Murat Ermis

Solving the uncapacitated hub location problem using genetic algorithms

Hub location problems are widely studied in the area of location theory, where they involve locating the hub facilities and designing the hub networks. In this paper, we present a new and robust solution based on a genetic search framework for the uncapacitated single allocation hub location problem (USAHLP). To present its effectiveness, we compare the solutions of our GA-based method with the best solutions presented in the literature by considering various problem sizes of the CAB data set and the AP data set. The experimental work demonstrates that even for larger problems the results of our method significantly surpass those of the related work with respect to both solution quality and the CPU time to obtain a solution. Specifically, the results from our method match the optimal solutions found in the literature for all test cases generated from the CAB data set with significantly less running time than the related work. For the AP data set, our solutions match the best solutions of the reference study with an average of 8 times less running time than the reference study. Its performance, robustness and substantially low computational effort justify the potential of our method for solving larger problem sizes.

3-D path planning for the navigation of unmanned aerial vehicles by using evolutionary algorithms

Military missions are turning to more complicated and advanced automation technology for maximum endurance and efficiency as well as the minimum vital risks. The path planners which generate collision-free and optimized paths are needed to give autonomous operation capability to the Unmanned Aerial Vehicles (UAVs). This paper presents an off-line path planner for UAVs. The path planner is based on Evolutionary Algorithms (EA), in order to calculate a curved path line with desired attributes in a 3-D terrain. The flight path is represented by parameterized B-Spline curves by considering four objectives: the shortest path to the destination, the feasible path without terrain collision, the path with the desired minimum and maximum distance to the terrain, and the path which provides UAV to maneuver with an angle greater than the minimum radius of curvature. The generated path is represented with the coordinates of its control points being the genes of the chromosome of the EA. The proposed method was tested in several 3-D terrains, which are generated with various terrain generator methods that differ with respect to levels of smoothness of the terrain.

Positioning and Utilizing Sensors on a 3-D Terrain Part I—Theory and Modeling

Positioning multiple sensors for acquisition of a given environment is one of the fundamental research areas in various fields, such as military scouting, computer vision, and robotics. In this paper, we propose a new model for the problem of sensor deployment. Deploying and configuring a set of given sensors on a synthetically generated 3-D terrain have multiple objectives on conflicting attributes: maximizing the visibility of the given terrain, maximizing the stealth of the sensors, and minimizing the cost of the sensors used. Since they are utility-independent, these complementary and conflicting objectives are modeled by a multiplicative total utility function, based on multiattribute utility theory. The total utility function proposed in this paper can also be adapted for various military scouting missions with different characteristics.

Positioning and Utilizing Sensors on a 3-D Terrain Part II—Solving With a Hybrid Evolutionary Algorithm

In this paper, we explore using a hybrid evolutionary algorithm (HEA) for deploying and configuring a set of given sensors on a synthetically generated 3-D terrain. In our evolutionary-algorithm (EA) based solution, various methods are considered in order to incorporate specialized operators for hybridization, including problem-specific heuristics for initial population generation, intelligent variation operators (contribution-based-crossover operator and proximity-based-crossover operator), which comprise problem-specific knowledge, and a local-search phase. The experimental study validates finding the optimal balance among visibility-oriented, stealth-oriented, and cost-oriented objectives. The obtained results also indicate the effectiveness and robustness of our HEA-based solution for various practical scenarios with different objectives.

Vibrational Genetic Algorithm (Vga) for Solving Continuous Covering Location Problems

This paper deals with a continuous space problem in which demand centers are independently served from a given number of independent, uncapacitated supply centers. Installation costs are assumed not to depend on either the actual location or actual throughput of the supply centers. Transportation costs are considered to be proportional to the square Euclidean distance travelled and a mini-sum criteria is adopted. In order to solve this location problem, a new heuristic method, called Vibrational Genetic Algorithm (VGA), is applied. VGA assures efficient diversity in the population and consequently provides faster solution. We used VGA using vibrational mutation and for the mutational manner, a wave with random amplitude is introduced into population periodically, beginning with the initial step of the genetic process. This operation spreads out the population over the design space and increases exploration performance of the genetic process. This makes passing over local optimums for genetic algorithm easy. Since the problem is recognized as identical to certain cluster analysis and vector quantization problems, we also applied Kohonen maps which are Artificial Neural Networks (ANN) capable of extracting the main features of the input data through a self-organizing process based on local adaptation rules. The numerical results and comparison will be presented.

