Baba is You is a challenging puzzle game in which the player can modify the rules of the game. This yields a large variety of puzzles and an enormous state space to be searched through. Recently, the Feature Space Sea...
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In the present paper, we investigate the management of a fleet of electric vehicles. We propose a hybrid evolutionary approach for solving the problem of simultaneously planning the charging of electric vehicles and t...
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Variable-length encoding evolutionary algorithm has been proved effective in small and medium-sized reversible logic synthesis. Variable-length chromosome is adopted because the length of optimal reversible circuit is...
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The activated sludge process (ASP) is the most widely used biological wastewater treatment system. Advances in research have led to the adoption of Artificial Intelligence (AI), in particular, Nature-Inspired Algorith...
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The activated sludge process (ASP) is the most widely used biological wastewater treatment system. Advances in research have led to the adoption of Artificial Intelligence (AI), in particular, Nature-Inspired Algorithm (NIA) techniques such as Genetic algorithms (GAs) and Particle Swarm Optimization (PSO) to optimize treatment systems. This has aided in reducing the complexity and computational time of ASP modelling. This paper covers the latest NIAs used in ASP and discusses the advantages and limitations of each algorithm compared to more traditional algorithms that have been utilized over the last few decades. algorithms were assessed based on whether they looked at real/ideal treatment plant (WWTP) data (and efficiency) and whether they outperformed the traditional algorithms in optimizing the ASP. While conventional algorithms such as Genetic algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) were found to be successfully employed in optimization techniques, newer algorithms such as Whale Optimization Algorithm (WOA), Bat Algorithm (BA), and Intensive Weed Optimization Algorithm (IWO) achieved similar results in the optimization of the ASP, while also having certain unique advantages.
An efficient public transport system is essential for sustainable city development, as it directly affects people's welfare. This article addresses the urban public transport timetabling problem with multi-objecti...
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An efficient public transport system is essential for sustainable city development, as it directly affects people's welfare. This article addresses the urban public transport timetabling problem with multi-objective evolutionary algorithms, considering multiple vehicle types and respecting the public transport restrictions of local authorities. The conflicting objectives are the minimization of fuel consumption and unsatisfied user demand, which are essential to make transit buses an attractive alternative for users, thus promoting environmentally friendly mobility. The problem was solved with two well-known metaheuristics, namely the non-dominated sorting genetic algorithm-II (NSGA-II) and cellular genetic algorithm for multi-objective optimization (MOCell), and their performance was compared using several metrics. Their parameters were tuned with a thorough study, and several evolutionary operators designed for the problem were considered. The outcomes suggest that a solution using various types of buses can produce diverse dispatching strategies, reducing pollutant emissions and maintaining tolerable ridership losses.
Multi-objective optimization of a hybrid system is investigated to supply an autonomous residential building. The proposed system consists of photovoltaic panel, wind turbine, ground source heat pump, diesel generator...
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Multi-objective optimization of a hybrid system is investigated to supply an autonomous residential building. The proposed system consists of photovoltaic panel, wind turbine, ground source heat pump, diesel generator, battery bank, and fuel cell. This study presents an innovative approach in optimization considering all economic, technical, environmental, and social aspects. Objective functions include loss of power supply probability (LPSP), levelized cost of energy (LCOE), CO2 emission, and human development index (HDI) that are optimized simultaneously. Also, the simulation-based approach in NSGA-II and MOPSO algorithms is used to estimate the Pareto front. The Pareto front solutions are the optimum points that help decision-makers choose the best system configuration based on priorities. Due to the importance of renewable energy utilization and reliability, two conditions of renewable fraction (RF) > 70% and LPSP < 0.05 are considered to select the optimal systems. Among the selected systems, the solutions with the highest RF also generated more extra energy. Diesel generators are much less expensive than fuel cells;however, the environmental benefits of the fuel cell make this technology attractive. Therefore, systems that use only the diesel generator as a backup unit have lower LCOE and higher CO2 emissions. LCOE in selected solutions is reduced by 51 to 88% by selling extra power to the grid. The environmental assessment results show that CO2 emissions in selected systems compared to coal-based power plants and natural gas power plants are decreased by 46-100% and 3-100%, respectively. Also, Pareto fronts evaluation shows that the NSGA-II algorithm's solutions covered a more extensive range and scattered more uniformly.
