As a meta-heuristic algorithm, bat algorithm (BA) is based on the characteristics of bat-based echolocation and has been widely used in various aspects of optimization problems since it appeared. However, the original BA still has many shortcomings, such as insufficient local search ability, lack of diversity and poor performance on high-dimensional optimization problems. To overcome these weaknesses, this paper proposes an improved BA with extremal optimization (EO) algorithm (IBA-EO) to improve the performance of BA. In IBA-EO, an improved update strategy is proposed to obtain the solutions generating from the random selected bats to enhance the global search capability. The exploitation ability is improved by EO algorithm with excellent local search capability. Furthermore, Boltzmann selection and a monitor mechanism are employed to keep suitable balance between exploration ability and exploitation ability. To testify the performance of IBA-EO in handling various optimization problems, this study considers four groups of contrast experiments. Extensive simulation results demonstrate that IBA-EO can achieve a strong competitive performance by comparing with other fifteen well-established algorithms in terms of accuracy, reliability and statistical tests.

蝙蝠算法（BA）作为一种元启发式算法，是基于基于蝙蝠的回声定位的特性，并且自出现以来已广泛用于优化问题的各个方面。但是，原始BA仍然存在许多不足，例如本地搜索能力不足，缺乏多样性以及在高维优化问题上的性能较差。为了克服这些缺点，本文提出了一种带有极值优化（EO）算法（IBA-EO）的改进BA，以提高BA的性能。在IBA-EO中，提出了一种改进的更新策略，以获得从随机选择的蝙蝠生成的解，以增强全局搜索能力。EO算法提高了开发能力，具有出色的局部搜索能力。此外，采用玻尔兹曼选择和监测机制来保持勘探能力与开发能力之间的适当平衡。为了证明IBA-EO在处理各种优化问题方面的性能，本研究考虑了四组对比实验。大量的仿真结果表明，与其他十五种公认的算法相比，IBA-EO可以在准确性，可靠性和统计测试方面取得强大的竞争性能。