A bacterial gene recombination algorithm for solving constrained optimization problems
Creature evolution manifests itself in the improved ability of species to adapt to their surroundings. Swarm intelligence and gene optimization are found in the population of interacting agents that are able to self-organize and self-strengthen. In this study, a new gene-based algorithm for constrained optimization problems is proposed and called ''bacterial gene recombination algorithm'' (BGRA). BGRA is inspired by the intelligent behavior of gene recombination in bacterial swarming. By referring to each phase of the well-regulated gene evolutionary process of bacteria, BGRA enables the exploration of problems and solution exploitation. For illustration, a set of constrained optimization problems are taken from the literature for testing purposes. In addition, satisfactory feasible solutions for constrained optimization problems were obtained by combining the BGRA and a penalty function method. Experimental results show that the proposed algorithm can yield near-optimal and stable solutions compared to the relevant literature, and thus, it can be an efficient alternative in the solving of constrained optimization problems.