Modern optimization algorithms typically require the setting of a large number of parameters
to optimize their performance. The immediate goal of automatic algorithm configuration is to …

Parameter tuning, that is, to find appropriate parameter settings (or configurations) of
algorithms so that their performance is optimized, is an important task in the development …

Swarm Intelligence (SI) is a popular multi-agent framework that has been originally inspired
by swarm behaviors observed in natural systems, such as ant and bee colonies. In a system …

A metaheuristic is a collection of algorithmic concepts that can be used to define heuristic
methods applicable to a wide set of optimization problems for which exact/analytical …

The design and development of metaheuristic algorithms can be time-consuming and
difficult for a number of reasons including the complexity of the problems being tackled, the …

Metaheuristic and heuristic methods have many tunable parameters, and choosing their
values can increase their ability to deal with hard optimization problems. Automated …

This paper proposes the African Buffalo Optimization (ABO) which is a new metaheuristic
algorithm that is derived from careful observation of the African buffalos, a species of wild …

Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm inspired
by humpback whale hunting behavior. WOA has many similarities with other swarm …

Several grammar-based genetic programming algorithms have been proposed in the
literature to automatically generate heuristics for hard optimization problems. These …

The artificial bee colony (ABC) algorithm is a popular metaheuristic that was originally
conceived for tackling continuous function optimization tasks. Over the last decade, a large …