Krzysztof Trojanowski

• PhD habil. eng.
• Head of Department at Cardinal Stefan Wyszyński University in Warsaw

Unmanned Aerial Vehicles (UAVs) can aid rescue workers during operational emergency response procedures in tasks such as communication delivery or aerial reconnaissance of the area. Before a UAV team starts operating in the natural environment, multiple simulations aimed at experimental verification of their paths’ effectiveness are necessary. Simu...

Determining paths for a team of Unmanned Aerial Vehicles (UAVs) that pass over a disaster area for reconnaissance and communication delivery for ground users is a subject of our research. It is assumed that the location of disaster victims is unknown because there is no contact with them. However, we have some statistical information about populati...

When ad-hoc connectivity for a group of ground users has to be delivered, one can use a network of Unmanned Aerial Vehicles (UAV) equipped with Mobile Base Stations (MBS). In this research, we minimize the number of UAVs by effectively deploying UAVs over the zone where users are located. The proposed model divides zone into sectors of different ar...

Sensor network lifetime maximization can be solved using heuristic methods, but they produce only suboptimal sensor activity schedules. However, knowing the quality of these solutions, we can use methods for solving decision problems to find better solutions than these suboptimal ones. We apply an answer set programming (ASP) system to answer the q...

Unmanned aerial vehicles (UAVs) with base stations can deliver communication and support services in emergency conditions. Their positions over, e.g., a disaster or festive area, are of utmost importance for ground users’ connectivity quality. We propose a new model of UAVs deployment optimization problem to minimize the number of UAVs used to prov...

The Maximum Lifetime Coverage Problem (MLCP) requires heuristic optimization methods due to its complexity. A real-world problem model determines how a solution is represented and the operators applied in these heuristics. Our paper describes adapting a local search scheme and its operators to MLCP optimization. The operators originate from three l...

We study runtime adaptation methods of sensor activity schedules for wireless sensor networks. Adaptation is necessary when the network operating conditions differ from the ones assumed in the scheduling phase. Usually, the ideal temperature conditions are assumed. When the system has to operate at a lower temperature, sensor batteries discharge fa...

In Particle Swarm Optimization, the behavior of particles depends on the parameters of movement formulas. In our research, we identify types of particles based on their movement trajectories. Then, we propose new rules of particle classification based on the two attributes of the measure representing the minimum number of steps necessary for the ex...

In this paper, we study run-time adaptation methods of a schedule of sensor activity generated for ideal temperature conditions. Such a schedule cannot be completed in low-temperature conditions due to a shorter lifetime of sensor batteries. We proposed several methods of selecting the next slot to be executed when the currently scheduled slot is u...

In this paper, we study relative performance of local search methods used for the Maximum Lifetime Coverage Problem (MLCP) solving. We consider nine algorithms obtained by swapping problem-specific major steps between three local search algorithms we proposed earlier: LS\(_{\mathrm {HMA}}\), LS\(_{\mathrm {CAIA}}\), and LS\(_{\mathrm {RFTA}}\). A l...

A problem of the sensor network lifetime maximization is typically solved for a fixed temperature, which means that the sensor battery performance is constant over the network time. However, networks usually have to operate in the varying temperature conditions, for example, outdoors, or in unheated rooms. The operating temperature variations influ...

Wireless Sensor Networks (WSNs) consist of a set of devices with limited energy capacity, therefore the longevity of the network can be one of the decisive quality parameters of WSNs. We study the problem of WSN lifetime maximization for a model of the network where sensors are randomly deployed over an area to keep watch on a number of points of i...

Stability properties of a particle in the stochastic models of PSO are a subject of presented analysis. Measures of a number of particle steps necessary to reach an equilibrium state, particularly, generalized weak versions of measures: particle convergence expected time (pcet) and the particle location variance convergence time (pvct) are develope...

Convergence properties in the model of PSO with inertia weight are a subject of analysis. Particularly, we are interested in estimating the time necessary for a particle to obtain equilibrium state in deterministic and stochastic models. For the deterministic model, an earlier defined upper bound of particle convergence time (pctb) is revised and u...

A property of particles in Particle Swarm Optimization (PSO), namely, particle convergence time (pct) is a subject of theoretical and experimental analysis. For the model of PSO with inertia weight a new measure for evaluation of pct is proposed. The measure evaluates number of steps necessary for a particle to obtain a stable state defined with an...

