All (not yet) about Strategy Tester, Optimization and Cloud - page 10

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Population optimization algorithms: Bat algorithm (BA)

Population optimization algorithms: Bat algorithm (BA)

Bats are amazing animals. Scientists believe that the first bats appeared 65-100 million years ago living side by side with dinosaurs.


The Bat Algorithm (BA) is a heuristic algorithm introduced by Yang in 2010 that mimics the echolocation behavior of bats to perform global optimization.
BA is a new and modern swarm-like algorithm that performs a search process using artificial bats as search agents simulating the natural sound pulse volume and emission frequency of real bats.

Population optimization algorithms: Bat algorithm (BA)
Population optimization algorithms: Bat algorithm (BA)
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In this article, I will consider the Bat Algorithm (BA), which shows good convergence on smooth functions.
 

Population optimization algorithms: Invasive Weed Optimization (IWO)

Population optimization algorithms: Invasive Weed Optimization (IWO)

The invasive weed metaheuristic algorithm is a population-based optimization algorithm that finds the overall optimum of the optimized function by simulating the compatibility and randomness of a weed colony.
Weed optimization algorithm refers to nature-inspired population algorithms and reflects the behavior of weeds in a limited area in the struggle for survival for a limited amount of time.
Population optimization algorithms: Invasive Weed Optimization (IWO)
Population optimization algorithms: Invasive Weed Optimization (IWO)
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The amazing ability of weeds to survive in a wide variety of conditions has become the idea for a powerful optimization algorithm. IWO is one of the best algorithms among the previously reviewed ones.
 

Population optimization algorithms: Bacterial Foraging Optimization (BFO)

The Bacterial Foraging Optimization (BFO) algorithm is a fascinating optimization technique that can be used to find approximate solutions to extremely complex or impossible numerical function maximization/minimization problems.
The algorithm is widely recognized as a global optimization algorithm for distributed optimization and control.
Population optimization algorithms: Bacterial Foraging Optimization (BFO)
Population optimization algorithms: Bacterial Foraging Optimization (BFO)
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E. coli bacterium foraging strategy inspired scientists to create the BFO optimization algorithm. The algorithm contains original ideas and promising approaches to optimization and is worthy of further study.
 

Population optimization algorithms: Harmony Search (HS)

Population optimization algorithms: Harmony Search (HS)

The Harmony Search (HS) method is an emerging metaheuristic optimization algorithm that has been used to solve numerous complex problems over the past decade. The Harmony Search algorithm (HS) was first proposed in 2001 by Z. W. Geem. The HS method is inspired by the founding principles of musical improvisation and the search for musical harmony. The combinations of perfect harmony of sounds are matched with the global extremum in the multidimensional optimization problem, while the musical improvisation process is matched with a search for the extremum.
Population optimization algorithms: Harmony Search (HS)
Population optimization algorithms: Harmony Search (HS)
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In the current article, I will study and test the most powerful optimization algorithm - harmonic search (HS) inspired by the process of finding the perfect sound harmony. So what algorithm is now the leader in our rating?
 

Population optimization algorithms: Gravitational Search Algorithm (GSA)

Population optimization algorithms: Gravitational Search Algorithm (GSA)

Gravitational Search Algorithm (GSA) was proposed by E. Rashedi to solve the optimization problem, especially non-linear problems, following the principles of Newton's law of gravitation. In the proposed algorithm, particles are considered as objects and their performance is estimated taking into account their masses.
Population optimization algorithms: Gravitational Search Algorithm (GSA)
Population optimization algorithms: Gravitational Search Algorithm (GSA)
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GSA is a population optimization algorithm inspired by inanimate nature. Thanks to Newton's law of gravity implemented in the algorithm, the high reliability of modeling the interaction of physical bodies allows us to observe the enchanting dance of planetary systems and galactic clusters. In this article, I will consider one of the most interesting and original optimization algorithms. The simulator of the space objects movement is provided as well.
 

Population optimization algorithms: Monkey algorithm (MA)

Population optimization algorithms

Monkey Algorithm (MA) is a metaheuristic search algorithm. This article will describe the main components of the algorithm and present solutions for the ascent (upward movement), local jump and global jump. The algorithm was proposed by R. Zhao and W. Tang in 2007. The algorithm simulates the behavior of monkeys as they move and jump over mountains in search of food. It is assumed that the monkeys come from the fact that the higher the mountain, the more food on its top.
Population optimization algorithms: Monkey algorithm (MA)
Population optimization algorithms: Monkey algorithm (MA)
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In this article, I will consider the Monkey Algorithm (MA) optimization algorithm. The ability of these animals to overcome difficult obstacles and get to the most inaccessible tree tops formed the basis of the idea of the MA algorithm.
 
Comments that do not relate to this topic, have been moved to "_Trash_ Forum Cleanup".
 
Comments that do not relate to this topic, have been moved to "_Trash_ Forum Cleanup".
 

Population optimization algorithms: Saplings Sowing and Growing up (SSG)

Population optimization algorithms: Saplings Sowing and Growing up (SSG)

There are many optimization methods inspired by processes occurring in nature, such as evolutionary computation, artificial immunology, population methods and others. SSG is basically defined as iterative generation and combination processes working with a garden of potential solutions called seedlings. The Saplings Sowing and Growing (SSG) algorithm was proposed by A. Karci with co-authors in 2002. The algorithm is inspired by the evolution of growing trees and models the growth and branching of trees.
Population optimization algorithms: Saplings Sowing and Growing up (SSG)
Population optimization algorithms: Saplings Sowing and Growing up (SSG)
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Saplings Sowing and Growing up (SSG) algorithm is inspired by one of the most resilient organisms on the planet demonstrating outstanding capability for survival in a wide variety of conditions.
 

Understand and Use MQL5 Strategy Tester Effectively

Understand and Use MQL5 Strategy Tester Effectively

We will try to cover the most popular points about these previous topics to well understand what we need to deal with as programmers or developers.
Understand and Use MQL5 Strategy Tester Effectively
Understand and Use MQL5 Strategy Tester Effectively
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There is an essential need for MQL5 programmers or developers to master important and valuable tools. One of these tools is the Strategy Tester, this article is a practical guide to understanding and using the strategy tester of MQL5.
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