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1
Content available remote Agricultural System Modelling with Ant Colony Optimization
Fidanova Stefka, Dimov Ivan, Angelova Denitsa, Ganzha Maria
Annals of Computer Science and Information Systems
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2022
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Vol. 30
329--332
EN
Cereals contribute significantly to humanity's livelihood. They are a source of more food energy worldwide than any other group of crops. Their production contributes considerably to the total global anthropogenic greenhouse gas (GHGs) emissions. In this study we propose a basic bio-economic farm model (BEFM) solved with the help of Ant Colony Optimization (ACO) methodology. We aim to assess farm profits and risks considering various types of policy incentives and adverse weather events. The proposed model can be applied to any annual crop.
2
Content available remote Optimized Stochastic Approach for Integral Equations
Todorov Venelin, Fidanova Stefka, Dimov Ivan, Georgieva Rayna
Annals of Computer Science and Information Systems
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2021
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Vol. 25
239--242
EN
An optimized Monte Carlo approach (OPTIMIZED MC) for a Fredholm integral equations of the second kind is presented and discussed in the present paper. Numerical examples and results are discussed and MC algorithms with various initial and transition probabilities are compared.
3
Content available remote Optimized Nano Grid Approach for Small Critical Loads
Todorov Daniel, Todorov Venelin, Fidanova Stefka
Annals of Computer Science and Information Systems
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2021
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Vol. 26
81--84
EN
This study is focused on the possibility of utilizing solar energy nano grids for feeding small scale critical loads. The reasons conditioning this necessity are reviewed and the strengths of small scale micro grids over the centralized type of powering are stated. On the basis of studies on renewable energy sources, photovoltaic converters are pointed to be most appropriate for small scale generation in urban conditions at specific geographical region. The loads of typical traffic lights show that they are relatively constant, which makes such consumers very suitable for micro or nano grids.
4
Content available remote Optimized Method based on Lattice Sequences for Multidimensional Integrals in Neural Networks
Todorov Venelin, Dimov Ivan, Fidanova Stefka
Annals of Computer Science and Information Systems
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2021
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Vol. 25
243--246
EN
In this work we investigate advanced stochastic methods for solving a specific multidimensional problem related to neural networks. Monte Carlo and quasi-Monte Carlo techniques have been developed over many years in a range of different fields, but have only recently been applied to the problems in neural networks. As well as providing a consistent framework for statistical pattern recognition, the stochastic approach offers a number of practical advantages including a solution to the problem for higher dimensions. For the first time multidimensional integrals up to 100 dimensions related to this area will be discussed in our numerical study.
5
Content available remote Optimized lattice rule and adaptive approach for multidimensional integrals with applications
Todorov Venelin, Dimov Ivan, Fidanova Stefka, Poryazov Stoyan
Annals of Computer Science and Information Systems
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2021
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Vol. 26
75--80
EN
In this work we make a comparison between optimized lattice and adaptive stochastic approaches for multidimensional integrals with different dimensions. Some of the integrals has applications in environmental safety and control theory.
6
Content available remote Multicriterial evaluation and optimization of an algorithm for charging energy storage elements
Kishkin Krasimir, Arnaudov Dimitar, Todorov Venelin, Fidanova Stefka
Annals of Computer Science and Information Systems
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2021
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Vol. 26
61--63
EN
This study compares optimized active voltage balancing algorithms, applicable for energy storage systems made of supercapacitor cells connected in series. The results presented herein are obtained from a simulation model and confirmed on an experimental stand.
7
Content available remote InterCriteria Analyzis of Hybrid Ant Colony Optimization Algorithm for Multiple Knapsack Problem
Fidanova Stefka, Ganzha Maria, Roeva Olympia
Annals of Computer Science and Information Systems
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2021
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Vol. 25
173--180
EN
The local search procedure is a method for hybridization and improvement of the main algorithm, when complex problems are solved. It helps to avoid local optimums and to find faster the global one. In this paper we apply InterCriteria analysis (ICrA) on hybrid Ant Colony Optimization (ACO) algorithm for Multiple Knapsack Problem (MKP). The aim is to study the algorithm behavior comparing with traditional ACO algorithm. Based on the obtained numerical results and on the ICrA approach the efficiency and effectiveness of the proposed local search procedure are confirmed.
8
Content available remote An Optimized Technique for Wigner Kernel Estimation
Todorov Venelin, Fidanova Stefka, Dimov Ivan, Poryazov Stoyan
Annals of Computer Science and Information Systems
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2021
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Vol. 25
235--238
EN
We study an optimized Adaptive Monte Carlo algorithm for the Wigner kernel- an important problem in quantum mechanics. We will compare the results with the basic adaptive approach and other stochastic approaches for computing the Wigner kernel represented by difficult multidimensional integrals in dimension d up to 12. The higher cases d > 12 will be considered for the first time. A comprehensive study and an analysis of the computational complexity of the optimized Adaptive MC algorithm under consideration has also been presented.
