# A Hybrid Algorithm for Metaheuristic Optimization

## TL;DR

This paper introduces a flexible hybrid metaheuristic algorithm that combines multiple optimizers with inter-agent communication, improving non-convex optimization and SVM classification performance.

## Contribution

It presents a novel algorithm that integrates metaheuristic optimizers as communicating agents, enhancing optimization capabilities and adaptability.

## Key findings

- Performance varies with key algorithm parameters.
- Effective on benchmark non-convex functions.
- Improves SVM classifier optimization.

## Abstract

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.