Bacterial foraging optimization algorithm with mutation to solve constrained problems

Abstract

A simple version of a Swarm Intelligence algorithm called bacterial foraging optimization algorithm with mutation and dynamic stepsize (BFOAM-DS) is proposed. The bacterial foraging algorithm has the ability to explore and exploit the search space through its chemotactic operator. However, premature convergence is a disadvantage. This proposal uses a mutation operator in a swim, similar to evolutionary algorithms, combined with a dynamic stepsize operator to improve its performance and allows a better balance between the exploration and exploitation of the search space. BFOAM-DS was tested in three well-known engineering design optimization problems. Results were analyzed with basic statistics and common measures for nature-inspired constrained optimization problems to evaluate the behavior of the swim with a mutation operator and the dynamic stepsize operator. Results were compared against a previous version of the proposed algorithm to conclude that BFOAM-DS is competitive and better than a previous version of the algorithm.

Bacterial Foraging Based Algorithm Front-end to Solve Global Optimization Problems

“…Furthermore, notice that each solution is similar to the global optimum or best-known solution. This can be confirmed by the feasibility rate, success rate, and success performance [27] where the 30 independent executions obtained feasible and equal or better solutions than the global optimum known in the state of the art. Finally, about 15,314 evaluations are required to obtain a feasible and optimal solution for this problem with this algorithm.…”