Success of metaheuristic algorithms depends on the efficient balance between of exploration and exploitation phases. Any optimization algorithm requires a combination of diverse exploration and proper exploitation to avoid local optima. This paper proposes a new improved version of the Woodpecker Mating Algorithm (WMA), based on opposition-based learning, known as the OWMA aiming to develop exploration and exploitation capacities and establish a simultaneous balance between these two phases. This improvement consists of three major mechanisms, the first of which is the new Distance Opposition-based Learning (DOBL) mechanism for improving exploration, diversity, and convergence. The second mechanism is the allocation of local memory of personal experiences of search agents for developing the exploitation capacity. The third mechanism is the use of a self-regulatory and dynamic method for setting the Hα parameter to improve the Running Away function (RA) performance. The ability of the proposed algorithm to solve 23 benchmark mathematical functions was evaluated and compared to that of a series of the latest and most popular metaheuristic methods reviewed in the research literature. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the classification problem on four biomedical datasets and three function approximation datasets. In addition, the OWMA algorithm was evaluated in five optimization problems constrained by the real world. The simulation results proved the superior and promising performance of the proposed algorithm in the majority of evaluations. The results prove the superiority and promising performance of the proposed algorithm in solving very complicated optimization problems.

中文翻译：

---

OWMA：一种改进的自律啄木鸟交配算法，使用基于对立的学习和本地内存分配来解决优化问题

元启发式算法的成功取决于勘探和开发阶段之间的有效平衡。任何优化算法都需要结合多种探索和适当利用来避免局部最优。本文提出了一种新的改进版本的啄木鸟交配算法（WMA），它基于对立的学习方法，即OWMA，旨在发展勘探和开发能力并在这两个阶段之间建立平衡。这项改进包括三个主要机制，第一个是新的基于距离对立的学习（DOBL）机制，用于改善探索，多样性和融合。第二种机制是分配本地存储的搜索代理人的个人经历，以提高开发能力。第三种机制是使用自调节和动态方法来设置Hα参数，以改善“跑步功能”（RA）的性能。评估了所提出算法解决23种基准数学函数的能力，并将其与研究文献中回顾的一系列最新和最受欢迎的元启发式方法进行了比较。所提出的算法还用作多层感知器（MLP）神经网络训练器，以解决四个生物医学数据集和三个函数逼近数据集的分类问题。此外，在现实世界约束的五个优化问题中评估了OWMA算法。仿真结果证明了该算法在大多数评估中的优越性和有希望的性能。