Multi-leader comprehensive learning particle swarm optimiser possesses strong exploitation ability, by randomly selecting and assigning best-ranked particles as leaders during optimisation. However, it lacks the ability to preserve diversity by mainly focusing on exploitation, and adopting random selection to choose leaders also hinders its performance. To overcome these deficiencies, an improved multi-leader comprehensive learning particle swarm optimiser is proposed based on Karush-Kuhn-Tucker proximity measure and Gravitational Search Algorithm. Karush-Kuhn-Tucker proximity measure is employed to determine the best-ranked particles’ contribution to the swarm’s convergence to influence their selection as guides for other particles. Gravitational Search Algorithm is introduced to preserve the algorithm’s ability to maintain diversity. To curb premature convergence and particles getting trapped in a local optimum, an adaptive reset velocity strategy is incorporated to activate stagnated particles. Some benchmark test functions are employed to compare the proposed algorithm with seven other peer algorithms. The results verify that our proposed algorithm possesses a better capability to elude local optima with faster convergence than other algorithms. Furthermore, to prove the efficacy of the application of our proposed algorithm in real-life, the algorithms are used to train a Feedforward neural network for epilepsy detection, of which our proposed algorithm outperforms the others.

多领导者综合学习粒子群优化器具有很强的开发能力，通过在优化过程中随机选择和分配排名最佳的粒子作为领导者。然而，它缺乏以开发为主的保持多样性的能力，采用随机选择来选择领导者也阻碍了其表现。为了克服这些不足，基于Karush-Kuhn-Tucker邻近度量和引力搜索算法，提出了一种改进的多领导者综合学习粒子群优化器。采用 Karush-Kuhn-Tucker 邻近度量来确定排名最高的粒子对群收敛的贡献，以影响它们作为其他粒子的指南的选择。引入引力搜索算法以保持算法保持多样性的能力。为了抑制过早收敛和粒子陷入局部最优，结合了自适应重置速度策略来激活停滞粒子。一些基准测试函数被用来将所提出的算法与其他七种对等算法进行比较。结果验证了我们提出的算法比其他算法具有更好的逃避局部最优的能力和更快的收敛速度。此外，为了证明我们提出的算法在现实生活中的应用的有效性，这些算法被用来训练一个用于癫痫检测的前馈神经网络，其中我们提出的算法优于其他算法。一些基准测试函数被用来将所提出的算法与其他七种对等算法进行比较。结果验证了我们提出的算法比其他算法具有更好的逃避局部最优的能力和更快的收敛速度。此外，为了证明我们提出的算法在现实生活中的应用的有效性，这些算法被用来训练一个用于癫痫检测的前馈神经网络，其中我们提出的算法优于其他算法。一些基准测试函数被用来将所提出的算法与其他七种对等算法进行比较。结果验证了我们提出的算法比其他算法具有更好的逃避局部最优的能力和更快的收敛速度。此外，为了证明我们提出的算法在现实生活中的应用的有效性，这些算法被用来训练一个用于癫痫检测的前馈神经网络，其中我们提出的算法优于其他算法。