The Particle Swarm Optimization (PSO) method is a global optimization technique based on the gradual evolution of a population of solutions called particles. The method evolves the particles based on both the best position of each of them in the past and the best position of the whole. Due to its simplicity, the method has found application in many scientific areas, and for this reason, during the last few years, many modifications have been presented. This paper introduces three modifications to the method that aim to reduce the required number of function calls while maintaining the accuracy of the method in locating the global minimum. These modifications affect important components of the method, such as how fast the particles change or even how the method is terminated. The above modifications were tested on a number of known universal optimization problems from the relevant literature, and the results were compared with similar techniques.

Differential Evolution (DE) is a method of optimization used in symmetrical optimization problems and also in problems that are not even continuous, and are noisy and change over time. DE optimizes a problem with a population of candidate solutions and creates new candidate solutions per generation in combination with existing rules according to discriminatory rules. The present work proposes two variations for this method. The first significantly improves the termination of the method by proposing an asymptotic termination rule, which is based on the differentiation of the average of the function values in the population of DE. The second modification proposes a new scheme for a critical parameter of the method, which improves the method’s ability to better explore the search space of the objective function. The proposed variations have been tested on a number of problems from the current literature, and from the experimental results, it appears that the proposed modifications render the method quite robust and faster even in large-scale problems.

Global optimization is a widely used technique that finds application in many sciences such as physics, economics, medicine, etc., and with many extensions, for example, in the area of machine learning. However, in many cases, global minimization techniques require a high computational time and, for this reason, parallel computational approaches should be used. In this paper, a new parallel global optimization technique based on the differential evolutionary method is proposed. This new technique uses a series of independent parallel computing units that periodically exchange the best solutions they have found. Additionally, a new termination rule is proposed here that exploits parallelism to accelerate process termination in a timely and valid manner. The new method is applied to a number of problems in the established literature and the results are quite promising.

Global optimization is widely adopted presently in a variety of practical and scientific problems. In this context, a group of widely used techniques are evolutionary techniques. A relatively new evolutionary technique in this direction is that of Giant-Armadillo Optimization, which is based on the hunting strategy of giant armadillos. In this paper, modifications to this technique are proposed, such as the periodic application of a local minimization method as well as the use of modern termination techniques based on statistical observations. The proposed modifications have been tested on a wide series of test functions available from the relevant literature and compared against other evolutionary methods.

The topic of efficiently finding the global minimum of multidimensional functions is widely applicable to numerous problems in the modern world. Many algorithms have been proposed to address these problems, among which genetic algorithms and their variants are particularly notable. Their popularity is due to their exceptional performance in solving optimization problems and their adaptability to various types of problems. However, genetic algorithms require significant computational resources and time, prompting the need for parallel techniques. Moving in this research direction, a new global optimization method is presented here that exploits the use of parallel computing techniques in genetic algorithms. This innovative method employs autonomous parallel computing units that periodically share the optimal solutions they discover. Increasing the number of computational threads, coupled with solution exchange techniques, can significantly reduce the number of calls to the objective function, thus saving computational power. Also, a stopping rule is proposed that takes advantage of the parallel computational environment. The proposed method was tested on a broad array of benchmark functions from the relevant literature and compared with other global optimization techniques regarding its efficiency.

For thousands of years forest fires played the role of a regulator in the ecosystem. Forest fires contributed to the ecological balance by destroying old and diseased plant material; but in the modern era fires are a major problem that tests the endurance not only of government agencies around the world, but also have an effect on climate change. Forest fires have become more intense, more destructive, and more deadly; these are known as megafires. They can cause major economic and ecological problems, especially in the summer months (dry season). However, humanity has developed a tool that can predict fire events, to detect them in time, but also to predict their duration. This tool is artificial intelligence, specifically, machine learning, which is one part of AI. Consequently, this paper briefly mentions several methods of machine learning as used in predicting forest fires and in early detection, submitting an overall review of current models. Our main overall objective is to venture into a new field: predicting the duration of ongoing forest fires. Our contribution offers a new way to manage forest fires, using accessible open data, available from the Hellenic Fire Service. In particular, we imported over 72,000 data from a 10-year period (2014–2023) using machine learning techniques. The experimental and validation results are more than encouraging, with Random Forest achieving the lowest value for the error range (8–13%), meaning it was 87–92% accurate on the prediction of forest fire duration. Finally, some future directions in which to extend this research are presented.