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Accurate forecasting remains a challenge, even with advanced techniques like deep learning (DL), ARIMA, and Holt–Winters (H&W), particularly for chaotic phenomena such as those observed in several areas, such as COVID-19, energy, and financial time series. Addressing this, we introduce a Forecasting Method with Filters and Residual Analysis (FMFRA), a hybrid methodology specifically applied to datasets of COVID-19 time series, which we selected for their complexity and exemplification of current forecasting challenges. FMFFRA consists of the following two approaches: FMFRA-DL, employing deep learning, and FMFRA-SSA, using singular spectrum analysis. This proposed method applies the following three phases: filtering, forecasting, and residual analysis. Initially, each time series is split into filtered and residual components. The second phase involves a simple fine-tuning for the filtered time series, while the third phase refines the forecasts and mitigates noise. FMFRA-DL is adept at forecasting complex series by distinguishing primary trends from insufficient relevant information. FMFRA-SSA is effective in data-scarce scenarios, enhancing forecasts through automated parameter search and residual analysis. Chosen for their geographical and substantial populations and chaotic dynamics, time series for Mexico, the United States, Colombia, and Brazil permitted a comparative perspective. FMFRA demonstrates its efficacy by improving the common forecasting performance measures of MAPE by 22.91%, DA by 13.19%, and RMSE by 25.24% compared to the second-best method, showcasing its potential for providing essential insights into various rapidly evolving domains. [ABSTRACT FROM AUTHOR]

Ionic liquids (ILs) are salts with a wide liquid temperature range and low melting points and can be fine-tuned to have specific physicochemical properties by the selection of their anion and cation. However, having a physical synthesis of multiple ILs for testing purposes can be expensive. For this reason, an insilico estimation of physicochemical properties is desired. The selection of these components is limited by the low precision offered by state-of-the-art predictive models. In this paper, we explore the prediction of melting points with clustering algorithms and a novel Neuroevolution approach. We focused our design on simplicity. We concluded that performing clustering analysis in a previous phase of the model generation improves the estimation accuracy of the melting point which is validated in experimentation made in-silico. [ABSTRACT FROM AUTHOR]

The combination of forecasting methods is a widespread technique. The most common technique for ensembling several individual methods is scoring. These ensemble methods have been useful for designing hybrid forecasting time series methods in several areas. However, more precise applications are required in modern times, and hybridizations using several ranking approaches have emerged to solve this problem. The main difficulty of this technique is finding the most suitable methodology to combine forecasting methods. This work presents a new methodology named FCTA (forecasting combined method with threshold accepting) for ensembling several forecasting methods. This methodology uses a Threshold Accepting algorithm for weighting individual predictions. FCTA starts from an initial weighting and aims to find the best ponderation of the individual methods by optimizing the precision of the global prediction. For testing FCTA, we selected a dataset taken from M4-Makridakis-competition, and we compared it with the best individual forecasting methods. FCTA is also compared with other successful methodologies. The experimentation shows that FCTA surpasses the best M4 individual methods and is equivalent or better than the best methodologies of the area. [ABSTRACT FROM AUTHOR]

We know that SARS-Cov2 produces the new COVID-19 disease, which is one of the most dangerous pandemics of modern times. This pandemic has critical health and economic consequences, and even the health services of the large, powerful nations may be saturated. Thus, forecasting the number of infected persons in any country is essential for controlling the situation. In the literature, different forecasting methods have been published, attempting to solve the problem. However, a simple and accurate forecasting method is required for its implementation in any part of the world. This paper presents a precise and straightforward forecasting method named SVR-ESAR (Support Vector regression hybridized with the classical Exponential smoothing and ARIMA). We applied this method to the infected time series in four scenarios, which we have taken for the Github repository: the Whole World, China, the US, and Mexico. We compared our results with those of the literature showing the proposed method has the best accuracy. [ABSTRACT FROM AUTHOR]

The Hurst exponent is a metric used to evaluate whether a time series exhibits long-term memory, and it is used to identify its complexity. Besides, forecasting methods are tested using time series from Makridakis competition. Additionally, Exponential Smoothing is among the best forecasting methods of this competition, and ARIMA is one of the most used for many applications. Nevertheless, the quality of using the Hurst exponent in Makridakis M3-Competition for measuring how well Simple Exponential Smoothing and ARIMA are adapted to a specific time series is unknown. In this work, we show the impact of applying the Hurst exponent using the complete set of series from the M3-Competition. We used k-means as clustering algorithm for the 3003 Hurst exponent values of these series, improving the visualization of all of the data to identify a relationship between Hurst exponent with MAPE and sMAPE forecasting error of the Simple Exponential Smoothing and ARIMA. Finally, the experimentation shows that Hurst exponent and MAPE for the tested methods are inversely related in most of the cases and that there is a trend between them. [ABSTRACT FROM AUTHOR]

