A Hybrid Algorithm for Metaheuristic Optimization
This work addresses optimization challenges in machine learning, but it appears incremental as it builds on existing metaheuristic methods.
The authors tackled the problem of non-convex optimization by proposing a hybrid algorithm that combines metaheuristic optimizers as communicating agents, and they applied it to benchmark functions and support-vector machine classification problems.
We propose a novel, flexible algorithm for combining together metaheuristicoptimizers for non-convex optimization problems. Our approach treatsthe constituent optimizers as a team of complex agents that communicateinformation amongst each other at various intervals during the simulationprocess. The information produced by each individual agent can be combinedin various ways via higher-level operators. In our experiments on keybenchmark functions, we investigate how the performance of our algorithmvaries with respect to several of its key modifiable properties. Finally,we apply our proposed algorithm to classification problems involving theoptimization of support-vector machine classifiers.