Connected multi UAV task planning for Flying Ad Hoc Networks

Flying Ad Hoc Networks (FANETs) is one of the most effective multi communication architectures through its capability of transferring data simultaneously without any infrastructure. The FANET task allocation problem is one-to-one assignment of agents to tasks so that the overall benefit of all the agents is maximized by taking delays and costs into account, given a set of agents and a set of tasks. A coordination based task allocation system ensuring spatial and temporal coordination between UAVs is essential for FANETs. In this paper, a new multi UAV task planning heuristic is proposed for FANETs to visit all target points in a minimum time, while preserving all time network connectivity. Effectiveness in the mission execution and cost efficiency in the task allocation have been presented by conducting a bunch of experiments performed on 2D terrains. Performance results validated the usage of our algorithms for the connected multi UAV task planning problem for FANET.

A meta-heuristic based three-dimensional path planning environment for unmanned aerial vehicles

Unmanned Aerial Vehicles (UAVs) are used for many missions, including weather reconnaissance, search and rescue assisting operations over seas and mountains, aerial photographing and mapping, fire detection, and traffic control. Autonomous operation of UAVs requires the development of control systems that can work without human support for long time periods. The path planners, which generate collision-free and optimized paths, are needed to provide autonomous operation capabilities to the UAVs. The optimization of the flight trajectory is a multi-objective problem dealing with variable terrain features as well as dynamic environment conditions. This paper presents a simulation environment for offline path planning of unmanned aerial vehicles on three-dimensional terrains. Our path planner aims to identify the shortest path and/or flight envelope in a given line of sight by avoiding terrain collisions, traveling on a path that stays within the restricted minimum and maximum distances above the terrain, traveling far from the specified threat zones, and maneuvering with an angle greater than the minimum curvature radius. We present two meta-heuristics (genetic algorithms and hyper-heuristics) in order to construct the paths for UAV navigation and compare our results with a reference work given in the literature. A comparative study over a set of terrains with various characteristics validates the effectiveness of the proposed meta-heuristics, where the quality of a solution is measured with the total cost of a constructed path, including the penalties of all constraints.

A new three-dimensional wireless multimedia sensor network simulation environment for connected coverage problems

A wireless multimedia sensor network (WMSN) is a network of wirelessly interconnected sensors that can gather multimedia information, such as sound and vision. One of the most important design issues of a WMSN is to maximize the coverage, while preserving the network connectivity. Although there are many studies about coverage for WMSNs, most of them are based on two-dimensional terrain assumptions. However, particularly for outdoor applications, three-dimensional (3-D) terrain structure affects the performance of the WMSN remarkably. In this paper, a novel 3-D WMSN simulation environment for connected coverage issues is presented. There are four main modules of our simulation environment. The terrain generator (TerGen) generates a synthetic 3-D landscape with different weather conditions (snow, rain, and fog), object occlusions (artificial or natural objects), and toughness levels of terrain (smooth or rough). The scenario editor (SenEd) is used to define various sensor types that have various behavioral and locational attributes. The outputs of TerGen and SenEd are the inputs of the simulator engine (SimEn), which simulates the WMSN and gives the performance results. The Optimization Module (OptMod), which is optional, can be used to determine the location of the sensors optimally, while satisfying a set of predefined constraints. Different scenarios are simulated to show the capabilities of the simulation environment. The performance results show that the 3-D terrain structure affects the coverage performance of the WMSN directly. The object occlusions and weather conditions are also very important for WMSN coverage.

A new hybrid evolutionary algorithm for three - dimensional packing problems

This paper presents a novel hybrid evolutionary algorithm (HEA) based technique to solve container loading problem which is a knapsack type. In this problem, there exists a set of different types of boxes, and those boxes are tried to stow into a 3D container. Within our approach, the container loading patterns are represented by choromosomes related with the problem. It includes specialized operators for hybridization, which are problem-specific heuristics, intelligent variation operators which comprise problem specific knowledge, and a local search phase. In the experimental study, comparison of results with existing algorithms justifies the potential of our method for solving strongly heterogeneous boxes.

A Hybrid Matheuristic Approach for Designing Reliable Wireless Multimedia Sensor Networks

One of the most important design considerations for Wireless Multimedia Sensor Networks (WMSNs) is the reliability which involves connectivity and coverage issues with sensor and relay node deployment strategies that affects the coverage performance of the network directly. This paper addresses synergies from combining exact algorithms and metaheuristics to solve relay node deployment problem so as to maximize the information gathering reliability. The objective of the proposed model is to maximize WMSN reliability by considering communication range of the nodes and terrain specific characteristics like occlusions, threat zones, and importance of targets under a given budget constraint. We also integrated a Branch&Bound (B&B) approach with a Hybrid Genetic Algorithm Based Matheuristic (HGABM) to find the exact orientations of the cameras, and a Mixed Integer Linear Programming (MILP) network flow model is used to find the exact deployment points of the relay nodes. Since the calculation of network reliabilities for each network is time consuming, a Parallel Monte Carlo (MC) simulation is also developed and performed on General Purpose Graphic Processing Unit (GPGPU). Experimental study and comparison is conducted on synthetically generated terrains with different characteristics in order to show the effectiveness of HGABM.