Convergence properties of a particle and a swarm decide about their performance. Particularly, one of these properties is a time, that is, a number of particle steps necessary to reach an equilibrium state. This is a subject of presented analysis. Generalized weak versions of measures: particle convergence expected time (pcet) and the particle loca...

A sensor power schedule for a homogenous network of sensors with a limited battery capacity monitoring a set of points of interest (POI) depends on locations of POIs and sensors, monitoring range and battery lifetimes. A good schedule keeps the network operational as long as possible while maintaining the required level of coverage (not all POIs ha...

Control of a set of sensors disseminated in the environment to monitor activity is a subject of the presented research. Due to redundancy in the areas covered by sensor monitoring ranges a satisfying level of coverage can be obtained even if not all the sensors are on. Sleeping sensors save their energy, thus, one can propose schedules defining act...

Theoretical properties of particle swarm optimization approach with inertia weight are investigated. Particularly, we focus on the convergence analysis of the expected value of the particle location and the variance of the location. Four new measures of the expected particle convergence time are defined: (1) convergence of the expected location of...

Particle Swarm Optimization (PSO) is a powerful heuristic optimization method being subject of continuous interest. Theoretical analysis of its properties concerns primarily the conditions necessary for guaranteeing its convergent behaviour. Particle behaviour depends on three groups of parameters: values of factors in a velocity update rule, initi...

In our paper selected linguistic features of genomes to study the statistics of the gene codes are considered. We present the information theory from which it follows that if the system is described by distributions of hyperbolic type it leads to the possibility of entropy loss and stability. We show that the histograms of gene lengths are similar...

A large number of heuristic optimization algorithms for dynamic optimization has already been proposed. The aim of this chapter is to discuss methods of evaluating their efficiency. Thus, the following issues have to be considered: (i) measures for performance and associated measurement methods, (ii) dynamic benchmarks and different types for imple...

Zipf's law implies the statistical distributions of hyperbolic type, which can describe the properties of stability and entropy loss in linguistics. We present the information theory from which follows that if the system is described by distributions of hyperbolic type it leads to the possibility of entropy loss. We present the number of repetition...

In this paper, we study the influence of the constrained computational resources on the expected value of the algorithm outcome. In our case, time is the limited resource, that is, the search process can be interrupted in any moment by the user who requests the current best solution. Different characteristics of the user behavior are discussed and...

In this paper we report on an ongoing research project aiming at evaluation of the hypothesis of stabilization of Web user segmentation via cross site information exchange. We check stability of user membership in segments derived at various points of time from the content of sites they visit. If it is true that users of the same service share segm...

This work presents a differential evolution (DE) algorithm equipped with a new perturbation operator applied for dynamic optimization. The selected version of DE, namely the jDE algorithm has been extended by a new type of mutation mechanism which employs random variates controlled by the a-stable distribution. Precisely, in the modified version of...

In this paper an adaptive differential evolution approach for dynamic optimization problems is studied. A new benchmark suite Syringa is also presented. The suite allows to generate test-cases from a multiple number of dynamic optimization classes. Two dynamic benchmarks: Generalized Dynamic Benchmark Generator (GDBG) and Moving Peaks Benchmark (MP...

A novel method of text categorization for Polish language documents, based on Polish Wikipedia resources is presented. The distinctive feature of the approach is that document labelling can be performed with no additional categorized corpora. Experiments with two different types of document semantic disambiguation have been performed, and evaluated...

This paper studies properties of a differential evolution approach (DE) for dynamic optimization problems. An adaptive version of DE, namely the jDE algorithm has been applied to two well known benchmarks: Generalized Dynamic Benchmark Generator (GDBG) and Moving Peaks Benchmark (MPB). The experiments have been performed for different numbers of th...

Recent research in applications of modern meta-heuristics to non-stationary opti- mization tasks showed high efficiency of multi-swarm approach. In this paper the multi-swarm approach extended with memory structure is discussed. The struc- ture stores complete solutions found during the search process and is common for all the sub-swarms. The algor...

The main problem with biologically inspired algorithms (like evolutionary algorithms or particle swarm optimization) when applied to dynamic optimization is to force their readiness for continuous search for new optima occurring in changing locations. Immune-based algorithm, being an instance of an algorithm that adapt by innovation seem to be a pe...

