Jaya algorithm (JAYA) is a recently developed metaheuristic algorithm for global optimization problems. JAYA has a very simple structure and only needs the essential population size and terminal condition for solving optimization problems. However, JAYA is easy to get trapped in the local optimum for solving complex global optimization problems due to its single learning strategy. Motivated by this disadvantage of JAYA, this paper presents an improved JAYA, named comprehensive learning JAYA algorithm (CLJAYA), for solving engineering design optimization problems. The core idea of CLJAYA is the designed comprehensive learning mechanism by making full use of population information. The designed comprehensive learning mechanism consists of three different learning strategies to improve the global search ability of JAYA. To investigate the performance of CLJAYA, CLJAYA is first evaluated by the well-known CEC 2013 and CEC 2014 test suites, which include 50 multimodal test functions and eight unimodal test functions. Then CLJAYA is employed to solve five real-world engineering optimization problems. Experimental results demonstrate that CLJAYA can achieve better solutions for most test problems than JAYA and the other compared algorithms, which indicates the designed comprehensive learning mechanism is very effective. In addition, the source code of the proposed CLJAYA can be loaded from https://www.mathworks.com/matlabcentral/fileexchange/82134-the-source-code-for-cljaya.

Jaya算法（JAYA）是最近开发的用于全局优化问题的元启发式算法。JAYA具有非常简单的结构，只需要基本的人口规模和最终条件即可解决优化问题。但是，JAYA由于其单一的学习策略，很容易陷入解决最优全局优化问题的局部最优中。出于JAYA的这一缺点，本文提出了一种改进的JAYA，称为综合学习JAYA算法（CLJAYA），用于解决工程设计优化问题。CLJAYA的核心思想是通过充分利用人口信息来设计综合学习机制。设计的综合学习机制包括三种不同的学习策略，以提高JAYA的全局搜索能力。为了研究CLJAYA的性能，首先由著名的CEC 2013和CEC 2014测试套件对CLJAYA进行了评估，其中包括50个多峰测试功能和八个单峰测试功能。然后，使用CLJAYA解决五个实际工程优化问题。实验结果表明，与JAYA和其他比较算法相比，CLJAYA可以更好地解决大多数测试问题，这表明所设计的综合学习机制非常有效。此外，可以从https://www.mathworks.com/matlabcentral/fileexchange/82134-the-source-code-for-cljaya加载建议的CLJAYA的源代码。然后使用CLJAYA解决五个实际工程优化问题。实验结果表明，与JAYA和其他比较算法相比，CLJAYA可以更好地解决大多数测试问题，这表明所设计的综合学习机制非常有效。此外，可以从https://www.mathworks.com/matlabcentral/fileexchange/82134-the-source-code-for-cljaya加载建议的CLJAYA的源代码。然后，使用CLJAYA解决五个实际工程优化问题。实验结果表明，与JAYA和其他比较算法相比，CLJAYA可以更好地解决大多数测试问题，这表明所设计的综合学习机制非常有效。此外，可以从https://www.mathworks.com/matlabcentral/fileexchange/82134-the-source-code-for-cljaya加载建议的CLJAYA的源代码。