Informatica

A new population-based metaheuristic called Zebrafish Optimization Algorithm (ZFO) is proposed to find the global optimum solution using foraging behaviour of zebrafish larvae (Danio rerio). In ZFO, an exploration and exploitation trade-off is realized by simulation of exploratory dispersion, collaborative shoaling and directional searching. The movement strategy incorporates a stagnation-avoiding reinitialization mechanism, ensuring that diversity is maintained and premature convergence is avoided. To demonstrate the efficiency of ZFO, 23 benchmark functions including unimodal, multimodal and fixed dimension multimodal functions are tested and their optimal solutions are searched. Results obtained by ZFO are compared with existing metaheuristic algorithms, namely Cuttlefish Optimization, Jellyfish Search, Pufferfish Optimizer and Krill Herd algorithm. The Friedman rank test and Wilcoxon signed-rank test show that ZFO yields better-quality solutions than the compared algorithms in terms of convergence speed and robustness. The novel characteristic of ZFO is that it combines biologically inspired zebrafish dynamics and adaptive diversification strategy to enhance the capability of global search.

In the discrete form of multi-criteria decision-making (MCDM) problems, we are usually confronted with a decision-matrix formed from the information of some alternatives on some criteria. In this study, a new method is proposed for simultaneous evaluation of criteria and alternatives (SECA) in an MCDM problem. For making this type of evaluation, a multi-objective non-linear programming model is formulated. The model is based on maximization of the overall performance of alternatives with consideration of the variation information of decision-matrix within and between criteria. The standard deviation is used to measure the within-criterion, and the correlation is utilized to consider the between-criterion variation information. By solving the multi-objective model, we can determine the overall performance scores of alternatives and the objective weights of criteria simultaneously. To validate the proposed method, a numerical example is used, and three analyses are made. Firstly, we analyse the objective weights determined by the method, secondly, the stability of the performance scores and ranking results are examined, and finally, the ranking results of the proposed method are compared with those of some existing MCDM methods. The results of the analyses show that the proposed method is efficient to deal with MCDM problems.

A relevant challenge introduced by decentralized installations of photo-voltaic systems is the mismatch between green energy production and the load curve for domestic use. We advanced an ICT solution that maximizes the self-consumption by an intelligent scheduling of appliances. The predictive approach is complemented with a reactive one to minimize the short term effects due to prediction errors and to unforeseen loads. Using real measures, we demonstrated that such errors can be compensated modulating the usage of continuously running devices such as fridges and heat-pumps. Linear programming is used to dynamically compute in real-time the optimal control of these devices.

In this study, we evaluated the effects of the normalization procedures on decision outcomes of a given MADM method. For this aim, using the weights of a number of attributes calculated from FAHP method, we applied TOPSIS method to evaluate the financial performances of 13 Turkish deposit banks. In doing this, we used the most popular four normalization procedures. Our study revealed that vector normalization procedure, which is mostly used in the TOPSIS method by default, generated the most consistent results. Among the linear normalization procedures, max-min and max methods appeared as the possible alternatives to the vector normalization procedure.

Fingerprint ridge frequency is a global feature, which is most prominently different in fingerprints of men and woman, and it also changes within the maturing period of a person. This paper proposes the method of fingerprint pre-classification, based on the ridge frequency replacement by the density of edge points of the ridge boundary. This method is to be used after applying the common steps in most fingerprint matching algorithms, namely the fingerprint image filtering, binarization and marking of good/bad image areas. The experimental performance evaluation of fingerprint pre-classification is presented. We have found that fingerprint pre-classification using the fingerprint ridge edges density is possible, and it enables to preliminary reject part of the fingerprints without heavy loss of the recognition quality. The paper presents the evaluation of two sources of fingerprint ridge edges density variability: a) different finger pressure during the fingerprint scanning, b) different distance between the geometrical center of the fingerprint and position of the fingerprint fragment.