Feature selection

Operator (biology)

Feature (linguistics)

Mathematics

Transcription factor

Gene

Algorithm

Random walk

Statistics

Lévy flight

Machine learning

Philosophy

Artificial intelligence

Repressor

Chemistry

Computer science

Linguistics

Mutation

Computational Science and Engineering

Biochemistry

Selection (genetic algorithm)

Neural Computing and Applications

Seagull optimization algorithm (SOA) is a recent bio-inspired technique utilized to improve the constrained large-scale problems in low computational cost and quick convergence speed. However, the globally optimized search space for the SOA is linear, which means that the SOA’s global search capability could not be fully utilized. Thus, we propose an improved SOA algorithm (ISOA) using Lévy flight and mutation operators. The ISOA obtains some Lévy flight features, which improves the original SOA by performing large jumps, making the search escape from the local optima and begin at a different search space region. The mutation operator, which improves the exploration–exploitation trade-off, allows the catch of the optimal solution quickly and accurately. In order to examine the performance of the proposed ISOA approach, three experiments were conducted. The first one evaluates the ISOA in solving the global optimization problem. The second one is a comparative study based on twenty benchmark datasets to evaluate the general capability of ISOA in feature selection, compared to ten recent and well-established algorithms constructed using the other meta-heuristics methods. Furthermore, the third experiment is conducted using a real dataset with various face poses to investigate the efficiency of the ISOA in pose-variation recognition. Compared to the other meta-heuristics methods, the results show that the proposed model is more accurate and efficient in global optimization, feature selection purposes, and pose variation recognition. Furthermore, the ISOA approach outperforms the other methods proposed in the state-of-the-art literature.

Improved seagull optimization algorithm using Lévy flight and mutation operator for feature selection