Multi-objective scheduling problem: Hybrid approach using fuzzy assisted cuckoo search algorithm

This article proposes a hybrid cuckoo search algorithm (CSA) integrated with fuzzy system for solving multi-objective unit commitment problem (MOUCP). The power system stresses the need for economic, non-polluting and reliable operation. Hence three conflicting functions such as fuel cost, emission and reliability level of the system are considered. CSA mimics the breeding behavior of cuckoos, where each individual searches the most suitable nest to lay an egg (compromise solution) in order to maximize the egg’s survival rate and achieve the best habitat society. Fuzzy set theory is used to create the fuzzy membership search domain where it consists of all possible compromise solutions. CSA searches the best compromise solution within the fuzzy search domain simultaneously tuning the fuzzy design boundary variables. Tuning of fuzzy design variables eliminate the requirement of expertise needed for setting these variables. On solving MOUCP, the proposed binary coded CSA finds the ON/OFF status of the generating units while the real coded CSA solves economic dispatch problem (EDP) and also tunes the fuzzy design boundary variables. The proposed methodology is tested and validated for both the single and multi-objective optimization problems. The effectiveness of the proposed technique is demonstrated on 6, 10, 26 and 40 unit test systems by comparing its performance with other methods reported in the literature.