改进花朵授粉算法

智能系统试图模拟人类专家来解决复杂的现实问题。问题的领域从工程、工业到医学、教育都各不相同。在大多数情况下,系统需要根据多个输入进行决策,但是搜索空间通常很大,因此很难使用传统的算法进行决策；元启发式算法可以用作寻找最优解的一种工具。因此,改进元启发式技术和现有算法是必要的。本文介绍了一种改进的花朵授粉算法(FPA)。将标准的FPA与克隆选择算法(CSA)结合,应用到23个优化基准函数上,并对其进行测试。将改进算法与五种著名的优化算法(模拟退火、遗传算法、花授粉算法、蝙蝠算法和萤火虫算法)进行比较。实验结果表明,相比标准FPA和其他四种方法,改进花朵授粉算法能够找到更精确的解。

A Modified Flower Pollination Algorithm

Expert and intelligent systems try to simulate intelligent human experts in solving complex real-world problems. The domain of problems varies from engineering and industry to medicine and education. In most situations, the system is required to take decisions based on multiple inputs, but the search space is usually very huge so that it will be very hard to use the traditional algorithms to take a decision; at this point, the metaheuristic algorithms can be used as an alternative tool to find near-optimal solutions. Thus, inventing new metaheuristic techniques and enhancing the current algorithms is necessary. In this paper, we introduced an enhanced variant of the Flower Pollination Algorithm (FPA). We hybridized the standard FPA with the Clonal Selection Algorithm (CSA) and tested the new algorithm by applying it to 23 optimization benchmark problems. The proposed algorithm is compared with five famous optimization algorithms, namely, Simulated Annealing, Genetic Algorithm, Flower Pollination Algorithm, Bat Algorithm, and Firefly Algorithm. The results show that the proposed algorithm is able to find more accurate solutions than the standard FPA and the other four techniques. The superiority of the proposed algorithm nominates it for being a part of intelligent and expert systems.