| Citation: | YAN X B，FANG Y W，PENG W S. Multi-objective Harris Hawk optimization algorithm based on adaptive Gaussian mutation[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（8）：2636-2645 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0686
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The Harris Hawk optimization algorithm tends to fall into local optimum due to poor population diversity when solving multi-objective optimization problems. To address this issue, a multi-objective Harris Hawk optimization algorithm based on adaptive Gaussian mutation was proposed. To make the solution shrink to the Pareto frontier better, a prey location method based on the grid division method was proposed. To improve the convergence performance of the algorithm, the individual location of the population beyond the interval was updated to the prey location. An adaptive Gaussian mutation strategy was used to improve the algorithm diversity and the uniformity of the Pareto frontier population particles. The simulation results show that when the algorithm solves the multi-objective optimization problem, compared with multi-objective genetic algorithm (NSGA-Ⅱ), multi-objective particle swarm optimization (MOPSO), multi-objective gray wolf optimization (MOGWO), and multi-objective Harris Hawk optimization (MOHHO), the optimization accuracy is improved by 8.02%～51.34%, and the convergence speed is improved by 16.67%～40.74%. The research work provides new methods and technical means for solving multi-objective optimization problems.
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