO) and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP) hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i) selection of optimal number of subregions and further subregion parts, (ii) cluster head selection using ABC algorithm, and (iii) efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS). The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively

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Social Odometry: Imitation Based Odometry in Collective Robotics

The improvement of odometry systems in collective robotics remains an important challenge for several applications. Social odometry is an online social dynamics which confers the robots the possibility to learn from the others. Robots neither share any movement constraint nor access to centralized information. Each robot has an estimate of its own location and an associated confidence level that decreases with distance traveled. Social odometry guides a robot to its goal by imitating estimated locations, confidence levels and actual locations of its neighbors. This simple online social form of odometry is shown to produce a self-organized collective pattern which allows a group of robots to both increase the quality of individuals’ estimates and efficiently improve their collective performanc

Ant colony optimization

En los últimos años, la comunidad científica ha realizado una gran cantidad de propuestas de nuevas metaheurísticas que prometían resolver un amplio espectro de problemas de optimización del tipo NP. Sin embargo, en la práctica solamente un grupo pequeño de esas propuestas han logrado consolidarse, demostrando una amplia aplicabilidad sobre problemas de muy diversas características y adquiriendo la madurez necesaria como técnica de optimización para ser una alternativa real al momento de resolver un problema de optimización. Ant Colony Optimization (ACO) es una metaheurística sobre la que se ha trabajado ampliamente en los últimos 15 años. Se ha aplicado con éxito sobre varios de los problemas estándares de optimización demostrando su potencial. El presente reporte es un relevamiento de las diversas variantes de ACO que han sido propuestas en estos 15 años. El eje central de este relevamiento es el estudio de las propuestas existentes para problemas estáticos de optimación combinatoria

Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection

Anomaly detection deals with identification of items that do not conform to an expected pattern or items present in a dataset. The performance of different mechanisms utilized to perform the anomaly detection depends heavily on the group of features used. Thus, not all features in the dataset can be used in the classification process since some features may lead to low performance of classifier. Feature selection (FS) is a good mechanism that minimises the dimension of high-dimensional datasets by deleting the irrelevant features. Modified Binary Grey Wolf Optimiser (MBGWO) is a modern metaheuristic algorithm that has successfully been used for FS for anomaly detection. However, the MBGWO has several issues in finding a good quality solution. Thus, this study proposes an enhanced binary grey wolf optimiser (EBGWO) algorithm for FS in anomaly detection to overcome the algorithm issues. The first modification enhances the initial population of the MBGWO using a heuristic based Ant Colony Optimisation algorithm. The second modification develops a new position update mechanism using the Bat Algorithm movement. The third modification improves the controlled parameter of the MBGWO algorithm using indicators from the search process to refine the solution. The EBGWO algorithm was evaluated on NSL-KDD and six (6) benchmark datasets from the University California Irvine (UCI) repository against ten (10) benchmark metaheuristic algorithms. Experimental results of the EBGWO algorithm on the NSL-KDD dataset in terms of number of selected features and classification accuracy are superior to other benchmark optimisation algorithms. Moreover, experiments on the six (6) UCI datasets showed that the EBGWO algorithm is superior to the benchmark algorithms in terms of classification accuracy and second best for the number of selected features. The proposed EBGWO algorithm can be used for FS in anomaly detection tasks that involve any dataset size from various application domains

Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality