The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms

This paper compares the effectiveness of five state-of-the-art multiobjective evolutionary algorithms (MOEAs) together with a steady state evolutionary algorithm on the mean–variance cardinality constrained portfolio optimization problem (MVCCPO). The main computational challenges of the model are due to the presence of a nonlinear objective function and the discrete constraints. The MOEAs considered are the Niched Pareto genetic algorithm 2 (NPGA2), non-dominated sorting genetic algorithm II (NSGA-II), Pareto envelope-based selection algorithm (PESA), strength Pareto evolutionary algorithm 2 (SPEA2), and e-multiobjective evolutionary algorithm (e-MOEA). The computational comparison was performed using formal metrics proposed by the evolutionary multiobjective optimization community on publicly available data sets which contain up to 2196 assets.     

The importance of numerical algorithms for solving economic optimization problems †

This paper contains a brief review of the recent literature on economic applications of optimal control theory. It is not a comprehensive review, rather it serves to illustrate the assertion made in the title. Pursuit of an analytical solution confines the economic theorist to a narrow class of problems : termed here as the ' single differential equation model world '. Empirical economists searching for linear decision rule3 are also restricting themselves to a narrow class of problems. The adoption of general numerical algorithms would liberate both groups and this may yet prove to be the major contribution of systems theory to economics.     

Time–space complexity of quantum search algorithms in symmetric cryptanalysis: applying to AES and SHA-2

Performance of cryptanalytic quantum search algorithms is mainly inferred from query complexity which hides overhead induced by an implementation. To shed light on quantitative complexity analysis removing hidden factors, we provide a framework for estimating time–space complexity, with carefully accounting for characteristics of target cryptographic functions. Processor and circuit parallelization methods are taken into account, resulting in the time–space trade-off curves in terms of depth and qubit. The method guides how to rank different circuit designs in order of their efficiency. The framework is applied to representative cryptosystems NIST referred to as a guideline for security parameters, reassessing the security strengths of AES and SHA-2.     

Efficient classical simulation of the Deutsch–Jozsa and Simon’s algorithms

A long-standing aim of quantum information research is to understand what gives quantum computers their advantage. This requires separating problems that need genuinely quantum resources from those for which classical resources are enough. Two examples of quantum speed-up are the Deutsch–Jozsa and Simon’s problem, both efficiently solvable on a quantum Turing machine, and both believed to lack efficient classical solutions. Here we present a framework that can simulate both quantum algorithms efficiently, solving the Deutsch–Jozsa problem with probability 1 using only one oracle query, and Simon’s problem using linearly many oracle queries, just as expected of an ideal quantum computer. The presented simulation framework is in turn efficiently simulatable in a classical probabilistic Turing machine. This shows that the Deutsch–Jozsa and Simon’s problem do not require any genuinely quantum resources, and that the quantum algorithms show no speed-up when compared with their corresponding classical simulation. Finally, this gives insight into what properties are needed in the two algorithms and calls for further study of oracle separation between quantum and classical computation.     

Regression using hybrid Bayesian networks: Modelling landscape–socioeconomy relationships

Modelling environmental systems becomes a challenge when dealing directly with continuous and discrete data simultaneously. The aim in regression is to give a prediction of a response variable given the value of some feature variables. Multiple linear regression models, commonly used in environmental science, have a number of limitations: (1) all feature variables must be instantiated to obtain a prediction, and (2) the inclusion of categorical variables usually yields more complicated models. Hybrid Bayesian networks are an appropriate approach to solve regression problems without such limitations, and they also provide additional advantages. This methodology is applied to modelling landscape–socioeconomy relationships for different types of data (continuous, discrete or hybrid). Three models relating socioeconomy and landscape are proposed, and two scenarios of socioeconomic change are introduced in each one to obtain a prediction. This proposal can be easily applied to other areas in environmental modelling.     

A constrained optimization perspective on actor–critic algorithms and application to network routing

We propose a novel actor–critic algorithm with guaranteed convergence to an optimal policy for a discounted reward Markov decision process. The actor incorporates a descent direction that is motivated by the solution of a certain non-linear optimization problem. We also discuss an extension to incorporate function approximation and demonstrate the practicality of our algorithms on a network routing application.     

