The paper introduces a evolutionary algorithm, based on Multi Population Genetic Algorithm (MPGA), to support decisions about allocation and power sizing of generation units within power systems. The effectiveness of the MPGA is tested over two different radial distribution systems taking into account five different scenario. The evaluated power systems are the 33 and 69-bus radial distribution systems, which are benchmark systems from literature. The promising results achieved are compared against recent literature results for the same problem.

The path planning methods based on nonconvex constrained optimization, such as mixed-integer linear programming (MILP), have found various important applications, ranging from unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) to space vehicles. Moreover, their stochastic extensions have enabled risk-aware path planning, which explicitly limits the probability of failure to a user-specified bound. However, a major challenge of those path planning methods is constraint violation between discrete time steps. In the existing approach, a path is represented by a sequence of waypoints and the safety constraints (e.g., obstacle avoidance) are imposed on waypoints. Therefore, the trajectory between waypoints could violate the safety constraints. A naive continuous-time extension results in unrealistic computation cost. In this paper, we propose a novel approach to ensure constraint satisfaction between waypoints without employing a continuous-time formulation. The key idea is to enforce that the same inequality constraint is satisfied on any two adjacent time steps, under assumptions of polygonal obstacles and straight line trajectory between waypoints. The resulting problem encoding is MILP, which can be solved efficiently by commercial solvers. Thus, we also introduce novel extensions to risk-allocation path planners with improved scalability for real-world scenarios and run-time performance. While the proposed encoding approach is general, the particular emphasis of this paper is placed on the chance-constrained, nonconvex path-planning problem (CNPP). We provide extensive simulation results on CNPP to demonstrate the path safety and scalability of our encoding and related path planners.

The present paper proposes two hybrid genetic algorithms as decision-making techniques for operational level decisions in the Glass Container Industry (GCI). The proposed methods address a production scenario where one new furnace and the related machines must be added to the current industrial plant. The configurations for each machine connected in a furnace is a decision to be taken, which depends on demand forecasts for glass containers within a time horizon. It is a tactical and operational level decisions that must be efficiently made. A mathematical formulation is first presented to describe precisely the objective and constraints for such problem. The formulation will also allow solving the problem instances by applying an exact method. Next, a hybrid approach combining genetic algorithms with mathematical programming techniques, and a greedy filter heuristic is proposed to solve the same problem instances. The set of instances is generated with data provided by a GCI located in Portugal and Brazil. The results reported indicate that the hybrid genetic algorithms return solutions able to support the operational and tactical decisions.

The present paper describes an embedded system architecture, based on genetic algorithms, aiming safety mission execution by Unmanned Aerial Vehicles (UAVs). A two-dimensional non-convex environment is considered since obstacle avoidance happens. The embedded system integrates the Mission Oriented Sensor Array (MOSA) and In-Flight Awareness (IFA) systems, where MOSA is responsible for mission accomplishment and IFA stands for flight safety. The features of MOSA and IFA are combined under a platform that applies promising genetic algorithm approaches from literature to reach their goals. First, the genetic algorithms performance running from the embedded system is compared against their performance on a personal computer architecture. Next, the proposed system is evaluated in a real-world scenario using Software-In-The-Loop (SITL) technique. The computational results showed that the embedded system provides reliable results.

The present paper evaluates some strategies to apply path re-planning algorithms for emergency landing of Unmanned Aerial Vehicles (UAVs). The strategies proposed are based on executing only one path re-planning algorithm or a combination of them using an ensemble approach. The proposed methods are integrated to the security supervision system of the UAV, namely In-Fly Awareness (IFA), where decision-making algorithms are applying after a critical situation happens. The critical situations considered are battery overheating and engine failure. The performance of the proposed strategies for emergency landing is evaluated in a real world scenario using the Software-In-The-Loop (SITL) technique. Computational results reported show that these strategies are promising based on the success rate to land the UAV safely.

This paper proposes a hybrid method to define a path planning for unmanned aerial vehicles in a non-convex environment with uncertainties. The environment becomes non-convex by the presence of no-fly zones such as mountains, cities and airports. Due to the uncertainties related to the path planning in real situations, risk of collision can not be avoided. Therefore, the planner must take into account a lower level of risk than one tolerated by the user. The proposed hybrid method combines a multi-population genetic algorithm with visibility graph. This is done by encoding all possible paths as individuals and solving a linear programming model to define the full path to be executed by the aircraft. The hybrid method is evaluated from a set of 50 maps and compared against an exact and heuristic approaches with promising results reported.

This paper studies the path planning for Unmanned Aerial Vehicles (UAVs) under critical situations, where the aircraft has to execute a hard landing. Such critical situations can be provoked by equipment failures or extreme environmental situations that demand the UAV to abort the mission running and to land the aircraft without risk for people, properties and itself. First, a mathematical formulation is introduced to describe this problem. A planner system is proposed based on a multi-population genetic algorithm and a greedy heuristic. Computational results are conducted over a large set of scenarios with different levels of difficulty. Also, some simulations are executed using FlightGear simulator to illustrate the UAV's behaviour when landing under different wind velocities. The results achieved indicate the greedy heuristic is able to define faster feasible landing paths, whose quality can be improved by the evolutionary approach always within a short computation time.

The development of Wireless Sensor Networks (WSNs) applications is an arduous task, since the application needs to be customized for each sensor. Thus, the automatic generation of WSN's applications is desirable to reduce costs, since it drastically reduces the human effort. This paper presents the use of Genetic Programming to automatically generate WSNs applications. A scripting language based on events and actions is proposed to represent the WSN behavior. Events represent the state of a given sensor node and actions modify these states. Some events are internal states and others are external states captured by the sensors. The genetic programming is used to automatically generate WSNs applications described using this scripting language. These scripts are executed by all network's sensors. This approach enables the application designer to define only the overall objective of the WSN. This objective is defined by means of a fitness function. An event-detection problem is presented in order to evaluate the proposed method. The results shown the capability of the developed approach to successfully solve WSNs problems through the automatic generation of applications.

The present paper solves the multi-level capacitated lot sizing problem with backlogging (MLCLSPB) combining a genetic algorithm with the solution of mixed-integer programming models and the improvement heuristic fix and optimize. This approach is evaluated over sets of benchmark instances and compared to methods from literature. Computational results indicate competitive results applying the proposed method when compared with other literature approaches.

The present paper applies a multi-population genetic algorithm (MPGA) to the Proportional, Integral and Derivative (PID) controller tuning problem. Two control criteria were optimized, the integral of the time multiplied by the absolute error (ITAE), and the integral of the time multiplied by the absolute output (ITAY). The MPGA is compared with a standard genetic algorithms (SGA) already applied to the same control model. The control criteria are supplied by neural networks (NN) previously trained for this purpose. The control tuning and the corresponding responses were obtained using the MATLAB/SIMULNK environment. The computational results show a superior performance of the MPGA even when compared with the exact values found by dynamic simulation using gradient techniques.

This paper proposes a tabu search approach to solve the Synchronized and Integrated Two-Level Lot Sizing and Scheduling Problem (SITLSP). It is a real-world problem, often found in soft drink companies, where the production process has two integrated levels with decisions concerning raw material storage and soft drink bottling. Lot sizing and scheduling of raw materials in tanks and products in bottling lines must be simultaneously determined. Real data provided by a soft drink company is used to make comparisons with a previous genetic algorithm. Computational results have demonstrated that tabu search outperformed genetic algorithm in all instances. Copyright 2011 ACM.