[1] zhao c, zhou y, chen z. decomposition-based evolutionary algorithm with automatic estimation to handle many-objective optimization problem[j]. information sciences, 2021, 546: 1030-1046. (sci一区top, if 8.1)
[2]jzhao c, zhou y, lai x. an integrated framework with evolutionary algorithm for multi-scenario multi-objective optimization problems[j]. information sciences, 2022, 600: 342-361. (sci一区top, if 8.1)
[3] zhao c, zhou y, hao y. decomposition-based evolutionary algorithm with dual adjustments for many-objective optimization problems[j]. swarm and evolutionary computation, 2022, 75: 101168. (sci一区top, if 10.0)
[4] hao y, zhao c*, li z, et al. a learning and evolution-based intelligence algorithm for multi-objective heterogeneous cloud scheduling optimization[j]. knowledge-based systems, 2024, 286: 111366. (sci一区top, if 8.8)
[5] zhao c, hao y, gong d, et al. an ensemble learning-based multi-population evolutionary framework for multi-scenario multi-objective optimization problems[j]. knowledge-based systems, 2023: 110708. (sci一区top, if 8.8)
[6] zhao c, zhou y, hao y, et al. a bi-layer decomposition algorithm for many-objective optimization problems[j]. applied intelligence, 2022, 52(13): 15122-15142. (sci二区, if 5.3)
[7] hao y, si b, zhao c. topology transformation-based multi-path algorithm for urban rail transit network[j]. transportation research part c: emerging technologies, 2022, 136: 103540. (sci一区top, if 9.02)
[8] hao y, si b, zhao c. a novel shortest path algorithm with topology transformation for urban rail transit network[j]. computers & industrial engineering, 2022, 169: 108223. (sci二区, if 7.9)
[9] hao y, bingfeng s, zhao c. streamlined hierarchical topology network-based passenger flow assignment of urban rail transit[j]. transportation research record, 2022, 2676(8): 683-696. (sci四区, if 1.7)
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