This is the question which everyone who researches about Heuristics and Metaheuristics might encounter. Someone believes both are equal to each other; that is the wrong opinion. In this post, we find an acceptable answer for this common question among researchers.
Generally speaking, Heuristic is like an approximate (not approximation) solution to a problem. At first, we have to know the difference between approximate and approximation; this difference is that the first is about getting a good guess of the solution of a problem, but that you don't really know how good it is. The second is about getting a solution for which you can prove how close it is to the optimal solution. So, by Heuristics, you don't really know how good the final solution is.
In addition, heuristics are problem-dependent, that is, you define an heuristic for a given or specific problem, for this case you can give an example of heuristics designed for 0-1 Knapsack problem such as greedy repair and improvement heuristics algorithms. On the other hand, Metaheuristics are problem-independent algorithms that can be applied to a broad range of problems, for instance, a Simulated Annealing designed for Travelling Salesman problem (TSP) can be used for Quadratic Assignment Problem (QAP) with the minor changes in just solution representation. Therefore, a metaheuristic knows nothing about the problem it will be applied, it can treat functions as 'black boxes'.
To summarize, someone can say that a heuristic exploits problem-dependent information to find a 'good enough' solution to a specific problem, while metaheuristics are, like design patterns, general algorithmic ideas that can be applied to a broad range of problems.
- Resource: https://stackoverflow.com/questions
- For more information you can check the Questions & Answers at ReseachGate.
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