Nature-Inspired Intelligence Methods and Applications
Qingzheng Xu, Ana Maria A. C. Rocha, Erik Cuevas, Iztok Fister · Mathematical Problems in Engineering · 2022
[Excerpt] Research in nature-inspired intelligence methods and applications has increased exponentially in the past decade. Inspired by a natural phenomenon from biology, physics, or sociology, population-based nature-inspired algorithms aim to achieve satisfactory results for many difficult optimization problems effectively. Compared with deterministic optimization methods, they have many advantages, such as scalability, adaptability, collective robustness, and individual simplicity. However, they still face many challenges that require further research. The target of this special issue was to provide a comprehensive and latest collection of research works on various aspects of population-based nature-inspired algorithms, as well as its potential application in various sciences and engineering domains. This special issue received 15 submissions in total. The authors were from 26 affiliations in 7 countries. Each submitted article was subject to assessment by at least two independent reviewers. After a fair and rigorous peer-review process, 6 of them are published in the special issue, with an acceptance rate of 40.0%. The research paper submitted by Cheng et al., entitled “A novel crow search algorithm based on improved flower pollination”, proposed a crow search algorithm based on an improved flower pollination algorithm (IFCSA) to prevent stagnation and convergence to local minima. In order to balance the global search and local search capabilities, an inverse incomplete gamma function was first introduced to make the awareness probability decrease nonlinearly. In addition, a cross-pollination strategy with Cauchy mutation was also introduced, to avoid the blindness of individual location update. Experimental results on benchmark problems show that this algorithm has better performance than the original crow search algorithm. [...]