This paper studies properties of a multi-swarm system based on a concept of physical quantum particles (mQSO). Quantum particles differ from the classic ones in the way they move. As opposed to the classic view of particle movement, where motion is controlled by linear kinematic laws, quantum particles change their location according to random dist...

Non-stationary optimization with the immune based algorithms is studied in this paper. The algorithm works with a binary representation of solu-tions. A set of different types of binary mutation is proposed and experimentally verified. The mutations differ in the way of calculation of the number of bits to be mutated. Obtained results allow to indi...

This paper studies properties of quantum particles rules of movement in particle swarm optimization (PSO) for non-stationary optimization tasks. A multi-swarm approach based on two types of particles: neutral and quantum ones is a framework of the experimental research. A new method of generation of new location candidates for quantum particles is...

This paper presents research considering mixed multi-swarm optimization approach applied to dynamic environments. One of the versions of this approach, called mQSO is a subject of our special interest. The mQSO algorithm works with a set of particles divided into sub-swarms where every sub-swarm consists of two types of particles: classic and quant...

Heuristic approaches already proved their efficiency for the cases where real-world problems dynamically change in time and there is no effective way of prediction of the changes. Among them a mixed multi-swarm optimization (mSO) is regarded as the most efficient. The approach is a hybrid solution and it is based on two types of particle swarm opti...

Mammalian immune system and especially clonal selection principle, responsible for coping with external intruders, is an inspiration for a set of heuristic optimization algorithms. In this paper we focus our attention on an instance of a clonal selection algorithm called BCA. This algorithm admits very good exploratory abilities when solving statio...

Efficiency of the B-Cell algorithm applied to one of the well-known test-case generators, the moving peaks benchmark (MPB) is a subject of study presented in this paper. We especially focused on the family of fitness landscapes generated by scenario 2 of the MPB. All of them represent the class of randomly changing environments. Some properties of...

Efficiency of two mutation operators applied in a clonal selection based optimization algorithm AIIA for non-stationary tasks is investigated. In both operators traditional Gaussian random number generator was exchanged by α-stable random number generator and thus α became one of the parameters of the algorithm. Obtained results showed that appropr...

Non-stationary optimization of randomly changing environments is a subject of unfading interest. In this paper we study application of multipopulation evolutionary algorithm to this problem. Presented algorithm works with a set of sub-populations managed by the mechanism of exclusion coming from the multiswarm version of particle swarm approach. Th...

Mammalian immune system and especially clonal selection principle, responsible for coping with external intruders, is an inspiration for a set of heuristic optimization algorithms. Below, a few of them are compared on a set of nonstationary optimization benchmarks. One of the algorithms is our proposal, called AIIA (Artificial Immune Iterated Algor...

Most real-world applications operate in dynamic environments. In such environments often it is necessary to modify the current solution due to various changes in the environment (e.g., machine breakdowns, sickness of employees, etc). Thus it is important to investigate properties of adaptive algorithms which do not require re-start every time a cha...

A new specification of an immune network system is proposed. The model works on a set of antibodies from the binary shape-space and it is able to build a stable network and learn new patterns as well. A set of rules based on diversity of the repertoire of patterns which control relations of stimulation and suppression is proposed. The model is desc...

Algorytmy ewolucyjne (AE) są narzędziem bardzo często wy-korzystywanym do rozwiązywania zadań optymalizacyjnych. W szczególnym stopniu ich przydatność uwidacznia się w przypadkach zadań trudnych, gdy inne metody nie dają zadawalających rezultatów. Poszukiwanie optymalnych rozwiązań zadań niestacjonarnych, o parametrach zmiennych w czasie jest jedny...

This volume contains selected papers, presented at the international conference on Intelligent Information Processing and Web Mining Conference IIS:IIPWM'06, organized in Ustro« (Poland) on June 19-22nd, 2006. The submitted papers cover new computing paradigms, among others in biologically motivated methods, advanced data analysis, new machine lear...

A large group of real–world problems are non–stationary optimization tasks, i.e. prob-lems where the environment is varying in time. In this case, a recently found optimal solu-tion can stop being an optimum in the next moment because of the changes that appear in the environment. Therefore in non–stationary environment the optimum has to be search...