The Job Shop Scheduling Problem (JSSP) arises in the context of high-performance computing and belongs to the NP-hard combinatorial optimization problems. The purpose of JSSP is to find the order of execution of a set of jobs on a group of machines, subject to certain precedence and resource availability constraints. The objective in this problem is minimizing the makespan that is the time elapsed from the starting time of the first job until the completion time of the last job. In this paper, a novel hybrid algorithm named AntGenSA for solving JSSP is proposed. AntGenSA uses Ant Colony System (ACS), Simulated Annealing (SA), and Genetic Algorithm (GA). To assess the performance of this algorithm, it is executed in a parallel computer, using a set of instances proposed by Fisher-Thompson, Yamada-Nakano, Taillard, Lawrence, and Applegate-Cook. The evaluation of this algorithm was performed mainly by the quality of the solution but the execution time was measuring as well. The experimental results show that the performance of the parallel execution of AntGenSA is highly competitive with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

A new hybrid Multiphase Simulated Annealing Algorithm using Boltzmann and Bose-Einstein distributions (MPSABBE) is proposed. MPSABBE was designed for solving the Protein Folding Problem (PFP) instances. This new approach has four phases: (i) Multiquenching Phase (MQP), (ii) Boltzmann Annealing Phase (BAP), (iii) Bose-Einstein Annealing Phase (BEAP), and (iv) Dynamical Equilibrium Phase (DEP). BAP and BEAP are simulated annealing searching procedures based on Boltzmann and Bose-Einstein distributions, respectively. DEP is also a simulated annealing search procedure, which is applied at the final temperature of the fourth phase, which can be seen as a second Bose-Einstein phase. MQP is a search process that ranges from extremely high to high temperatures, applying a very fast cooling process, and is not very restrictive to accept new solutions. However, BAP and BEAP range from high to low and from low to very low temperatures, respectively. They are more restrictive for accepting new solutions. DEP uses a particular heuristic to detect the stochastic equilibrium by applying a least squares method during its execution. MPSABBE parameters are tuned with an analytical method, which considers the maximal and minimal deterioration of problem instances. MPSABBE was tested with several instances of PFP, showing that the use of both distributions is better than using only the Boltzmann distribution on the classical SA. [ABSTRACT FROM AUTHOR]

The Job Shop Scheduling Problem (JSSP) consists of finding the best scheduling for a set of jobs that should be processed in a specific order using a set of machines. This problem belongs to the NP-hard class problems and has enormous industrial applicability. In the manufacturing area, decision-makers consider several criteria to elaborate their production schedules. These cases are studied in multi-objective optimization. However, few works are addressed from this multi-objective perspective. The literature shows that multi-objective evolutionary algorithms can solve these problems efficiently; nevertheless, multi-objective algorithms have slow convergence to the Pareto optimal front. This paper proposes three multi-objective Scatter Search hybrid algorithms that improve the convergence speed evolving on a reduced set of solutions. These algorithms are: Scatter Search/Local Search (SS/LS), Scatter Search/Chaotic Multi-Objective Threshold Accepting (SS/CMOTA), and Scatter Search/Chaotic Multi-Objective Simulated Annealing (SS/CMOSA). The proposed algorithms are compared with the state-of-the-art algorithms IMOEA/D, CMOSA, and CMOTA, using the MID, Spacing, HV, Spread, and IGD metrics; according to the experimental results, the proposed algorithms achieved the best performance. Notably, they obtained a 47% reduction in the convergence time to reach the optimal Pareto front. [ABSTRACT FROM AUTHOR]

The classic model of Markowitz for designing investment portfolios is an optimization problem with two objectives: maximize returns and minimize risk. Various alternatives and improvements have been proposed by different authors, who have contributed to the theory of portfolio selection. One of the most important contributions is the Sharpe Ratio, which allows comparison of the expected return of portfolios. Another important concept for investors is diversification, measured through the average correlation. In this measure, a high correlation indicates a low level of diversification, while a low correlation represents a high degree of diversification. In this work, three algorithms developed to solve the portfolio problem are presented. These algorithms used the Sharpe Ratio as the main metric to solve the problem of the aforementioned two objectives into only one objective: maximization of the Sharpe Ratio. The first, GENPO, used a Genetic Algorithm (GA). In contrast, the second and third algorithms, SAIPO and TAIPO used Simulated Annealing and Threshold Accepting algorithms, respectively. We tested these algorithms using datasets taken from the Mexican Stock Exchange. The findings were compared with other mathematical models of related works, and obtained the best results with the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the linear ordering problem, which has been solved using different metaheuristics approaches. These algorithms have the common problem of finding a proper balance of the intensification and diversification processes; in this work we propose an iterated local search in which it is incorporated a Becker heuristic strategy for constructing the initial solution, and a search strategy as perturbation process, achieving a better balance between intensification and diversification. The proposed algorithm obtained an improvement greater than 90%, decreasing the average percentage error respect the state of art ILS algorithm. The Wilcoxon nonparametric statistical test shows that the proposed algorithm significantly outperforms the iterated local search solution of the state of the art, ranking it among the top five solutions of the state of the art for the linear ordering problem. [ABSTRACT FROM AUTHOR]