Evolutionary bacterial foraging algorithm to solve constraint numerical optimization problems

A version of Modified Bacterial Foraging Optimization Algorithm to solve Constraints Numerical Optimization is tested. The proposal uses mutation operator, skew mechanism and local search operator. To prove the effectiveness of the mechanism and adaptations proposed, 24 well-known test problems are solved along set experiments. Performance measures are used for validating results obtained by the proposal and they are compared against state-of-the-art algorithms. The results show that the proposed algorithm is able to generate feasible solutions within of feasible region with few evaluations and improves them over the generations. Moreover, the results are competitive against the comparison algorithms based on performance measures found in the literature. Bacterial foraging optimization, mutation operator, swarm intelligence, Constrained optimization, premature convergence.