Hybrid Invasive Weed /
Gholamreza Khademi, Hanieh Mohammadi, and Dan Simon
We propose a new variant of invasive weed optimization (IWO). The proposed algorithm features three new components that are typically not present in IWO: (1) migration; (2) gradient descent; and (3) mutation. In standard IWO, each individual uses only its own features (that is, independent solution variables) to randomly distribute new seeds over the search space. In other words, there is no sharing of features among individuals. We propose the application of the migration operator from biogeography-based optimization (BBO) to include feature-sharing capability in IWO. This modification improves the quality of the distributed seeds (that is, new candidate solutions) in the population. To further improve the local search ability of IWO, we propose the addition
of gradient descent. Mutation is activated under certain conditions to increase the diversity of the population, and escape local optima. We demonstrate the performance of this new hybrid IWO / BBO on a set of single-objective benchmarks, and on a real-world cyber-physical system problem to optimize a user intent recognition system for transfemoral amputees. Hybrid IWO / BBO is compared to standard IWO, BBO, blended BBO, and seven other evolutionary optimization algorithms. The Wilcoxon signed rank test is used to statistically compare the algorithms. The results for hybrid IWO / BBO present promising improvements over standard IWO. Out of 25 benchmarks, hybrid IWO / BBO performs better than IWO on 18 problems with dimension 30.
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G. Khademi, H. Mohammadi, and D. Simon, "Hybrid Invasive Weed / Biogeography-Based Optimization," Engineering Applications of Artificial Intelligence, vol. 64C, pp. 213-231, 2017 - pdf, 1.9 MB
Last Revised: November 3, 2017