New exploration operators for the api algorithm

In this paper, we are interested in the search stratagy of the API algorithm [3] which was the result of a formalization of the foraging strategy of Pachycondyla apicalis ponerin ants [1, 2]. The API algorithm does not use pheromones but performs a coordinated exploration of a search space using fundamental simple rules with which ants search their preys using both a global and a local viewpoints. Starting from their nest, they globally cover a given surface by partitioning it into many hunting sites. Each ant performs a local random exploration of its hunting sites and its site choice is sensitive to the successes previously met on the sites. Besides global features of the API algorithm are well defined, in this work, we show that the original exploration strategy can be modified to improve this already efficient general optimization algorithm.