### Article

## Intelligent virtual reference feedback tuning and its application to heat treatment electric furnace control

Virtual Reference Feedback Tuning (VRFT) is a data-driven one-shot control method which is very attractive for engineering applications. However, it cannot design controllers with the optimal control performance based on the standard VRFT approach as performance indices are not explicitly represented in its objective function. To deal with this problem, this paper presents a novel intelligent VRFT (IVRFT) based on adaptive binary ant system harmony search (ABASHS) where the reference model of VRFT, which potentially determines the control performance, is coordinately optimized with the controller by ABASHS to achieve the best control performance. Finally, the proposed ABASHS-based intelligent virtual reference feedback tuning (ABASHS-IVRFT) method is applied to the temperature control of the heat treatment electric furnace. The simulation results demonstrate that ABASHS-IVRFT is valid and can implement the optimal non-overshoot control easily and efficiently. Considering the characteristics such as ease of implementation and no need of the model information of controlled objects, ABASHS-IVRFT is a promising approach for engineering applications.

The companies that are IT-industry leaders perform from several tens to several hundreds of projects simultaneously. The main problem is to decide whether the project is acceptable to the current strategic goals and resource limits of a company or not. This leads firms to an issue of a project portfolio formation; therefore, the challenge is to choose the subset of all projects which satisfy the strategic objectives of a company in the best way. In this present article we propose the multi-objective mathematical model of the project portfolio formation problem, defined on the fuzzy trapezoidal numbers. We provide an overview of methods for solving this problem, which are a branch and bound approach, an adaptive parameter variation scheme based on the epsilon-constraint method, ant colony optimization method and genetic algorithm. After analysis, we choose ant colony optimization method and SPEA II method, which is a modification of a genetic algorithm. We describe the implementation of these methods applied to the project portfolio formation problem. The ant colony optimization is based on the max min ant system with one pheromone structure and one ant colony. Three modification of our SPEA II implementation were considered. The first adaptation uses the binary tournament selection, while the second requires the rank selection method. The last one is based on another variant of generating initial population. The part of the population is generated by a non-random manner on the basis of solving a one-criterion optimization problem. This fact makes the population more strongly than an initial population, which is generated completely by random. Comparing of ant colony optimization algorithm and three modifications of a genetic algorithm was performed. We use the following parameters: speed of execution and the C-metric between each pair of algorithms. Genetic algorithm with non-random initial population show better results than other methods. Thus, we propose using this algorithm for solving project portfolio formation problem.

In this paper we consider application of ant colony optimization techniques for capacitated vehicle routing problem. Modified ant colony optimization algorithm is proposed, computational results are reported.

This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.

The companies that are IT-industry leaders perform from several tens to several hundreds of projects simultaneously. The main problem is to decide whether the project is acceptable to the current strategic goals and resource limits of a company or not. This leads firms to an issue of a project portfolio selection; therefore, the challenge is to choose the subset of all projects which satisfy the strategic objectives of a company in the best way. In this present article we propose the multi-objective mathematical model of the project portfolio selection problem, defined on the fuzzy trapezoidal numbers. We provide an overview of methods for solving this problem, which are a branch and bound approach, an adaptive parameter variation scheme based on the epsilon-constraint method, ant colony optimization method and genetic algorithm. After analysis, we choose ant colony optimization method and SPEA II method, which is a modification of a genetic algorithm. We describe the implementation of these methods applied to the project portfolio selection problem. The ant colony optimization is based on the max min ant system with one pheromone structure and one ant colony. Three modification of our SPEA II implementation were considered. The first adaptation uses the binary tournament selection, while the second requires the rank selection method. The last one is based on another variant of generating initial population. The part of the population is generated by a non-random manner on the basis of solving a one-criterion optimization problem. This fact makes the population more strongly than an initial population, which is generated completely by random.

Motor fuel distribution problem is considered. Accepting some assumptions it can be reduced to a well-known vechicle routing problem with capacity constraints. Ant colony optimization approach is suggested for solving CVRP. Modified ant algorithms are performed. Computational results for some benchmarks are given in compare with classical ant algorithms.

A form for an unbiased estimate of the coefficient of determination of a linear regression model is obtained. It is calculated by using a sample from a multivariate normal distribution. This estimate is proposed as an alternative criterion for a choice of regression factors.