Internet primal–dual congestion control: Stability and applications

This paper studies the stability of the primal–dual algorithm for Internet congestion control with round-trip communication delays. The model is formulated as a time-delayed multivariable control system for a general network, and then is studied by using time-delayed control theory. A necessary and sufficient condition is established for a network with a general topology, and then is further discussed for the representative dumbbell network. The stability condition is applied to the design of FAST TCP and adaptive virtual queue (AVQ) scheme as guideline, which is validated by simulation results.     

Input–output operability of control systems: The steady-state case

For high-dimensional systems with more outputs than inputs, some outputs must be controlled within ranges, instead of at set-points. This may also be true if the outputs are equal in number to the inputs and disturbances of high magnitude exist. A linear programming framework is postulated to calculate the tightest achievable operating ranges of the outputs, given the ranges of the inputs and the expected disturbances, for any linear input–output control system at the steady-state. This approach removes the computational constraints on the size of the problem that a previous communication of the authors [1] could address. The hyper-volume obtained for the tightest achievable outputs’ region of a high-dimensional industrial process is calculated to be four orders of magnitude smaller than the one initially assumed, enabling much tighter control.     

The capacitated multi-facility location–allocation problem with probabilistic customer location and demand: two hybrid meta-heuristic algorithms

A new mathematical model for the capacitated multi-facility location–allocation problem with probabilistic customers' locations and demands is developed in this article. The model is formulated into the frameworks of the expected value model (EVM) and the chance-constrained programming (CCP) based on two different distance measures. In order to solve the model, two hybrid intelligent algorithms are proposed, where the simplex algorithm and stochastic simulation are the bases for both algorithms. However, in the first algorithm, named SSGA, a special type of genetic algorithm is combined and in the second, SSVDO, a vibration-damping optimisation (VDO) algorithm is united. The Taguchi method is employed to tune the parameters of the two proposed algorithms. Finally, some numerical examples are given to illustrate the applications of the proposed methodologies and to compare their performances.     

From ‘simulation optimization’ to ‘simulation configuration’ of systems

Simulation optimization aims at determining the best values of input parameters, given an output criterion. We address here the much more complex case where design options have to be chosen, which can themselves necessitate choices about other sub-options, as well as numerical parameters. This so-called configuration problem is addressed using simulation associated with a distributed evolutionary algorithm, and is illustrated through an example. This approach allows a large variety of new types of problems to be solved, such as design of complex dynamic systems. In the light of these results, the limits in the optimized configuration of systems are shown to be now determined by the modeling and simulation tools and concepts. In this respect, a possible research direction, based on so-called functional simulation, is discussed and some research perspectives are introduced.     

Heuristic and exact algorithms for the max–min optimization of the multi-scenario knapsack problem

We are concerned with a variation of the standard 0–1 knapsack problem, where the values of items differ under possible S scenarios. By applying the ‘pegging test’ the ordinary knapsack problem can be reduced, often significantly, in size; but this is not directly applicable to our problem. We introduce a kind of surrogate relaxation to derive upper and lower bounds quickly, and show that, with this preprocessing, the similar pegging test can be applied to our problem. The reduced problem can be solved to optimality by the branch-and-bound algorithm. Here, we make use of the surrogate variables to evaluate the upper bound at each branch-and-bound node very quickly by solving a continuous knapsack problem. Through numerical experiments we show that the developed method finds upper and lower bounds of very high accuracy in a few seconds, and solves larger instances to optimality faster than the previously published algorithms.     

