Abstract: 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.

Key words: Carbon cycle, Metaheuristic algorithm, Global optimization, Benchmark function test, Optimal solution