COMPARATIVE STUDY OF MUTATION OPERATORS IN THE GENETIC ALGORITHMS FOR THE K-MEANS PROBLEM

The k-means problem and the algorithm of the same name are the most commonly used clustering model and algorithm. Being a local search optimization method, the k-means algorithm falls to a local minimum of the objective function (sum of squared errors) and depends on the initial solution which is given or selected randomly. This disadvantage of the algorithm can be avoided by combining this algorithm with more sophisticated methods such as the Variable Neighborhood Search, agglomerative or dissociative heuristic approaches, the genetic algorithms, etc. Aiming at the shortcomings of the k-means algorithm and combining the advantages of the k-means algorithm and rvolutionary approack, a genetic clustering algorithm with the cross-mutation operator was designed. The efficiency of the genetic algorithms with the tournament selection, one-point crossover and various mutation operators (without any mutation operator, with the uniform mutation, DBM mutation and new cross-mutation) are compared on the data sets up to 2 millions of data vectors. We used data from the UCI repository and special data set collected during the testing of the highly reliable semiconductor components. In this paper, we do not discuss the comparative efficiency of the genetic algorithms for the k-means problem in comparison with the other (non-genetic) algorithms as well as the comparative adequacy of the k-means clustering model. Here, we focus on the influence of various mutation operators on the efficiency of the genetic algorithms only

NEW GENETIC ALGORITHM WITH GREEDY HEURISTIC FOR CLUSTERING PROBLEMS WITH UNKNOWN NUMBER OF GROUPS

In this research, we propose new modification of the genetic algorithm withgreedy heuristic witch allows to solve series of clustering problems with unknown number of groups (clusters). The applicability and efficiency of new method is experimentally proved. Achieved results are compared with other algorithms which solve each of problems separately. Experiments show that new algorithm is faster than other methods for various problems

VNS-BASED ALGORITHMS FOR THE CENTROID-BASED CLUSTERING PROBLEM

The k-means algorithm with the corresponding problem formulation is one of the first methods that researchers use when solving a new automatic grouping (clus-tering) problem. Its improvement, modification and combination with other algorithms are described in the works of many researchers. In this research, we propose new al-gorithms of the Greedy Heuristic Method, which use an idea of the search in variable neighborhoods for solving the classical cluster analysis problem, and allows us to obtain a more accurate and stable result of solving in comparison with the known algorithms. Our computational experiments show that the new algorithms allow us to obtain re-sults with better values of the objective function value (sum of squared distances) in comparison with classical algorithms such as k-means, j-means and genetic algorithms on various practically important datasets. In addition, we present the first results for the GPU realization of the Greedy Heuristic Method

A BRANCH-AND-BOUND ALGORITHM FOR A PSEUDO-BOOLEAN OPTIMIZATION PROBLEM WITH BLACK-BOX FUNCTIONS

We consider a conditional pseudo-Boolean optimization problem with both the objective function and all constraint functions given algorithmically (black-box functions) and defined on {0, 1}n only. We suppose that these functions have certain properties, for example, unimodality and monotonicity. To solve problems of this type, we propose an optimization algorithm based on finding boundary points of the feasible region and the branch-and-bound method. The developed algorithm is aimed at the reception of an exact solution of an optimization problem. In addition, this algorithm can be used as an improvement of approximate algorithms such as the greedy heuristic and the random search algorithms for finding boundary points. Even after a small number of iterations (branchings), a significant improvement of the found feasible solution is achieved

A HYBRID DIFFERENTIAL EVOLUTION FOR NON-SMOOTH OPTIMIZATION PROBLEMS

Solving high dimentional, multimodal, non-smooth global optimization problems faces challenges concerning quality of solution, computational costs or even the impossibility of solving the problem. Evolutionary algorithms, in particular, differential evolution algorithm proved itself as good method of global optimization. On the other side, approach based on subgradient methods are good for optimizing non-smooth functions. Combination of these two approaches enables to improve the quality of the algorithm, using the best features of both methods. In this paper, a new hybrid evolutionary approach based on differential evolution and subgradient algorithm as the local search procedure is proposed. Behavior of the proposed SSGDE algorithm was studied in a numerical experiment on three groups of generated tests. Comparison of the new hybrid algorithm with the pure DE approach showed the advantage of the SSGDE. It has been experimentally established that the proposed method finds the global minimum in the best way for all considered dimensions of the problem with respect to the differential evolution method. The SSGDE algorithm showed the best results with a significant increase in the number of functions