9
Content available remote An Optimized Stochastic Techniques related to Option Pricing
Todorov Venelin, Dimov Ivan, Fidanova Stefka, Apostolov Stoyan
Annals of Computer Science and Information Systems
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2021
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Vol. 25
247--250
EN
Recently stochastic methods have become very important tool for high performance computing of very high dimensional problems in computational finance. The advantages and disadvantages of the different highly efficient stochastic methods for multidimensional integrals related to evaluation of European style options will be analyzed. Multidimensional integrals up to 100 dimensions related to European options will be computed with highly efficient optimized lattice rules.
10
Content available remote Fast BF-ICrA Method for the Evaluation of MO-ACO Algorithm for WSN Layout
Dezert Jean, Fidanova Stefka, Tchamova Albena
Annals of Computer Science and Information Systems
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2020
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Vol. 21
241--249
EN
In this paper, we present a fast Belief Function based Inter-Criteria Analysis (BF-ICrA) method based on the canonical decomposition of basic belief assignments defined on a dichotomous frame of discernment. This new method is then applied for evaluating the Multiple-Objective Ant Colony Optimization (MO-ACO) algorithm for Wireless Sensor Networks (WSN) deployment.
11
Content available remote Ant Colony Optimization Algorithm for Fuzzy Transport Modelling
Fidanova Stefka, Roeva Olympia, Ganzha Maria
Annals of Computer Science and Information Systems
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2020
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Vol. 21
237--240
EN
Public transport plays an important role in our live. The good service is very important. Up to 1000 km, trains and buses play the main role in the public transport. The number of the people and which kind of transport they prefer is important information for transport operators. In this paper is proposed algorithm for transport modelling and passenger flow, based on Ant Colony Optimization method. The problem is described as multi-objective optimization problem. There are two optimization purposes: minimal transportation time and minimal price. Some fuzzy element is included. When the price is in a predefined interval it is considered the same. Similar for the starting traveling time. The aim is to show how many passengers will prefer train and how many will prefer buses according their preferences, the price or the time.
12
Content available remote A Two-Stage Monte Carlo Approach for Optimization of Bimetallic Nanostructures
Mikhov Rossen, Myasnichenko Vladimir, Kirilov Leoneed, Sdobnyakov Nickolay, Matrenin Pavel, Sokolov Denis, Fidanova Stefka
Annals of Computer Science and Information Systems
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2020
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Vol. 21
285--288
EN
In this paper we propose a two-stage lattice Monte Carlo approach for optimization of bimetallic nanoalloys: simulated annealing on a larger lattice, followed by simulated diffusion. Both algorithms are fairly similar in structure, but their combination was found to give significantly better solutions than simulated annealing alone. We also discuss how to tune the parameters of the algorithms so that they work together optimally.
13
Content available remote A New Optimized Stochastic Approach for Multidimensional Integrals in Machine Learning
Todorov Venelin, Apostolov Stoyan, Dimov Ivan, Fidanova Stefka
Annals of Computer Science and Information Systems
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2020
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Vol. 21
337--340
EN
Stochastic techniques have been developed over many years in a range of different fields, but have only recently been applied to the problems in machine learning. A fundamental problem in this area is the accurate evaluation of multidimensional integrals. An introduction to the theory of the stochastic optimal generating vectors has been given. A new optimized lattice sequence with a special choice of the optimal generating vector have been applied to compute multidimensional integrals up to 30-dimensions. Clearly, the progress in the area of machine learning is closely related to the progress in reliable algorithms for multidimensional integration.
14
Content available remote A New Optimized Adaptive Approach for Estimation of the Wigner Kernel
Todorov Venelin, Fidanova Stefka, Dimov Ivan, Poryazov Stoyan
Annals of Computer Science and Information Systems
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2020
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Vol. 21
341--344
EN
In this paper we study numerically an optimized Adaptive Monte Carlo algorithm for the Wigner kernel - an important problem in quantum mechanics represented by difficult multidimensional integrals. We will show the advantages of the optimized Adaptive MC algorithm and compare the results with the Adaptive approach from our previous work [4] and other stochastic approaches for computing the Wigner kernel in 3,6,9-dimensional case. The 12-dimensional case will be considered for the first time. A comprehensive study and an analysis of the computational complexity of the optimized Adaptive MC algorithm under consideration has also been presented.
15
Content available remote Ant colony optimization algorithm for workforce planning: influence of the evaporation parameter
Fidanova Stefka, Luque Gabriel, Roeva Olympia, Ganzha Maria
Annals of Computer Science and Information Systems
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2019
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Vol. 18
177--181
EN
Optimization of the production process is important for every factory or organization. The better organization can be done by optimization of the workforce planing. The main goal is decreasing the assignment cost of the workers with the help of which, the work will be done. The problem is NP-hard, therefore it can be solved with algorithms coming from artificial intelligence. The problem is to select employers and to assign them to the jobs to be performed. The constraints of this problem are very strong and for the algorithms is difficult to find feasible solutions. We apply Ant Colony Optimization Algorithm to solve the problem. We investigate the algorithm performance according evaporation parameter. The aim is to find the best parameter setting.
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