Artificial Intelligence
B. Mahdipour; S. H. Zahiri; I. Behravan
Abstract
Background and Objectives: Path planning is one of the most important topics related to the navigation of all kinds of moving vehicles such as airplanes, surface and subsurface vessels, cars, etc. Undoubtedly, in the process of making these tools more intelligent, detecting and crossing obstacles without ...
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Background and Objectives: Path planning is one of the most important topics related to the navigation of all kinds of moving vehicles such as airplanes, surface and subsurface vessels, cars, etc. Undoubtedly, in the process of making these tools more intelligent, detecting and crossing obstacles without encountering them by taking the shortest path is one of the most important goals of researchers. Significant success in this field can lead to significant progress in the use of these tools in a variety of applications such as industrial, military, transportation, commercial, etc. In this paper, a metaheuristic-based approach with the introduction of new fitness functions is presented for the problem of path planning for various types of surface and subsurface moving vehicles.Methods: The proposed approach for path planning in this research is based on the metaheuristic methods, which makes use of a novel fitness function. Particle Swarm Optimization (PSO) is the metaheuristic method leveraged in this research but other types of metaheuristic methods can also be used in the proposed architecture for path planning.Results: The efficiency of the proposed method, is tested on two synthetic environments for finding the best path between the predefined origin and destination for both surface and subsurface unmanned intelligent vessels. In both cases, the proposed method was able to find the best path or the closest answer to it.Conclusion: In this paper, an efficient method for the path planning problem is presented. The proposed method is designed using Particle Swarm Optimization (PSO). In the proposed method, several effective fitness function have been defined so that the best path or one of the closest answers can be obtained by utilized metaheuristic algorithm. The results of implementing the proposed method on real and simulated geographic data show its good performance. Also, the obtained quantitative results (time elapsed, success rate, path cost, standard deviation) have been compared with other similar methods. In all of these measurements, the proposed algorithm outperforms other methods or is comparable to them.
Artificial Intelligence
M. Amoozegar; S. Golestani
Abstract
Background and Objectives: In recent years, various metaheuristic algorithms have become increasingly popular due to their effectiveness in solving complex optimization problems across diverse domains. These algorithms are now being utilized for an ever-expanding number of real-world applications across ...
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Background and Objectives: In recent years, various metaheuristic algorithms have become increasingly popular due to their effectiveness in solving complex optimization problems across diverse domains. These algorithms are now being utilized for an ever-expanding number of real-world applications across many fields. However, there are two critical factors that can significantly impact the performance and optimization capability of metaheuristic algorithms. First, comprehensively understanding the intrinsic behavior of the algorithms can provide key insights to improve their efficiency. Second, proper calibration and tuning of an algorithm's parameters can dramatically enhance its optimization effectiveness. Methods: In this study, we propose a novel response surface methodology-based approach to thoroughly analyze and elucidate the behavioral dynamics of optimization algorithms. This technique constructs an informative empirical model to determine the relative importance and interaction effects of an algorithm's parameters. Although applied to investigate the Gravitational Search Algorithm, this systematic methodology can serve as a generally applicable strategy to gain quantitative and visual insights into the functionality of any metaheuristic algorithm.Results: Extensive evaluation using 23 complex benchmark test functions exhibited that the proposed technique can successfully identify ideal parameter values and their comparative significance and interdependencies, enabling superior comprehension of an algorithm's mechanics.Conclusion: The presented modeling and analysis framework leverages multifaceted statistical and visualization tools to uncover the inner workings of algorithm behavior for more targeted calibration, thereby enhancing the optimization performance. It provides an impactful approach to elucidate how parameter settings shape algorithm searche so they can be calibrated for optimal efficiency.
Artificial Intelligence
I. Behravan; S. M. Razavi
Abstract
Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, ...
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Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem.Methods: In this paper, a novel machine learning approach, which works in two phases, is introduced to predict the price of a stock in the next day based on the information extracted from the past 26 days. In the first phase of the method, an automatic clustering algorithm clusters the data points into different clusters, and in the second phase a hybrid regression model, which is a combination of particle swarm optimization and support vector regression, is trained for each cluster. In this hybrid method, particle swarm optimization algorithm is used for parameter tuning and feature selection. Results: The accuracy of the proposed method has been measured by 5 companies’ datasets, which are active in the Tehran Stock Exchange market, through 5 different metrics. On average, the proposed method has shown 82.6% accuracy in predicting stock price in 1-day ahead.Conclusion: The achieved results demonstrate the capability of the method in detecting the sudden jumps in the price of a stock.
