Global continuous optimization is populated by its implementation in many real-world applications. Such optimization problems are often solved by nature-inspired and meta-heuristic algorithms, including the firefly algorithm (FA), which offers fast exploration and exploitation. To further strengthen FA’s search for global optimum, a Levy-flight FA (LF-FA) has been developed through sampling from a Levy distribution instead of the traditional uniform one. However, due to its poor exploitation in local areas, the LF-FA does not guarantee fast convergence. To address this problem, this paper provides an adaptive logarithmic spiral-Levy FA (AD-IFA) that strengthens the LF-FA’s local exploitation and accelerates its convergence. Our AD-IFA is integrated with logarithmic-spiral guidance to its fireflies’ paths, and adaptive switching between exploration and exploitation modes during the search process. Experimental results show that the AD-IFA presented in this paper consistently outperforms the standard FA and LF-FA for 29 test functions and 6 real cases of global optimization problems in terms of both computation speed and derived optimum.

全局连续优化是由其在许多实际应用程序中的实现组成的。此类优化问题通常通过自然启发和元启发式算法（包括萤火虫算法（FA））来解决，该算法可提供快速的探索和利用。为了进一步加强FA对全球最优飞机的搜寻，已经通过从Levy分布中采样而不是传统的统一模式开发了Levy-flight FA（LF-FA）。但是，由于LF-FA在当地的开发不充分，因此不能保证快速收敛。为了解决这个问题，本文提供了一种自适应对数螺旋征税FA（AD-IFA），可以加强LF-FA的局部开发并加速其收敛。我们的AD-IFA与萤火虫的飞行路径集成了对数螺旋引导，搜索过程中在探索和开发模式之间进行自适应切换。实验结果表明，本文提出的AD-IFA在29个测试功能和6个实际全局优化问题的实际情况方面，在计算速度和推导的最优方面均始终优于标准FA和LF-FA。