Sardine Optimization Algorithm with Agile Locality and Globality Strategies for Real Optimization Problems

How to steadily find satisfactory solutions for high-dimensional multimodal and composition optimization problems is still a challenging issue. To fight against this pain-point problem, we propose sardine optimization algorithm (SOA) with agile locality and globality strategies for real optimization problems. Inspired by the survival philosophy of sardines, SOA simulates the transformation, migration, reproduction, elimination, and spread of sardines. As a varied-population-size optimization algorithm, the features of SOA are summarized as two key points. (i) Agile locality and globality strategies use adjacent and corresponding period ratios to control the local and global search behaviors. To our best knowledge, these strategies are a new technical road to balance exploration and exploitation efforts. (ii) Besides, SOA uses unique search operators based on the center movement of sardine schools. Specifically, when the center of one sardine school moves in these search operators, all sardines in this school also move in the same direction. This mobility style is different from most meta-heuristic algorithms, as far as we know. We used all unconstrained optimization problems in the CEC2013 test suite and six real-world constrained optimization problems as our benchmarks. SOA outperforms eight algorithms (like the two winning algorithms of CEC2013 and CEC2014), especially for high-dimension multimodal and composition optimization problems. For instance, Wilcoxon results between SOA and the two winning algorithms of CEC2013 and CEC2014 for 90-dimensional unconstrained optimization problems of the CEC2013 test suite are 16:10 and 18:8. The coding of SOA can be downloaded from “ https://github.com/1654402787/SOA ” (unzip password: soasoasoa1357).