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A multi-objective cat swarm optimization algorithm based on two-archive mechanism for UAV 3-D path planning problem
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DOI：10.1016/j.asoc.2024.112306.png)
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摘要
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Achieving a balance between convergence and diversity is crucial in addressing multi-objective optimization problems (MOPs). In this paper, a multi-objective cat swarm optimization based on anew two-archive mechanism (MOCSO_TA) is proposed for the above challenge. In this approach, solutions that can promote convergence are stored by the convergence archive (CA). While solutions that can enhance diversity in the population are saved in the diversity archive (DA). Path planning is a critical process for unmanned aerial vehicles (UAVs), involving the identification of a route that is short and secure. Multi-objective algorithms have become a crucial approach for addressing UAV path planning problem, thus motivating the use of the proposed MOCSO_TA in path planning problem. The proposed algorithm is compared with two sets of representative multi-objective algorithms on the DTLZ, WFG, and ZDT benchmark problems. The experimental results demonstrate the outstanding performance of MOCSO_TA. The effectiveness of the MOCSO_TA is demonstrated by designing two terrains and comparing it with various multi-objective algorithms. The results confirmed the superiority of MOCSO_TA.
Keyword:
Multi-objective optimization
Cat swarm optimization
Two-archive mechanism
UAV
Path planning
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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