Ant colony systems optimization applied to BNF grammars rule derivation (ACORD algorithm)

Ant colony systems have been widely employed in optimization issues primarily focused on path finding optimization, such as travelling salesman problem. The main advantage lies in the choice of the edge to be explored, defined using pheromone trails. This paper proposes the use of ant colony systems to explore a Backus–Naur form grammar whose elements are solutions to a given problem. Similar models, without using ant colonies, have been used to solve optimization problems or to automatically generate programs such as grammatical swarm (based on particle swarm optimization) and grammatical evolution (based on genetic algorithms). This work presents the application of proposed ant colony rule derivation algorithm and benchmarks this novel approach in a well-known deceptive problem, the Santa Fe Trail. Proposed algorithm opens the way to a new branch of research in swarm intelligence, which until now has been almost nonexistent, using ant colony algorithms to generate solutions of a given problem described by a BNF grammar with the advantage of genotype/phenotype mapping, described in grammatical evolution. In this case, such mapping is performed based on the pheromone concentration for each production rule. The experimental results demonstrate proposed algorithm outperforms grammatical evolution algorithm in the Santa Fe Trail problem with higher success rates and better solutions in terms of the required steps.