Andrey Dik / 프로필

Статья посвящена подробному анализу алгоритма Exchange Market Algorithm (EMA), который вдохновлен поведением трейдеров на фондовом рынке. Алгоритм моделирует процесс торговли акциями, где участники рынка с разным уровнем успеха применяют различные стратегии для максимизации прибыли.

Что если алгоритм оптимизации мог бы помнить свои прошлые путешествия и использовать эту память для поиска лучших решений? BSA делает именно это — балансируя между исследованием нового и возвращением к проверенному. В статье раскрываем секреты алгоритма. Простая идея, минимум параметров и стабильный результат.

In this article, we take a closer look at the DEA algorithm, a metaheuristic optimization method inspired by dolphins' unique ability to find prey using echolocation. From mathematical foundations to practical implementation in MQL5, from analysis to comparison with classical algorithms, we will examine in detail why this relatively new method deserves a place in the arsenal of researchers facing optimization problems.

The article explores one of the most interesting non-gradient optimization algorithms, which learns to understand the geometry of the objective function. We will focus on the classical implementation of CMA-ES with a slight modification - replacing the normal distribution with the power one. We will thoroughly examine the math behind the algorithm, as well as practical implementation, and check where CMA-ES is unbeatable and where it should be avoided.

Eagle Strategy is an algorithm that mimics the eagle's two-phase hunting strategy: global search via Levy flights using Mantegna method, alternating with intense local exploitation using the firefly algorithm, a mathematically sound approach to balancing exploration and exploitation, and a bioinspired concept that combines two natural phenomena into a single computational method.

Biogeography-Based Optimization (BBO) is an elegant global optimization method inspired by natural processes of species migration between islands within archipelagos. The algorithm is based on a simple yet powerful idea: high-quality solutions actively share their characteristics, while low-quality ones actively adopt new features, creating a natural flow of information from the best solutions to the worst. A unique adaptive mutation operator provides an excellent balance between exploration and exploitation. BBO demonstrates high efficiency on a variety of tasks.

Deterministic Oscillatory Search (DOS) algorithm is an innovative global optimization method that combines the advantages of gradient and swarm algorithms without the use of random numbers. The fitness oscillation and slope mechanism allows DOS to explore complex search spaces in a deterministic manner.

The Camel Algorithm, developed in 2016, simulates the behavior of camels in the desert to solve optimization problems, taking into account temperature, supply, and endurance. This article also presents a modified version of the algorithm (CAm) with key improvements: the use of a Gaussian distribution in generating solutions and the optimization of the oasis effect parameters.

The article presents a new metaheuristic method based on a fractal approach to partitioning the search space for solving optimization problems. The algorithm sequentially identifies and separates promising areas, creating a self-similar fractal structure that concentrates computing resources on the most promising areas. A unique mutation mechanism aimed at better solutions ensures an optimal balance between exploration and exploitation of the search space, significantly increasing the efficiency of the algorithm.

We continue studying the chaotic optimization algorithm. The second part of the article deals with the practical aspects of the algorithm implementation, its testing and conclusions.

This is an improved chaotic optimization algorithm (COA) that combines the effects of chaos with adaptive search mechanisms. The algorithm uses a set of chaotic maps and inertial components to explore the search space. The article reveals the theoretical foundations of chaotic methods of financial optimization.

The article presents a comprehensive analysis of the Coral Reef Optimization (CRO) algorithm, a metaheuristic method inspired by the biological processes of coral reef formation and development. The algorithm models key aspects of coral evolution: broadcast spawning, brooding, larval settlement, asexual reproduction, and competition for limited reef space. Particular attention is paid to the improved version of the algorithm.

The article explores the Battle Royale Optimizer algorithm — a metaheuristic in which solutions compete with their nearest neighbors, accumulate “damage,” are replaced when a threshold is exceeded, and periodically shrink the search space around the current best solution. It presents both pseudocode and an MQL5 implementation of the CAOBRO class, including neighbor search, movement toward the best solution, and an adaptive delta interval. Test results on the Hilly, Forest, and Megacity functions highlight the strengths and limitations of the approach. The reader is provided with a ready-to-use foundation for experimentation and tuning key parameters such as popSize and maxDamage.

The new proprietary optimization algorithm NOA2 (Neuroboids Optimization Algorithm 2) combines the principles of swarm intelligence with neural control. NOA2 combines the mechanics of a neuroboid swarm with an adaptive neural system that allows agents to self-correct their behavior while searching for the optimum. The algorithm is under active development and demonstrates potential for solving complex optimization problems.

The article presents the Central Force Optimization (CFO) algorithm inspired by the laws of gravity. It explores how principles of physical attraction can solve optimization problems where "heavier" solutions attract less successful counterparts.

A new bioinspired optimization metaheuristic, NOA (Neuroboids Optimization Algorithm), combines the principles of collective intelligence and neural networks. Unlike conventional methods, the algorithm uses a population of self-learning "neuroboids", each with its own neural network that adapts its search strategy in real time. The article reveals the architecture of the algorithm, the mechanisms of self-learning of agents, and the prospects for applying this hybrid approach to complex optimization problems.

Successful Restaurateur Algorithm (SRA) is an innovative optimization method inspired by restaurant business management principles. Unlike traditional approaches, SRA does not discard weak solutions, but improves them by combining with elements of successful ones. The algorithm shows competitive results and offers a fresh perspective on balancing exploration and exploitation in optimization problems.

The BOA method is inspired by the classic game of billiards and simulates the search for optimal solutions as a game with balls trying to fall into pockets representing the best results. In this article, we will consider the basics of BOA, its mathematical model, and its efficiency in solving various optimization problems.