一种求解复杂优化问题的新型人工鱼群算法

受自然界群体生物繁衍生息行为的启发，提出了一种新型人工鱼群算法。新算法将鱼群行为概括为：觅食行为、繁衍行为和逃逸行为。其中，繁衍行为是指利用进化算法的选择和交叉算子赋予了人工鱼繁衍能力;逃逸行为利用了云模型云滴的随机性和稳定倾向性的特点，由基本云发生器实现人工鱼变异操作。新算法还采用了双曲正切函数建立了步长参数自适应模型，从而动态调整算法寻优能力。通过10个标准测试函数的计算验证和分析比较，表明了提出的新型自适应混合人工鱼群算法具有计算精度高、搜索速度快等特点。

Novel artificial fish-swarm algorithm for solving complex optimization problem

In this paper, the social behaviors of fish swarm are classified in three ways： foraging behavior, reproductive behavior, and flight behavior. Inspired by this, a Novel Artificial Fish Swarm Algorithm（NAFSA） is proposed, which integrates the mutation strategy and evolution behavior into the social behaviors of fish swarm. In the case of mutation strategy, the basic cloud generator is used as the mutation operator because of the properties of randomness and stable tendency of a normal cloud model. For the reproductive behavior, the selection, and crossover operator in evolutionary algorithm are applied to define the reproductive ability of an artificial fish. Furthermore, the parameters of step and visual are developed in forms of hyperbolic tangent function to adjust the optimize performance dynamically during iterations process. Ten standard test functions are used as the benchmark to validate the effectiveness of the NAFSA. Experimental results have confirmed the superiority of NAFSA in terms of both solution quality and convergence speed, and shown broad application prospect in engineering.