What kind of problems can be solved with metaheuristic algorithms?

Classical metaheuristics, such as Iterated Local Search, Hill Climbing, Genetic Algorithms, Simulated Annealing, TabuSearch and Ant Colony Optimization, have shown their suitability to solve complex scheduling problems, space allocation problems, and clustering problems, among others.

What is the difference between heuristics and Metaheuristics?

So, heuristics are often problem-dependent, that is, you define an heuristic for a given problem. Metaheuristics are problem-independent techniques that can be applied to a broad range of problems. An heuristic is, for example, choosing a random element for pivoting in Quicksort.

What is heuristic optimization?

Heuristic designates a computational procedure that determines an optimal solution by iteratively trying to improve a candidate solution with regard to a given measure of quality. Other methods having a similar meaning as heuristic are derivative-free, direct search, and black-box optimization techniques.

What is meta heuristic approach?

Metaheuristic is an approach method based on a heuristic method that does not rely on the type of the problem. The metaheuristic method can be distinguished into two which are metaheuristic with single-solution based (local search) and metaheuristic based on population (random search).

What is particle swarm optimization used for?

In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.

Which heuristic is the best?

In psychology, the take-the-best heuristic is a heuristic (a simple strategy for decision-making) which decides between two alternatives by choosing based on the first cue that discriminates them, where cues are ordered by cue validity (highest to lowest).

Why are metaheuristics important?

In combinatorial optimization, by searching over a large set of feasible solutions, metaheuristics can often find good solutions with less computational effort than optimization algorithms, iterative methods, or simple heuristics. As such, they are useful approaches for optimization problems.

Which is better PSO or GA?

Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.