River flood routing is an important issue in current water resources management. As a popular hydrological flood routing method, Muskingum model has always been the dominant method of flood routing. This paper reviews...
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River flood routing is an important issue in current water resources management. As a popular hydrological flood routing method, Muskingum model has always been the dominant method of flood routing. This paper reviews the development of Muskingum model and the research status of its parameter estimation. The characteristics and relationships of different types of Muskingum models are compared, and it is found that the combination of mathematical techniques and evolutionary algorithms has shown good results in parameter estimation in recent years. In addition, this paper also gives a brief overview of six accuracy evaluation criteria and nine research case data sets commonly used in the literature. It also introduces some challenges of the Muskingum model and new trends in future research, which should interest researchers and engineers.
The evaluation of student projects is a difficult task, especially when they involve both a technical and a creative component. We propose an artificial intelligence (AI)-based methodology to help in the evaluation of...
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The evaluation of student projects is a difficult task, especially when they involve both a technical and a creative component. We propose an artificial intelligence (AI)-based methodology to help in the evaluation of complex projects in engineering and computer science courses. This methodology is intended to evaluate the assessment process itself allowing to analyze the influence of each variable in the final grade, to discover possible biases, inconsistencies and discrepancies, and to generate appropriate rubrics that help to avoid them. As an example of its application, we consider the evaluation of the projects submitted in an undergraduate introductory course on computer science. Using data collected from the evaluation during five academic years, we follow the proposed methodology to create AI models and analyze the main variables which are involved in the assessment of the projects. The proposed methodology can be applied to other courses and degrees, where both technical and creative components are considered to evaluate the projects.
Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent...
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Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go a step further and propose its parallelization. Specifically, we propose a two-layer parallelization. Firstly, an automation of the molecule distribution process between the available nodes in a cluster, and secondly, a parallelization of the internal methods (initialization, reproduction, selection and optimization). This new software, called pOptiPharm, aims to improve the quality of predictions and reduce experimentation time. As the results show, the performance of the proposed methods is good. It can find better solutions than the sequential OptiPharm, all while reducing its computation time almost proportionally to the number of processing units considered.
Structural damage can be caused by a variety of factors, which include internal factors such as design flaws, construction errors, material deficiencies, and external factors such as earthquakes, overloading, environm...
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ISBN:
(纸本)9781605956930
Structural damage can be caused by a variety of factors, which include internal factors such as design flaws, construction errors, material deficiencies, and external factors such as earthquakes, overloading, environmental factors, and terms of service violations. As such damage can compromise the safety, functionality, and financial viability of a structure, it is essential to identify and address any issues quickly. The engineering community, including civil, mechanical, and aerospace engineers, is greatly interested in structural damage identification. Numerous methods, such as experiential and simulation-based techniques, have been developed to achieve this goal. However, accurately modeling a structure and developing a robust inverse algorithm for damage detection is a significant challenge. Finite element modeling is one of the widely used methods for detecting structural damage. Here, Model updating is a crucial process that involves adjusting a structural model to improve its accuracy and reliability. Various methods for updating Finite Element Models have been proposed, including sensitivity-based, direct, statistical, probabilistic, and iterative methods. However, traditional methods can be complex and time-consuming, leading researchers to explore the use of evolutionary algorithms to streamline the process. evolutionary algorithms are a type of computational optimization technique that mimics the process of natural selection to find the best solution to a given problem. In the context of model updating, evolutionary algorithms can identify the optimal combination of model parameters that best matches measured data. There has been a growing interest in using evolutionary algorithms to update Finite Element Models for structural damage identification in recent years. This paper presents a case study on damage identification, specifically sectional loss and boundary condition rigidity, as an optimization problem. It demonstrates the use of an evolutionary a
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