一种新的全局优化算法:碳循环算法

New Global Optimization Algorithm:Carbon Cycle Algorithm

随着人类科学技术水平的高速发展,在应用研究、工程设计等领域存在维数大、阶数高、目标函数多、约束条件复杂等传统算法难以求解的困难问题需要优化和解决。以计算机运算与解决问题水平的持续发展为基础,元启发式优化算法被提出并被证明解决以上类别的问题要优于传统优化方法。作为对元启发式优化算法的补充,文中提出了一种新的用于连续全局优化的元启发式算法:碳循环算法(Carbon Cycle Algorithm,CCA)。该算法模拟了碳元素的自然循环过程,具体为通过模拟动植物呼吸、动物捕食、动植物死亡、分解者分解以及植物光合作用过程,以此为策略来更好地探索和利用搜索空间。通过与一些著名的优化算法在13个基准函数上的测试对比结果,剖析了该算法的计算收敛过程。测试结果表明,该算法具有一定的竞争力并能够解决具有挑战性的问题,可以在大多数基准函数上提供更好的求解精度。

With the rapid development of human science and technology,in the fields of applied research and engineering design,there are problems of large dimensions,high order,many objective functions,and complex constraints,which are difficult to solve by traditional algorithms and need to be optimized and resolved.Based on the continuous development of computer operation and problem solving level,metaheuristic optimization algorithms have been proposed and proved to be superior to traditional optimization methods in solving the above categories of problems.As a complement to the metaheuristic optimization algorithm,this paper proposes a new metaheuristic algorithm,called the carbon cycle algorithm(CCA),for continuous global optimization.This algorithm simulates the carbon element cycle in nature(mainly the biosphere).Plant respiration,animal respiration,animal predation,plant death process,animal death process,decomposer’s de-composition and plant photosynthesis process are simulated by this algorithm which uses these as search strategies to explore and search space.The computational convergence procedure of the proposed algorithm is dissected by comparing the result of some well-known optimization algorithms on the 13 benchmark functions.The test results of benchmark functions reveal that the proposed algorithm can provide an excellent solution which proves CCA can solve the challenging problem and is a competitive algorithm.CCA provides better solution accuracy on most benchmark functions.