Hierarchical optimization of boiler–turbine unit using fuzzy stable model predictive control

This paper develops a hierarchical control system structure based on the Takagi–Sugeno fuzzy model to achieve an optimal control of a boiler–turbine unit. In the upper layer of the hierarchy, an optimal reference governor is designed to find the optimal operating point. A disturbance term is introduced to the fuzzy model to lump the modeling mismatch and unknown disturbance. Thus, the effect of plant behavior variation can be removed and the operating point found can be feasible to control. In the lower layer, a stable model predictive controller is developed to track the optimal set-points while guaranteeing the input-to-state stability of the system. Fuzzy Lyapunov function and appropriate slack and collection matrices are used to reduce the conservatism of stability design and improve the performance. Through the estimation of the disturbance term using an observer, the two layers in the hierarchy are coupled and the integrated system can realize a dynamic optimal control of the boiler–turbine unit, even in the case of severe plant behavior variations.     

The property analysis of evolutionary algorithms applied to spanning tree problems

The search behavior of an evolutionary algorithm depends on the interactions between the encoding that represents candidate solutions to the target problem and the operators that act on that encoding. In this paper, we focus on analyzing some properties such as locality, heritability, population diversity and searching behavior of various decoder-based evolutionary algorithm (EA) frameworks using different encodings, decoders and genetic operators for spanning tree based optimization problems. Although debate still continues on how and why EAs work well, many researchers have observed that EAs perform well when its encoding and operators exhibit good locality, heritability and diversity properties. We analyze these properties of various EA frameworks with two types of analytical ways on different spanning tree problems; static analysis and dynamic analysis, and then visualize them. We also show through this analysis that EA using the Edge Set encoding (ES) and the Edge Window Decoder encoding (EWD) indicate very good locality and heritability as well as very good diversity property. These are put forward as a potential explanation for the recent finding that they can outperform other recent high-performance encodings on the constrained spanning tree problems.     

System identification and control of the ground operationmode of a hybrid aerial–ground robot

This paper presents an in-depth analytical and empirical assessment of the performance of DoubleBee, a novel hybrid aerial–ground robot. Particularly, the dynamic model of the robot with ground contact is analyzed, and the unknown parameters inthe model are identified. We apply an unscented Kalman filter-based approach and a least square-based approach to estimatethe parameters with given measurements and inputs at every time step. Real data are collected and used to estimate theparameters; test data verify that the values obtained are able to model the rotation of the robot accurately. A gain-scheduledfeedback controller is proposed, which leverages the identified model to generate accurate control inputs to drive the systemto the desired states. The system is proven to track a constant-velocity reference signal with bounded error. Simulations andreal-world experiments using the proposed controller show improved performance than the PID-based controller in trackingstep commands and maintaining attitude under robot movement.     

The application of harmonic linearization to optimal control problems†

The method of harmonic linearization is used to obtain an approximate optimal control law for a second-order non-linear state-regulator problem.     

From “Onto-GeoNoesis” to “Onto-Genesis”: The Design of Geographic Ontologies

An important issue in geographic ontological research is the ability to design new ontologies. In this context, we first explore the desiderata of domain ontologies in terms of their constituting elements: i.e., the lexicon, concepts, relations, and axioms. Furthermore, we touch upon several characteristics of geographic concepts, which have puzzled geographic information scientists, and present critical topics of geographic ontological research. Based on the previous aspects of the problem, and guided by prior work of analyzing existent geographic ontologies, we have identified their qualities and deficiencies with regard to completeness and adequacy. This meta-ontological approach has guided us in presenting herein, a framework for generating robust geographic ontologies, which will comply with the semantics of the concepts of the specific domain.     

The active learning hypothesis of the job–demand–control model: an experimental examination

The active learning hypothesis of the job–demand–control model [Karasek, R. A. 1979. “Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign.” Administration Science Quarterly 24: 285–307] proposes positive effects of high job demands and high job control on performance. We conducted a 2 (demands: high vs. low) × 2 (control: high vs. low) experimental office workplace simulation to examine this hypothesis. Since performance during a work simulation is confounded by the boundaries of the demands and control manipulations (e.g. time limits), we used a post-test, in which participants continued working at their task, but without any manipulation of demands and control. This post-test allowed for examining active learning (transfer) effects in an unconfounded fashion. Our results revealed that high demands had a positive effect on quantitative performance, without affecting task accuracy. In contrast, high control resulted in a speed–accuracy tradeoff, that is participants in the high control conditions worked slower but with greater accuracy than participants in the low control conditions.