A MODELING FRAMEWORK ON DISTANCE PREDICTING FUNCTIONS FOR LOCATION MODELS IN CONTINUOUS SPACE

Continuous location models are the oldest models in locations analysis dealing with the geometrical representations of reality, and they are based on the continuity of location area. The classical model in this area is the Weber problem. Distances in the Weber problem are often taken to be Euclidean distances, but almost all kinds of the distance functions can be employed. In this survey, we examine an important class of distance predicting functions (DPFs) in location problems all of practical relevance. This paper provides a review on recent efforts and development in modeling travel distances based on the coordinates they use and their applicability in certain practical settings. Very little has been done to include special cases of the class of metrics and its classification in location models and such merit further attention. The new metrics are discussed in the well-known Weber problem, its multi-facility case and distance approximation problems. We also analyze a variety of papers related to the literature in order to demonstrate the effectiveness of the taxonomy and to get insights for possible research directions. Research issues which we believe to be worthwhile exploring in the future are also highlighted

Search method for optimal interpolation of thermomechanical coefficients for conventional and low alloy steels

One of the most important factors influencing the behaviour of a metal when subjected to temperature and pressure is the material's ability to resist deformation. So-called thermomechanical coefficients are widely used for mathematical description of this property. These are the ones that determine the direct effect on deformation resistance. There are temperature coefficient (Kt) deformation degree coefficient (Kɛ) and deformation rate coefficient (Ku) describing the influence of temperature, degree of deformation and deformation rate respectively. The situation becomes complicated because the thermomechanical coefficients are not constants. For example, as the temperature increases, the deformation resistance decreases. Also, the deformation resistance increases with increasing degree of deformation and the deformation rate increases with the deformation resistance increases too. The aim of this paper is to find the most accurate method of interpolating thermomechanical coefficients for various steels and alloys using the least squares method

The Automatic Design of Multimode Resonator Topology with Evolutionary Algorithms

Microwave electromagnetic devices have been used for many applications in tropospheric communication, navigation, radar systems, and measurement. The development of the signal preprocessing units including frequency-selective devices (bandpass filters) determines the reliability and usability of such systems. In wireless sensor network nodes, filters with microstrip resonators are widely used to improve the out-of-band suppression and frequency selectivity. Filters based on multimode microstrip resonators have an order that determines their frequency-selective properties, which is a multiple of the number of resonators. That enables us to reduce the size of systems without deteriorating their selective properties. Various microstrip multimode resonator topologies can be used for both filters and microwave sensors, however, the quality criteria for them may differ. The development of every resonator topology is time consuming. We propose a technique for the automatic generation of the resonator topology with required frequency characteristics based on the use of evolutionary algorithms. The topology is encoded into a set of real valued parameters, which are varied to achieve the desired features. The differential evolution algorithm and the genetic algorithm with simulated binary crossover and polynomial mutation are applied to solve the formulated problem using the dynamic penalties method. The experimental results show that our technique enables us to find microstrip resonator topologies with desired amplitude-frequency characteristics automatically, and manufactured devices demonstrate characteristics very close to the results of the algorithm. The proposed algorithmic approach may be used for automatically exploring the new perspective topologies of resonators used in microwave filters, radar antennas or sensors, in accordance with the defined criteria and constraints

Commuting Outer Inverse-Based Solutions to the Yang–Baxter-like Matrix Equation

This paper investigates new solution sets for the Yang–Baxter-like (YB-like) matrix equation involving constant entries or rational functional entries over complex numbers. Towards this aim, first, we introduce and characterize an essential class of generalized outer inverses (termed as {2,5}-inverses) of a matrix, which commute with it. This class of {2,5}-inverses is defined based on resolving appropriate matrix equations and inner inverses. In general, solutions to such matrix equations represent optimization problems and require the minimization of corresponding matrix norms. We decided to analytically extend the obtained results to the derivation of explicit formulae for solving the YB-like matrix equation. Furthermore, algorithms for computing the solutions are developed corresponding to the suggested methods in some computer algebra systems. The main features of the proposed approach are highlighted and illustrated by numerical experiments