| Summary: | A new multi-objective optimized bacterial foraging algorithm - Hybrid Multi-Objective Optimized Bacterial Foraging Algorithm (HMOBFA) is presented in this article. The proposed algorithm combines the crossover-archives strategy and the life-cycle optimization strategy, look for the best method through research area. The crossover-archive strategy with an external archive and internal archive is assigned to different selection principles to focus on diversity and convergence separately. Additionally, according to the local landscape to satisfy population diversity and variability as well as avoiding redundant local searches, individuals can switch their states periodically throughout the colony lifecycle with the life-cycle optimization strategy. all of which may perform significantly well. The performance of the algorithm was examined with several standard criterion functions and compared with other classical multi-objective majorization methods. The examiner results show that the HMOBFA algorithm can achieve a significant enhancement in performance compare with other method and handles many-objective issues with solid complexity, convergence as well as diversity. The HMOBFA algorithm has been proven to be an excellent alternative to past methods for solving the improvement of many-objective problems.
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