1,720,961 research outputs found
Ant colony optimization for Chinese postman problem
No full textThis paper aims to solve the Chinese Postman Problem (CPP) using an Ant Colony Optimization (ACO) algorithm. In graph theory, the CPP looks for the shortest closed path that visits every edge of a connected undirected graph. This problem has many applications, including route optimization, interactive system analysis, and flow design. Although numerous algorithms aimed at solving CPP are present in the literature, very few meta-heuristic algorithms are proposed, and no ACO applications have been proposed to solve them. This paper tries to fill this gap by presenting an ACO algorithm that solves CPP (ACO-CPP). To prove its consistency and effectiveness, ACO-CPP is compared with a Genetic Algorithm (GA) and a recursive algorithm throughout three experiments: (1) recursive-ACO-GA comparisons over randomly generated graphs for the attainment of the global optimum; (2) ACO-GA statistical comparisons over specifically generated graphs; (3) recursive-ACO-GA comparisons by changing ACO hyperparameters over randomly generated graphs for the attainment of the global optimum. The experiments prove that the ACO-CPP algorithm is efficient and exhibits a consistency similar to GA when the number of possible solutions to explore is relatively low. However, when that number greatly exceeds those explored, ACO outperforms GA. This suggests that ACO is more suitable for solving problems with a CPP structure
Genetic algorithm for optimal multivariate mixture
No full textThis paper proposes an algorithm to find an optimal mixture that is as close as possible to an ideal solution, starting from a set of elements (items) described by a set of variables (features).
This class of optimization problems can be tackled through traditional approaches belonging to the field of operations research (OR) or even through meta-heuristics techniques belonging to the field of artificial intelligence (AI).
In order to present an artificial intelligence perspective, this paper uses a genetic algorithm (GA) model which proves its consistency through the comparison with a linear programming (LP) solver on a set of 8-items 5-features experiments. Results show that the proposed GA converges towards the global optimum and provides competitive results
Optimal multivariate mixture: a genetic algorithm approach
No full textThe Optimal Multivariate Mixture Problem (OMMP) consists of finding an optimal mixture which, starting from a set of elements (items) described by a set of variables (features), is as close as possible to an ideal solution. This problem has numerous applications spanning various fields, including food science, agriculture, chemistry, materials science, medicine, and pharmaceuticals. The OMMP is a class of optimization problems that can be addressed using traditional Operations Research (OR) approaches. However, it can also be effectively tackled using meta-heuristic techniques within Artificial Intelligence (AI). This paper aims to present an Artificial Intelligence perspective. It proposes a Genetic Algorithm (GA) for Optimal Multivariate Mixture (GA-OMM), a novel improved version of a GA whose modified genetic operators prove to improve the exploration efficiency. Here, the algorithm is described in its general framework, and a test case 8-items 5-features is conducted to evaluate efficiency by exploring various combinations of hyperparameters. Test cases are also set up for the previous version, as well as a linear programming (LP) approach. The data experiments indicate that the proposed GA is efficient, converges towards the global optimum, consistently outperforms its predecessor, and delivers highly competitive results. In particular, GA-OMM shows an average fitness of GA-OMMP/LP and standard deviation with an order of magnitude ranging between to . Moreover, it consistently outperforms its predecessor, which exhibits similar values around $10^{−3}
Ant Colony Optimization for solving Directed Chinese Postman Problem
No full textThe Chinese Postman Problem (CPP) is a well-known optimization problem involving determining the shortest route, modeling the system as an undirected graph, for delivering mail, ensuring all roads are traversed while returning to the post office. The Directed Chinese Postman Problem (DCPP) extends the Chinese Postman Problem (CPP), where the underlying graph representing the system incorporates exclusively directed edges. Similarly to CPP, this problem has plenty of applications in route optimization, interactive system analysis, and circuit design problems. However, due to the added constraint (directionality of edges), DCPP results are more challenging to solve. Although methods to solve it in literature are proposed, typically by using minimum-cost-flow algorithms, the meta-heuristics approaches proposed to deal with it are very limited. In this paper, we propose an innovative meta-heuristic approach to solve DCPP by using an ant colony optimization (ACO) algorithm, i.e., an algorithm that simulates in a simplified way the behavior of some species of ants to solve optimization problems. The efficiency of our ant colony optimization for solving the Directed Chinese Postman Problem (ACO-DCPP) is measured by comparing the ACO outcomes with the results obtained by a recursive algorithm that explores all the possible solutions. Results show that ACO-DCPP is stable and gets the global optimum frequently by using an extremely limited number of solutions explored
