Towards the fast and robust optimal design of wireless body area networks

Wireless body area networks are wireless sensor networks whose adoption has recently emerged and spread in important healthcare applications, such as the remote monitoring of health conditions of patients. A major issue associated with the deployment of such networks is represented by energy consumption: in general, the batteries of the sensors cannot be easily replaced and recharged, so containing the usage of energy by a rational design of the network and of the routing is crucial. Another issue is represented by traffic uncertainty: body sensors may produce data at a variable rate that is not exactly known in advance, for example because the generation of data is event-driven. Neglecting traffic uncertainty may lead to wrong design and routing decisions, which may compromise the functionality of the network and have very bad effects on the health of the patients. In order to address these issues, in this work we propose the first robust optimization model for jointly optimizing the topology and the routing in body area networks under traffic uncertainty. Since the problem may result challenging even for a state-of-the-art optimization solver, we propose an original optimization algorithm that exploits suitable linear relaxations to guide a randomized fixing of the variables, supported by an exact large variable neighborhood search. Experiments on realistic instances indicate that our algorithm performs better than a state-of-the-art solver, fast producing solutions associated with improved optimality gaps.     

An efficient hybrid particle swarm optimization algorithm in a rolling horizon framework for the aircraft landing problem

This paper proposes a hybrid particle swarm optimization algorithm in a rolling horizon framework to solve the aircraft landing problem (ALP). ALP is an important optimization problem in air traffic control and is well known as NP-hard. The problem consists of allocating the arriving aircrafts to runways at an airport and assigning a landing time to each aircraft. Each aircraft has an optimum target landing time determined based on its most fuel-efficient airspeed and a deviation from it incurs a penalty which is proportional to the amount of deviation. The landing time of each aircraft is constrained within a specified time window and must satisfy minimum separation time requirement with its preceding aircrafts. The objective is to minimize the total penalty cost due to deviation of landing times of aircrafts from the respective target landing times. The performance of the proposed algorithm is evaluated on a set of benchmark instances involving upto 500 aircrafts and 5 runways. Computational results reveal that the proposed algorithm is effective in solving the problem in short computational time.     

Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem

We consider the university course timetabling problem, which is one of the most studied problems in educational timetabling. In particular, we focus our attention on the formulation known as the curriculum-based course timetabling problem (CB-CTT), which has been tackled by many researchers and for which there are many available benchmarks.The contribution of this paper is twofold. First, we propose an effective and robust single-stage simulated annealing method for solving the problem. Second, we design and apply an extensive and statistically-principled methodology for the parameter tuning procedure. The outcome of this analysis is a methodology for modeling the relationship between search method parameters and instance features that allows us to set the parameters for unseen instances on the basis of a simple inspection of the instance itself. Using this methodology, our algorithm, despite its apparent simplicity, has been able to achieve high quality results on a set of popular benchmarks.A final contribution of the paper is a novel set of real-world instances, which could be used as a benchmark for future comparison.     

Multi-directional local search

This paper introduces multi-directional local search, a metaheuristic for multi-objective optimization. We first motivate the method and present an algorithmic framework for it. We then apply it to several known multi-objective problems such as the multi-objective multi-dimensional knapsack problem, the bi-objective set packing problem and the bi-objective orienteering problem. Experimental results show that our method systematically provides solution sets of comparable quality with state-of-the-art methods applied to benchmark instances of these problems, within reasonable CPU effort. We conclude that the proposed algorithmic framework is a viable option when solving multi-objective optimization problems.     

Reliability-based design optimization with discrete design variables and non-smooth performance functions: AB-PSO algorithm

The purpose of reliability-based design optimization (RBDO) is to find a balanced design that is not only economical but also reliable in the presence of uncertainty. Practical applications of RBDO involve discrete design variables, which are selected from commercially available lists, and non-smooth (non-differentiable) performance functions. In these cases, the problem becomes an NP-complete combinatorial optimization problem, which is intractable for discrete optimization methods. Moreover, the non-smooth performance functions would hinder the use of gradient-based optimizers as gradient information is of questionable accuracy. A framework is presented in this paper whereby subset simulation is integrated with a new particle swarm optimization (PSO) algorithm to solve the discrete and non-smooth RBDO problem. Subset simulation overcomes the inefficiency of direct Monte Carlo simulation (MCS) in estimating small failure probabilities, while being robust against the presence of non-smooth performance functions. The proposed PSO algorithm extends standard PSO to include two new features: auto-tuning and boundary-approaching. The former feature allows the proposed algorithm to automatically fine tune its control parameters without tedious trial-and-error procedures. The latter feature substantially increases the computational efficiency by encouraging movement toward the boundary of the safe region. The proposed auto-tuning boundary-approaching PSO algorithm (AB-PSO) is used to find the optimal design of a ten-bar truss, whose component sizes are selected from commercial standards, while reliability constraints are imposed by the current design code. In multiple trials, the AB-PSO algorithm is able to deliver competitive solutions with consistency. The superiority of the AB-PSO algorithm over standard PSO and GA (genetic algorithm) is statistically supported by non-parametric Mann-Whitney U tests with the p-value less than 0.01.     

