Partial Leader Optimizer
نویسندگان
چکیده
A new swarm intelligence-based metaheuristic optimizer, namely Partial Leader Optimizer (PLO), is presented. PLO contains several autonomous agents that represent the solution. The best solution represents collective intelligence, i.e., leader. has distinct mechanics in finding acceptable during given iteration. Every agent moves to a specified target every Two options can be chosen determine target. First, calculated by pushing virtual away from corresponding agent. Second, randomly within space. This selection conducted stochastically based on threshold set manually before Then, candidates are generated between and agent's current location. distance adjacent same. candidate updates Simulation implemented observe analyze PLO’s performance. well-known 23 benchmark functions used as optimization problems. In this simulation, benchmarked with marine predator algorithm (MPA), particle (PSO), average subtraction-based optimizer (ASBO), slime mold (SMA), pelican (POA). result shows competitive compared these algorithms, especially solving fixed-dimension multimodal functions. better than PSO, MPA, SMA, ASBO, POA optimizing 22, 19, 18, 9, 20 out of 23, respectively.
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ژورنال
عنوان ژورنال: International Journal on Advanced Science, Engineering and Information Technology
سال: 2023
ISSN: ['2088-5334', '2460-6952']
DOI: https://doi.org/10.18517/ijaseit.13.4.18217