Compared to other techniques, particle swarm optimization is more frequently utilized because of its ease of use and low variability. However, it is complicated to find the best possible solution in the search space in large-scale optimization problems. Moreover, changing algorithm variables does not influence algorithm convergence much. The PSO algorithm can be combined with other algorithms. It can use their advantages and operators to solve this problem. Therefore, this paper proposes the onlooker multi-parent crossover discrete particle swarm optimization (OMPCDPSO). To improve the efficiency of the DPSO algorithm, we utilized multi-parent crossover on the best solutions. We performed an independent and intensive neighborhood search using the onlooker bees of the bee algorithm. The algorithm uses onlooker bees and crossover. They do local search (exploitation) and global search (exploration). Each of these searches is among the best solutions (employed bees). The proposed algorithm was tested on the allocation problem, which is an NP-hard optimization problem. Also, we used two types of simulated data. They were used to test the scalability and complexity of the better algorithm. Also, fourteen 2D test functions and thirteen 30D test functions were used. They also used twenty IEEE CEC2005 benchmark functions to test the efficiency of OMPCDPSO. Also, to test OMPCDPSO's performance, we compared it to four new binary optimization algorithms and three classic ones. The results show that the OMPCDPSO version had high capability. It performed better than other algorithms. The developed algorithm in this research (OMCDPSO) in 36 test functions out of 47 (76.60%) is better than other algorithms. The Onlooker bees and multi-parent operators significantly impact the algorithm's performance.

翻译：与其他技术相比，粒子群优化因其易用性和低变异性而更常被采用。然而，在大规模优化问题中，在搜索空间内寻找最优可行解仍具复杂性。此外，改变算法变量对算法收敛性的影响有限。PSO算法可与其他算法融合，借助其优势及算子来解决此问题。因此，本文提出旁观多父代交叉离散粒子群优化（OMPCDPSO）。为提升DPSO算法效率，我们对最优解实施了多父代交叉操作，并利用蜂群算法的旁观蜂进行了独立且密集的邻域搜索。该算法通过旁观蜂与交叉机制分别执行局部搜索（开发）与全局搜索（探索），每种搜索均在最优解（雇佣蜂）中进行。所提算法在分配问题（一种NP难优化问题）上进行了测试。同时，我们采用两类模拟数据以检验算法的可扩展性与复杂度。此外，使用14个二维测试函数、13个三十维测试函数及20个IEEE CEC2005基准函数评估OMPCDPSO性能。为测试OMPCDPSO表现，我们将其与四种新型二进制优化算法及三种经典算法进行对比。结果表明，OMPCDPSO版本展现出高性能，优于其他算法。本研究所开发的算法（OMPCDPSO）在47个测试函数中有36个（76.60%）表现更优。旁观蜂与多父代算子对算法性能具有显著影响。