mql5

In the previous article, we considered the evolution of social groups where they moved freely in the search space. However, here I propose that we change this concept and assume that groups move between sectors, jumping from one to another. All groups have their own centers, which are updated at each iteration of the algorithm. In addition, we introduce the concept of memory both for the group as a whole and for each individual particle in it. Using these changes, our algorithm now allows

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In this installment of our ongoing series on the Modified Grid-Hedge EA in MQL5, we delve into the intricacies of the Grid EA. Building on our experience with the Simple Hedge EA, we now apply similar techniques to improve the performance of the Grid EA. Our journey begins with an existing Grid EA, which serves as our canvas for mathematical exploration. The goal? To dissect the underlying strategy, unravel its intricacies, and uncover the theoretical underpinnings that drive its behavior.

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