MCMC Approach for Stock Price Forecasting Using an Italian-BERT Model
No full textSentiment Analysis is a task of Natural Language Processing (NLP) whose main goal is to classify sentences (or entire texts) to obtain a score about their polarity: positive, negative, or neutral. Recently, a Transformer-based architecture, AlBERTino [5], has been introduced to determine a sentiment score in the financial sector through a specialized corpus of sentences. Here, the AlBERTino model can be used to improve stock forecasting, determining the sentiment score associated with events in the market and using a Markov Chain Monte Carlo (MCMC) method to determine a new series of bounded drift and volatility values based on this score. With these new values obtained through Bayesian inference, generating a series of paths through a Monte Carlo method to predict a polarity-driven future price is possible
Harnessing the power of blockchain in the agri-food sector: a meta-analysis of current research and best practices
No full textThe Blockchain is a shared and ``immutable” data structure defined as a digital register whose entries are grouped into ``blocks”, concatenated in chronological order, and whose integrity is guaranteed through the use of cryptography. Blockchain technology in agriculture and agribusiness has gained significant attention as a potential solution to various challenges the agri-food industry faces. In this paper, we conduct a meta-analysis exploring the various applications of Blockchain in agriculture and agribusiness. We examine the types of technology used, the specific product branches involved, and the security protocols employed.
Our findings show that several experiments and pilot projects have been conducted in the field and that Blockchain applications such as Hyperledger Fabric and Corda are ready for real-world implementation in the agri-food sector. We also identify potential benefits of using Blockchain in the agri-food sector, including enhanced product quality guarantees, improved traceability and transparency, assurance of provenance, and more equitable distribution of profits along the agri-food supply chain. Overall, our review suggests that the use of Blockchain in agriculture and agribusiness has the potential to address a range of challenges faced by these sectors and drive innovation, technical, and economic efficiencies
ANT COLONY OPTIMIZATION FOR CHINESE POSTMAN PROBLEM
No full textThis paper aims to solve the Chinese Postman Problem (CPP) using an Ant Colony Op-timization (ACO) algorithm. In graph theory, the CPP looks for the shortest closed path that visits every edge of a connected undirected graph. This problem has many applica-tions, including route optimization, interactive system analysis, and flow design. Alt-hough numerous algorithms aimed at solving CPP are present in the literature, there are very few meta-heuristic algorithms proposed, and no ACO applications have been pro-posed to solve it. This paper tries to fill this gap by presenting an ACO algorithm that solves CPP (ACO-CPP). In addition, it compares its performances with a Genetic Algo-rithm (GA) and a recursive algorithm that explores all the possible solutions and selects the best one. Experiments show that the ACO-CPP algorithm is efficient and can maintain consistency even when the number of possible solutions is much greater than the number of solutions explored
Rivoluzione educativa: come l’AI personalizza l’insegnamento, risolve problemi e prevede comportamenti,
No full textThe text explores some of the potentialities of artificial intelligence (AI) in the educational field, made possible by this technology’s ability to categorize data. Through data analysis and machine learning, AI can improve existing social models and provide personalized analyses. It examines the challenges and opportunities of AI in personalizing learning paths, assessing competencies, and even detecting issues such as depression and internet addiction, even in their early stages. The authors emphasize the importance of integrating AI with the human approach, using it as a decision-making support rather than a replacement
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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