Discussing the article: "Coyote Optimization Algorithm (COA)"

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Check out the new article: Coyote Optimization Algorithm (COA).

Introducing an MQL5 implementation of the Coyote Optimization Algorithm: packs with local alphas, a median-based tendency, and built-in crossover enable parallel exploration of regions of the search space while keeping premature convergence in check. The algorithm is integrated into C_AO and has been tested on the standard test bench and in a composite anti-cheat test. The article includes code, pseudocode, and an analysis of the operators that enable the use of COA to optimize the parameters of a trading system.

Anyone who has ever run a trading system optimization knows that feeling. The parameter space is vast: indicator periods, stop-loss and take-profit levels, time and volatility filters — even a simple Expert Advisor can easily have a dozen dimensions. An exhaustive search is physically impossible. Premature convergence is the main enemy of anyone seeking stable parameters in a multimodal landscape, and the profit landscape of a trading system is precisely that: rugged, noisy, and full of narrow peaks and broad, gentle plateaus — which are actually what interest us most.

You have an optimization problem, and you know that relying on a single swarm converging to a single point is a gamble. This raises the question: Are there algorithms that can explore multiple regions of space in parallel without sacrificing convergence speed? In our series of articles on population-based optimization algorithms, we have already examined dozens of metaheuristics — from classic swarm-based algorithms to the most exotic ones — and run each through the same test bench: the Hilly, Forest, and Megacity functions, three problem dimensionalities, a fixed budget of 10,000 calls to the objective function, and ten repetitions. This standardized protocol lets us compare all the algorithms under equal conditions rather than taking their developers at their word. The series' ranking table has shown time and again that a catchy name and an appealing metaphor from nature are no guarantee of success.

Today's guest in the series is the Coyote Optimization Algorithm (COA), proposed by Juliano Pierezan and Leandro dos Santos Coelho in 2018. At first glance, it is just another animal in the sprawling zoo of metaheuristics. But COA has a distinctive structural feature: its population is divided from the outset into isolated packs, each of which leads its own social life, evolves around its own leader, and only occasionally exchanges individuals with its neighbors. It is not a swarm that breathes as a single entity — it is an archipelago of small populations that naturally preserves diversity. There are also two mechanisms absent from a typical swarm: the pack's collective culture, expressed through the median (rather than the mean — and this matters), and the birth of pups — genetic crossover built directly into the swarm-based scheme.

Author: Andrey Dik