https://www.google.com/search?q=Heuristic+vs+Metaheuristic

i.e.,

A heuristic is a technique aimed to solve a problem faster when traditional techniques are too slow. A metaheuristic is a higher-level technique or heuristic that seeks, generates, or selects a heuristic that may provide a sufficiently good solution to an optimization problem (Attea et al. 2021).Jan 11, 2022

Some university departments used to offer course(s) in Metaheuristics

https://www.google.com/search?q=graduate+course+Metaheuristics

I agree with Steftcho

A very good and concise definiton if given by Wikipedia when states that "Metaheuristics are strategies that guide the search process"

Metaheuristic is then a process to define strategies for finding solutions for a problem.

In my opinion, it is perhaps similar to SWOT (Strength, Weakness, Opportunities, and Threats, that deterrmines strategies.

A MCDM heuristic method, choosing those sterategies as alternatives, can find which is the most appropriate for a problem.

Heuristic refers to algorimth that finds a solution for a problem

Heuristic and metaheuristic algorithms are both types of optimization algorithms used to find approximate solutions to complex problems. Here's a brief overview of the differences between heuristic and metaheuristic algorithms:

Heuristic Algorithms:

Metaheuristic Algorithms:

Summary:

• Scope:Heuristics are problem-specific and tailored to a particular domain. Metaheuristics are general-purpose and applicable across a wide range of problems.
• Search Strategy:Heuristics often use domain-specific knowledge to guide the search. Metaheuristics provide a more abstract, problem-independent framework for exploration and exploitation.
• Applicability:Heuristics are suitable for well-defined problems where domain knowledge is crucial. Metaheuristics are versatile and can be applied to complex problems without requiring in-depth knowledge of the specific problem.

In summary, while heuristics are specialized methods designed for specific problems, metaheuristics provide a more generic and flexible approach to optimization across various domains.

Heuristics and Meta-heuristics are both approximation techniques without guarantees of finding an optimal solution. The Difference is that Metaheuristics includes at least one mechanism for escaping local optima solutions. Therefore, the prefix Meta is in their name; popular heuristics algorithms in optimization, such as Gradient Descent or Hill Climbing, cannot escape local optima solutions.

Dear Alejandro Santigo

Linear Programming gives you the optimal solution, provided that it exists

It is not an approximation technique

What do you mean by scaping local optimum solutions?

Yes Nolberto,

Furthermore, technically, any convex optimization technique/method gives the optimal solution.

https://www.google.com/search?q=does+any+convex+optimization+technique+give+optimal+solution

https://www.google.com/search?q=%22Convex+Optimization+Problem%22+%22only+one+optimal+solution%2C+which+is+globally+optimal%22

Dear Steftcho

Very good material the references that you suggest, thank you

However, the reader needs to have a certain knowledge of lineal algebra as well as Lineal Programming, to understand for instance, the concept of a feasible convex polytope, to say nothing of defining the optimal vertex

Dear Shahad,

Inside the Stochastic family, there are Heuristic and Metaheuristic approaches. According to Yang (2010a) Heuristic means “to find" or “to discover by trial and error" (Khattar et al., 2019). On the other hand, Metaheuristic, meta-means “beyond" or “higher level", and they generally perform better than simple heuristics (Yang, 2010a).

You can see more detailed information about this topic in this paper: Article Hybrid approaches to optimization and machine learning metho...

Since you are interested in metaheuristic algorithms, I recommend studying the following systematic review.

Article A Systematic Review of the Whale Optimization Algorithm: The...