Geodesy

Gene

Algorithm

Gaussian

Benchmark (surveying)

Linguistics

Engineering

Philosophy

Robustness (evolution)

Agricultural engineering

Selection (genetic algorithm)

Biochemistry

Computer science

Mathematical optimization

Grasshopper

Geology

Paleontology

Pattern recognition (psychology)

Mathematics

Geography

Feature selection

Feature (linguistics)

Computational chemistry

Artificial intelligence

Global optimization

Chemistry

Optimization problem

Machine learning

Multi-objective optimization

Expert Systems with Applications

As a recent meta-heuristic algorithm, the uniqueness of the grasshopper optimization algorithm (GOA) is to imitate the biological features of grasshoppers for single-objective optimization cases. Despite its advanced optimization ability, the basic GOA has a set of shortcomings that pose challenges in numerous practical scenarios. The GOA core limit is its early convergence to the local optimum and suffering from slow convergence. To mitigate these concerns, this study adopts the elite opposition-based learning and bare-bones Gaussian strategy to extend GOA's global and local search capabilities and effectively balance the exploration and exploitation inclinations. Specifically, elite opposition-based learning can help find better solutions at the early stage of exploration, while the bare-bones Gaussian strategy has an excellent ability to update the search agents. To evaluate the robustness of the proposed Enhanced GOA (EGOA) based on global constrained and unconstrained optimization problems, a straight comparison was made between the proposed EGOA and other meta-heuristics on 30 IEEE CEC2017 benchmark tasks. Moreover, we applied it experimentally to structural design problems and its binary version to the feature selection cases. Findings demonstrate the effectiveness of EGOA and its binary version as an acceptable tool for optimization and feature selection purposes.

Enhanced Gaussian bare-bones grasshopper optimization: Mitigating the performance concerns for feature selection