The search for powerful optimizers has led to the development of a multitude of
metaheuristic algorithms inspired from all areas. This work focuses on the animal kingdom …

Population diversity is crucial in evolutionary algorithms to enable global exploration and to
avoid poor performance due to premature convergence. This chapter reviews runtime …

Meta-heuristic search algorithms (MHSs) are methods that take their inspiration from nature.
However, the fitness value information used in the design of the update mechanism in MHSs …

Comparing, or benchmarking, of optimization algorithms is a complicated task that involves
many subtle considerations to yield a fair and unbiased evaluation. In this paper, we …

Gravitational search algorithm is an effective population-based algorithm. It simulates the
law of gravity to implement the interaction among particles. Although it can effectively …

Several optimization problems from various types of applications have been efficiently
resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and …

Abstract In Metaheuristic Algorithms (MA), the balance between exploration and exploitation
is a common issue considered an open research problem in the MA community. Besides its …

In many natural environments, there are different forms of living creatures that successfully
accomplish the same task while being diverse in shape and behavior. This biodiversity is …

Exploration, the process of visiting a new region in a search space, and exploitation, the
process of searching in the neighborhood of previously visited regions, are two centerpieces …

The popularity of metaheuristics, especially Swarm Intelligence and Evolutionary Algorithms,
has increased rapidly over the last two decades. Numerous algorithms are proposed each …