An efficient multi-objective HBMO algorithm for distribution feeder reconfiguration

This paper presents an efficient multi-objective honey bee mating optimization (MHBMO) evolutionary algorithm to solve the multi-objective distribution feeder reconfiguration (DFR). The purposes of the DFR problem are to decrease the real power loss, the number of the switching operations and the deviation of the voltage at each node. Conventional algorithms for solving the multi-objective optimization problems convert the multiple objectives into a single objective using a vector of the user-predefined weights. This transformation has several drawbacks. For instance, the final solution of the algorithms extensively depends on the values of the weights. This paper presents a new MHBMO algorithm for the DFR problem. The proposed algorithm utilizes several queens and considers the queens as an external repository to save non-dominated solutions found during the search process. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository within the limits. The proposed algorithm is tested on two distribution test feeders.