Generator maintenance scheduling of electric power systems using genetic algorithms with integer representations

The effective maintenance scheduling of power system generators is very important to a power utility for the economical and reliable operation of a power system. Many mathematical methods have been implemented for generator maintenance scheduling (GMS). However, these methods have many limitations and require many approximations. Here a Genetic Algorithm is proposed for GMS problems in order to overcome some of the limitations of the conventional methods. This paper formulates a general GMS problem using a reliability criterion as an integer programming problem, and demonstrates the use of GAs with three different problem encodings: binary, binary for integer and integer. The GA performances for each of these representations are analysed and compared for a test problem based on a practical power system scenario. The effects of different GA parameters are also studied. The results show that the integer GA is a very effective method for GMS problems

Constraint handling strategies in Genetic Algorithms application to optimal batch plant design

Optimal batch plant design is a recurrent issue in Process Engineering, which can be formulated as a Mixed Integer Non-Linear Programming(MINLP) optimisation problem involving specific constraints, which can be, typically, the respect of a time horizon for the synthesis of various products. Genetic Algorithms constitute a common option for the solution of these problems, but their basic operating mode is not always wellsuited to any kind of constraint treatment: if those cannot be integrated in variable encoding or accounted for through adapted genetic operators, their handling turns to be a thorny issue. The point of this study is thus to test a few constraint handling techniques on a mid-size example in order to determine which one is the best fitted, in the framework of one particular problem formulation. The investigated methods are the elimination of infeasible individuals, the use of a penalty term added in the minimized criterion, the relaxation of the discrete variables upper bounds, dominancebased tournaments and, finally, a multiobjective strategy. The numerical computations, analysed in terms of result quality and of computational time, show the superiority of elimination technique for the former criterion only when the latter one does not become a bottleneck. Besides, when the problem complexity makes the random location of feasible space too difficult, a single tournament technique proves to be the most efficient one

Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

Global optimization is an essential part of any kind of system. Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. The main idea of all nature-inspired algorithms is to generate an interconnected network of individuals, a population. Although most of unconstrained optimization problems can be easily handled with Evolutionary Algorithms (EA), constrained optimization problems (COPs) are very complex. In this paper, a comprehensive literature review will be presented which summarizes the constraint handling techniques for COP

Real Evaluations Tractability using Continuous Goal-Directed Actions in Smart City Applications

One of the most important challenges of Smart City Applications is to adapt the system to interact with non-expert users. Robot imitation frameworks aim to simplify and reduce times of robot programming by allowing users to program directly through action demonstrations. In classical robot imitation frameworks, actions are modelled using joint or Cartesian space trajectories. They accurately describe actions where geometrical characteristics are relevant, such as fixed trajectories from one pose to another. Other features, such as visual ones, are not always well represented with these pure geometrical approaches. Continuous Goal-Directed Actions (CGDA) is an alternative to these conventional methods, as it encodes actions as changes of any selected feature that can be extracted from the environment. As a consequence of this, the robot joint trajectories for execution must be fully computed to comply with this feature-agnostic encoding. This is achieved using Evolutionary Algorithms (EA), which usually requires too many evaluations to perform this evolution step in the actual robot. The current strategies involve performing evaluations in a simulated environment, transferring only the final joint trajectory to the actual robot. Smart City applications involve working in highly dynamic and complex environments, where having a precise model is not always achievable. Our goal is to study the tractability of performing these evaluations directly in a real-world scenario. Two different approaches to reduce the number of evaluations using EA, are proposed and compared. In the first approach, Particle Swarm Optimization (PSO)-based methods have been studied and compared within the CGDA framework: naïve PSO, Fitness Inheritance PSO (FI-PSO), and Adaptive Fuzzy Fitness Granulation with PSO (AFFG-PSO).The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

An introduction of Krill Herd algorithm for engineering optimization

A new metaheuristic optimization algorithm, called Krill Herd (KH), has been recently proposed by Gandomi and Alavi (2012). In this study, KH is introduced for solving engineering optimization problems. For more verification, KH is applied to six design problems reported in the literature. Further, the performance of the KH algorithm is com­pared with that of various algorithms representative of the state-of-the-art in the area. The comparisons show that the results obtained by KH are better than the best solutions obtained by the existing methods. First published online: 25 Aug 201

Vector well pattern optimization of horizontal well in offshore edge water reservoirs

The L30up reservoir is a strongly heterogeneous edge water reservoir with obvious provenance direction and channel direction. It is developed by horizontal wells, and the traditional well pattern adjustment is not suitable for tapping the remaining oil potential of this type of reservoirs, while vector well pattern adjustment is one of the important measures to enhance oil recovery. In this paper, aiming at maximizing the economic net present value, taking the characteristic parameter matrix of well pattern reconfiguration (well position, azimuth angle, horizontal section length) as variables, an optimization model of horizontal well pattern vector adjustment is established. Furthermore, the PSO-MADS algorithm is proposed to solve the above optimization model. Thus, a vector well pattern adjustment technology that can realize the distribution matching of reservoir heterogeneity and remaining oil has been formed. On the basis of the deployment strategy of vector well pattern in L30up reservoir, according to the direction of sediment source, reservoir heterogeneity, distribution of remaining oil, etc., we determined the best vector well pattern adjustment scheme, and applied the above optimization method to optimize the infill well locations. Through the optimal deployment of the vector well pattern, the oil recovery factor after the vector well pattern adjustment is 5.21% percentage points higher than the original well pattern conditions, which precisely matches the well pattern parameters and the geological vector parameters, such as sand body distribution, remaining oil distribution, and edge waters in L30up reservoir