Optimised loading patterns for intermodal trains

This paper considers one important aspect of operations planning referred to hereafter as train planning. Train planning is the process of spatially assigning containers to specific wagons (also known as railcars) on an intermodal train. The spatial arrangement of containers on a train can have a significant influence over the amount of time and energy consumed in the handling of containers. Efficient train planning can also maximise utilisation of wagon carrying capacity. This study proposes a mixed-integer programming model to determine the arrangement of containers on a train to minimise a weighted sum of number of wagons required and equipment working time. Due to the large number of variables, the proposed model cannot be solved in a timely manner for practical problems. This is addressed by applying heuristic algorithms local search and simulated annealing. Discrete-event simulation of an intermodal terminal is used to evaluate the proposed methods and to illuminate various properties of the model.     

Optimization of the Electrical Performance of Polymeric Films

In the present study, an attempt is made to optimize the electrical performance of the thin polymeric films through optimization techniques. The study is conducted in two phases: (1) laboratory experiments and (2) through numerical optimization. For laboratory analysis, thin and transparent films are prepared using polyethersulfone (PES) as host material and meta-nitroaniline (MNA) as guest materials. A set of nine film samples are prepared by the solution casting method in the laboratory using different concentrations of MNA. The electrical properties capacitance, conductance, and dissipation factor of films are measured by Aligent Impedance Analyzer. These characteristics are then optimized mathematically. For this purpose, initially single-objectives are considered for optimizing the electrical properties individually, and later a multiobjective model is considered for analyzing the properties simultaneously. The algorithms employed are metaheuristics: genetic algorithms, particle swarm optimization, differential evolution, and its variant modified differential evolution along with fmincon (a MATLAB toolbox) for single-objective optimization and multiobjective differential evolution algorithm and nondominated sorting genetic algorithm-II for multiobjective optimization.     

Self-organizing feature maps for the vehicle routing problem with backhauls

In the Vehicle Routing Problem with Backhauls (VRPB), a central depot, a fleet of homogeneous vehicles, and a set of customers are given. The set of customers is divided into two subsets. The first (second) set of linehauls (backhauls) consists of customers with known quantity of goods to be delivered from (collected to) the depot. The VRPB objective is to design a set of minimum cost routes; originating and terminating at the central depot to service the set of customers. In this paper, we develop a self-organizing feature maps algorithm, which uses unsupervised competitive neural network concepts. The definition of the architecture of the neural network and its learning rule are the main contribution. The architecture consists of two types of chains: linehaul and backhaul chains. Linehaul chains interact exclusively with linehaul customers. Similarly, backhaul chains interact exclusively with backhaul customers. Additonal types of interactions are introduced in order to form feasible VRPB solution when the algorithm converges. The generated routes are then improved using the well-known 2-opt procedure. The implemented algorithm is compared with other approaches in the literature. The computational results are reported for standard benchmark test problems. They show that the proposed approach is competitive with the most efficient metaheuristics.     

A tabu search approach for the minimum sum-of-squares clustering problem

In this paper, a tabu search based clustering approach called TS-Clustering is proposed to deal with the minimum sum-of-squares clustering problem. In the TS-Clustering algorithm, five improvement operations and three neighborhood modes are given. The improvement operation is used to enhance the clustering solution obtained in the process of iterations, and the neighborhood mode is used to create the neighborhood of tabu search. The superiority of the proposed method over some known clustering techniques is demonstrated for artificial and real life data sets.     

Analysis of new variable selection methods for discriminant analysis

Several methods to select variables that are subsequently used in discriminant analysis are proposed and analysed. The aim is to find from among a set of m variables a smaller subset which enables an efficient classification of cases. Reducing dimensionality has some advantages such as reducing the costs of data acquisition, better understanding of the final classification model, and an increase in the efficiency and efficacy of the model itself. The specific problem consists in finding, for a small integer value of p, the size p subset of original variables that yields the greatest percentage of hits in the discriminant analysis. To solve this problem a series of techniques based on metaheuristic strategies is proposed. After performing some test it is found that they obtain significantly better results than the stepwise, backward or forward methods used by classic statistical packages. The way these methods work is illustrated with several examples.