What are metaheuristic algorithms used for?
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or …
What is the main reason for using computational intelligence approaches especially in form of metaheuristics?
In addition, metaheuristics are usually considered as a higher level of heuristics, because metaheuristic algorithms are not simple trial-and-error approaches and metaheuristics are designed to learn from past solutions, to be biased towards better moves, to select the best solutions, and to construct sophisticated …
Is PSO meta heuristic algorithm?
Genetic algorithm (GA) and swarm intelligence [20–22] are two well-known population-based meta-heuristic approaches. In the literature, swarm intelligence-based approaches, such as PSO, have several advantages over GA [26,27].
What is whale optimization algorithm?
Introduction. The whale optimization algorithm (WOA) was proposed by Mirjalili and Lewis [1] in 2016. It is a new swarm intelligence optimization algorithm that simulates humpback whale hunting behavior. The main idea of the algorithm is to solve the target problem by imitating the whale’s predatory behavior [2].
Which is the best metaheuristic algorithm?
Most widely known Meta-heuristic algorithms are Genetic algorithm (GA), simulated annealing (SA) and Tabu search (TS).
Which is the most efficient metaheuristic for optimization?
Metaheuristics are widely recognized as efficient approaches for many hard optimization problems. This paper provides a survey of some of the main metaheuristics. It outlines the components and concepts that are used in various metaheuristics in order to analyze their similarities and differences.
How is a metaheuristic different from a heuristic?
A metaheuristic is an algorithm designed to solve approximately a wide range of hard optimization problems without having to deeply adapt to each problem. Indeed, the greek prefix “meta”, present in the name, is used to indicate that these algorithms are “higher level” heuristics, in contrast with problem-specific heuristics.
How are metaheuristics different from population based algorithms?
Unlike population-based metaheuristics, they start with a single initial solution and move away from it, describing a trajectory in the search space. Some of them can be seen as “intelligent” extensions of local search algorithms.
What do you mean by hard optimization problem?
We roughly define hard optimization problems as problems that cannot be solved to optimality, or to any guaranteed bound, by any exact (deterministic) method within a “reasonable” time limit.