关键词: 苍狼优化算法, 自主搜索, 淘汰重组机制, 变异

Abstract: Analyzing the development and exploration performance of the standard Grey Wolf Optimizer（GWO）, a hybrid grey wolf optimizer（MAR-GWO） is proposed, the search domain is fully extended. In MAR-GWO, the initiative searching operators are introduced for Wolf Alfa, Beta, and Delta respectively to advance more on effect and speed, the eliminating-reconstructing mechanism is applied for poor search wolves to improve the search efficiency and the mutation with a given probability is adopted for excellent search wolves to increase the individual diversity and to protect it against the local optimum. To verify the validity of MAR-GWO, the experiment about 13 global optimization problems are done, and GWO, GWO-EPD（another improved GWO）, PSO, and EA are involved as comparisons. The result shows its success rate is greater, its convergence speed is faster, and it is not easy to fall into the local optimum, meaning MAR-GWO has a strong competitiveness in intelligent algorithms.

Key words: grey wolf optimizer, initiative search, eliminating-reconstructing mechanism, mutation