Heuristic optimisation techniques, especially evolutionary algorithms were successfully applied to non-stationary optimisation tasks. One of the most important conclusions for the evolutionary approach was a three-population architecture of the algorithm, where one population plays the role of a memory while the two others are used in the searching...

This edited book contains articles accepted for presentation during The Intelligent Information Processing and Web Mining Conference IIS:IIP WM¿04 held in Zakopane, Poland, on May 17-20, 2004. Considerable attention is devoted to the newest developments in the area of Artificial Intelligence with special calls for contributions on Web mining. This...

In this paper an idea of the artificial immune system was used to design an algorithm for non-stationary function optimization. The unknown and varying in time optimum is treated here as an antigen and the aim of the system is to produce antibodies. Three different strategies awarding memory cells are investigated.

This edited book contains articles accepted for presentation during The Intelligent Information Processing and Web Mining Conference IIS:IIPWM´03 held in Zakopane, Poland, on June 2-5, 2003. A lot of attention is devoted to the newest developments in the area of Artificial Intelligence with special calls for contributions on artificial immune syste...

In this paper we discuss forms of data storage in knowledge systems. A set of selected software systems is analysed. Then a new system called InlenStar is briefly presented and its structure of database is widely discussed, as an example of the structure of knowledge database.

In this paper an idea of the artificial immune system was used to design an algorithm for non-stationary function optimization. It was demonstrated that in the case of periodic function changes the algorithm constructively builds and uses immune memory. This result was contrasted with cases when no periodic changes occur. Further, an attempt toward...

The aim of this paper is to study the problem of optimization of non-stationary problems with evolutionary algorithms. Obtained solutions have to satisfy different demands than with problems static in time, so the approach to this class of problems has to be different. In this paper we present a review of measures for the obtained results. Some new...

We consider the problem of how to measure a “correlation” (or interdependence) between two survey-questionnaire questions. In the real-life questionnaires it is usual to offer several options-answers for most of the questions. Some (or many) questions give the possibility to choose more than one answer. There are very few statistical methods that e...

As most real-world problemas are dynamic, it is not sufficient to "solve" the problem for the some (current) scenario, but it is also necessary to modify the current solution due to various changes in the environment (e. g., machine breakdowns, sickness of employees, etc.). Thus it is important to investigate properties of adaptive algorithms which...

Evolutionary real-time optimization system for ecological power control for the area of Poland is presented. We describe the modeling issues of the problem, provide discussion on data used for the developed system, and introduce statistical methodology incorporated in the system. Finally, we describe evolutionary optimization system EROS and some e...

Application of evolutionary algorithms to non-stationary problems is the subject of research discussed. We extended evolutionary algorithm by two mechanisms dedicated to non-stationary optimization: redundant genetic memory structures and a particular diversity maintenance technique-random immigrants mechanism. We made experiments with evolutionary...

Evolutionary algorithms (EAs), which are based on a powerful principle of evolution: survival of the fittest, and which model some natural phenomena: genetic inheritance and Darwinian strife for survival, constitute an interesting category of modern heuristic search. During the last two decades there has been a growing interest in these algorithms;...

In this paper we investigate the issue of adding problem-specific knowledge to evolutionary systems; we compare also evolutionary systems versus nonevolutionary ones. The discussion is based on example of path-planning and navigation problems; we have also used Evolutionary Planner/Navigator for various experiments. Keywords: path planning and navi...

The integration of evolutionary approaches with adaptive memory processes is emerging as a promising new area for research and practical applications. In this paper, we report our study on adding memory to the Evolutionary Planner/Navigator (EP/N), which is an adaptive planning/navigation system for mobile robots based on evolutionary computation....

Based on evolutionary computation (EC) concepts, we developed an adaptive evolutionary planner/navigator (EP/N) as a novel approach to path planning and navigation. The EP/N is characterized by generality, flexibility, and adaptability. It unifies off-line planning and online planning/navigation processes in the same evolutionary algorithm which 1)...

. The field of evolutionary computation has been growing rapidlyover the last few years. Yet, there are still many gaps to be filled, manyexperiments to be done, many questions to be answered. In this paperwe examine a few important directions in which we can expect a lot ofactivities and significant results; we discuss them from a general perspect...