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Differential evolution and large-scale optimization applications
Aleš Zamuda, znanstveni film, znanstvena zvočna ali video publikacija

Opis: Differential Evolution (DE) is one of the most popular, high-performance optimization algorithms with variants that have been outperforming others for years. As a result, DE has grown to accommodate wide usage for a variety of disciplines across scientific fields. Differential Evolution and Large-Scale Optimization Applications presents a research-based overview and cross-disciplinary applications of optimization algorithms. Emphasizing applications of Differential Evolution (DE) across sectors and laying the foundation for further use of DE algorithms in real-world settings, this video is an essential resource for researchers, engineers, and graduate-level students. Topics Covered : Algorithms, Optimization, Parallel Differential Evolution, Performance Improvement, Stochastic Methods, Tree Model Reconstruction.
Ključne besede: differential Evolution, optimization, algorithms, stochastic methods, tree models, tree model reconstruction
Objavljeno v DKUM: 14.05.2019; Ogledov: 1720; Prenosov: 219
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Multi-objective optimization algorithms with the island metaheuristic for effective project management problem solving
Christina Brester, Ivan Ryzhikov, Eugene Semenkin, 2017, izvirni znanstveni članek

Opis: Background and Purpose: In every organization, project management raises many different decision-making problems, a large proportion of which can be efficiently solved using specific decision-making support systems. Yet such kinds of problems are always a challenge since there is no time-efficient or computationally efficient algorithm to solve them as a result of their complexity. In this study, we consider the problem of optimal financial investment. In our solution, we take into account the following organizational resource and project characteristics: profits, costs and risks. Design/Methodology/Approach: The decision-making problem is reduced to a multi-criteria 0-1 knapsack problem. This implies that we need to find a non-dominated set of alternative solutions, which are a trade-off between maximizing incomes and minimizing risks. At the same time, alternatives must satisfy constraints. This leads to a constrained two-criterion optimization problem in the Boolean space. To cope with the peculiarities and high complexity of the problem, evolution-based algorithms with an island meta-heuristic are applied as an alternative to conventional techniques. Results: The problem in hand was reduced to a two-criterion unconstrained extreme problem and solved with different evolution-based multi-objective optimization heuristics. Next, we applied a proposed meta-heuristic combining the particular algorithms and causing their interaction in a cooperative and collaborative way. The obtained results showed that the island heuristic outperformed the original ones based on the values of a specific metric, thus showing the representativeness of Pareto front approximations. Having more representative approximations, decision-makers have more alternative project portfolios corresponding to different risk and profit estimations. Since these criteria are conflicting, when choosing an alternative with an estimated high profit, decision-makers follow a strategy with an estimated high risk and vice versa. Conclusion: In the present paper, the project portfolio decision-making problem was reduced to a 0-1 knapsack constrained multi-objective optimization problem. The algorithm investigation confirms that the use of the island meta-heuristic significantly improves the performance of genetic algorithms, thereby providing an efficient tool for Financial Responsibility Centres Management.
Ključne besede: 0-1 multi-objective constrained knapsack problem, project management portfolio problem, multi-objective evolution-based optimization algorithms, collaborative and cooperative meta-heuristics
Objavljeno v DKUM: 04.05.2018; Ogledov: 1484; Prenosov: 296
.pdf Celotno besedilo (993,98 KB)
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Evolutionary programming of CNC machines
Miha Kovačič, Miran Brezočnik, Ivo Pahole, Jože Balič, Borut Kecelj, 2005, izvirni znanstveni članek

Opis: The paper proposes a new concept for programming of CNC machines. The concept based on genetic algorithms assures evolutionary generation and optimization of NC programs on the basis of CAD models of manufacturing environment. The structure, undergoing simulated evolution, is the population of NC programs. The NC programs control the machine which performs simple elementary motions. During the evolution the machine movement becomes more and more complex and intelligent solutions emerge gradually as a result of the interaction between machine movements and manufacturing environment. The examples of evolutionary programming of CNC lathe and CNC milling machine tool for different complexities of the blanks and products are presented. The proposed concept showed a high degree of universality, efficiency, and reliability and it can be also simply adopted to other CNC machines.
Ključne besede: manufacturing systems, NC-programming, CNC lathes, simulated evolution, genetic algorithms
Objavljeno v DKUM: 01.06.2012; Ogledov: 1833; Prenosov: 107
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