The recently introduced Marine Predators Algorithm (MPA) exhibits competitive performance in solving optimization problems. However, it often prematurely converges due to an imbalance between its exploration and exploitation capabilities. Therefore, in this paper, an improved MPA variant using a proposed Local Escaping Operator (LEO) is introduced. In the MPA, solution candidates are replaced by better candidates from the previous iteration, mimicking a memory of already visited prey-abundant areas. This indicates a weak inter-dependence between the candidates in the population and possible acceptance of new solution candidates created outside the algorithm without damaging the optimization process. Consequently, in the proposed approach, the worst candidates are replaced with solutions created by the LEO. The LEO is based on representative solutions, taking into account the relationship between predators and the characteristics of their population. The approach is experimentally compared with the state-of-the-art meta-heuristics on 82 test functions, including IEEE Congress on Evolutionary Computation test suites (CEC’14 and CEC’17) and three engineering problems. The results show the superiority of the LEO-MPA over the MPA and recent algorithms. Furthermore, in this paper, the suitability of the hybridization of meta-heuristics with the LEO is discussed and successful attempts with the best algorithms are shown.

最近推出的海洋捕食者算法 (MPA) 在解决优化问题方面表现出具有竞争力的性能。然而，由于其探索和开发能力之间的不平衡，它往往会过早地收敛。因此，在本文中，介绍了使用建议的局部转义运算符 (LEO) 的改进 MPA 变体。在 MPA 中，候选解决方案被前一次迭代中更好的候选替代，模仿已经访问过的猎物丰富区域的记忆。这表明总体中的候选者之间存在弱的相互依赖性，并且可能接受在算法之外创建的新解决方案候选者，而不会破坏优化过程。因此，在提议的方法中，最差的候选者被 LEO 创建的解决方案所取代。LEO 基于具有代表性的解决方案，考虑到捕食者与其种群特征之间的关系。该方法在 82 个测试函数上与最先进的元启发式进行了实验比较，包括 IEEE 进化计算大会测试套件（CEC'14 和 CEC'17）和三个工程问题。结果表明 LEO-MPA 优于 MPA 和最近的算法。此外，在本文中，讨论了元启发式与 LEO 混合的适用性，并展示了使用最佳算法的成功尝试。包括 IEEE 进化计算大会测试套件（CEC'14 和 CEC'17）和三个工程问题。结果表明 LEO-MPA 优于 MPA 和最近的算法。此外，在本文中，讨论了元启发式与 LEO 混合的适用性，并展示了使用最佳算法的成功尝试。包括 IEEE 进化计算大会测试套件（CEC'14 和 CEC'17）和三个工程问题。结果表明 LEO-MPA 优于 MPA 和最近的算法。此外，在本文中，讨论了元启发式与 LEO 混合的适用性，并展示了使用最佳算法的成功尝试。