Due to the issue of environmental protection coupled with high energy demand, there was an initiation for exploration of different renewable energy sources. This article aims to optimize the total annual cost of hybrids of wind and solar renewable energy system to satisfy the predesigned load. Minimization of the total annual cost of the system by determining appropriate numbers of the components, so that the desired load can be economically and reliably satisfied under the given constraints. Gravitational Search Algorithm (GSA) was employed for the optimization process. GSA is a recently proposed metaheuristic algorithm which is based on Newton's universal gravitational law of gravity and mass interactions. It uses stochastic rules to escape local optima and find the global optimal solutions. MATLAB codes were designed for the developed fitness function and employed algorithm. The proposed methodology was run for the fitness function through the code and the results were discussed. The result was compared with the results of Particle Swarm Optimization (PSO) and also shown that: GSA has some advantage over PSO algorithm. Even though, the algorithm has several parameters to be adjusted, it is strong in both local and global optimal searches.

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基于引力搜索算法的风光互补可再生能源系统优化

由于环保问题和高能源需求，人们开始探索不同的可再生能源。本文旨在优化风能和太阳能混合可再生能源系统的年总成本，以满足预先设计的负荷。通过确定适当数量的组件来最小化系统的年总成本，以便在给定的约束条件下经济可靠地满足所需的负载。引力搜索算法（GSA）被用于优化过程。GSA 是最近提出的基于牛顿万有引力定律和质量相互作用的元启发式算法。它使用随机规则来逃避局部最优并找到全局最优解。MATLAB 代码是为开发的适应度函数和采用的算法设计的。通过代码对适应度函数运行所提出的方法，并对结果进行了讨论。将结果与粒子群优化算法（PSO）的结果进行了比较，也表明：GSA算法比PSO算法有一定的优势。尽管该算法有几个参数需要调整，但它在局部和全局最优搜索方面都很强大。