Andrey Dik / Profil

The article examines the impact of changing the shape of probability distributions on the performance of optimization algorithms. We will conduct experiments using the Smart Cephalopod (SC) test algorithm to evaluate the efficiency of various probability distributions in the context of optimization problems.

The first part was devoted to the well-known and popular algorithm - simulated annealing. We have thoroughly considered its pros and cons. The second part of the article is devoted to the radical transformation of the algorithm, which turns it into a new optimization algorithm - Simulated Isotropic Annealing (SIA).

The Simulated Annealing algorithm is a metaheuristic inspired by the metal annealing process. In the article, we will conduct a thorough analysis of the algorithm and debunk a number of common beliefs and myths surrounding this widely known optimization method. The second part of the article will consider the custom Simulated Isotropic Annealing (SIA) algorithm.

The article presents a complete exploration of the Nelder-Mead method, explaining how the simplex (function parameter space) is modified and rearranged at each iteration to achieve an optimal solution, and describes how the method can be improved.

In this article, we will consider the algorithm that demonstrates the most controversial results of all those discussed previously - the differential evolution (DE) algorithm.

The article presents an optimization algorithm based on the patterns of constructing spiral trajectories in nature, such as mollusk shells - the spiral dynamics optimization (SDO) algorithm. I have thoroughly revised and modified the algorithm proposed by the authors. The article will consider the necessity of these changes.

The article considers an interesting algorithm derived from inanimate nature - intelligent water drops (IWD) simulating the process of river bed formation. The ideas of this algorithm made it possible to significantly improve the previous leader of the rating - SDS. As usual, the new leader (modified SDSm) can be found in the attachment.

In this article, we will consider another optimization algorithm inspired by inanimate nature - Charged System Search (CSS) algorithm. The purpose of this article is to present a new optimization algorithm based on the principles of physics and mechanics.

The article discusses Stochastic Diffusion Search (SDS), which is a very powerful and efficient optimization algorithm based on the principles of random walk. The algorithm allows finding optimal solutions in complex multidimensional spaces, while featuring a high speed of convergence and the ability to avoid local extrema.

The article considers the algorithm of the MEC family called the simple mind evolutionary computation algorithm (Simple MEC, SMEC). The algorithm is distinguished by the beauty of its idea and ease of implementation.

The article presents a detailed description of the shuffled frog-leaping (SFL) algorithm and its capabilities in solving optimization problems. The SFL algorithm is inspired by the behavior of frogs in their natural environment and offers a new approach to function optimization. The SFL algorithm is an efficient and flexible tool capable of processing a variety of data types and achieving optimal solutions.

L'article décrit les principes, les méthodes et les possibilités d'utilisation de l'Algorithme Electro-Magnétique dans divers problèmes d'optimisation. L'algorithme EM est un outil d'optimisation efficace capable de travailler avec de grandes quantités de données et des fonctions multidimensionnelles.

L'algorithme SSG (Saplings Sowing and Growing up) s'inspire de l'un des organismes les plus résistants de la planète, qui fait preuve d'une capacité de survie exceptionnelle dans des conditions très diverses.

Dans cet article, j'examinerai l'algorithme d'optimisation Monkey Algorithm (MA). La capacité de ces animaux à surmonter des obstacles difficiles et à atteindre les cimes des arbres les plus inaccessibles est à l'origine de l'idée de l'algorithme MA.

Dans cet article, j'étudierai et testerai l'algorithme d'optimisation le plus puissant : la recherche harmonique (HS), inspirée par le processus de recherche de l'harmonie sonore parfaite. Quel est donc l'algorithme qui domine aujourd'hui notre classement ?

GSA est un algorithme d'optimisation de la population inspiré de la nature inanimée. Grâce à la loi de la gravité de Newton implémentée dans l'algorithme, la grande fiabilité de la modélisation de l'interaction des corps physiques nous permet d'observer la danse enchanteresse des systèmes planétaires et des amas de galaxies. Dans cet article, j'examinerai l'un des algorithmes d'optimisation les plus intéressants et les plus originaux. Le simulateur de mouvement des objets spatiaux est également fourni.