Benchmarking NLopt and state-of-art algorithms for Continuous Global Optimization via Hybrid IACO$_\mathbb{R}$

TL;DR

This paper compares various algorithms from the NLopt library with a hybrid local search strategy for continuous global optimization, demonstrating that a hybrid of Mtsls1 and BFGS outperforms other methods on benchmark problems.

Contribution

It introduces a hybrid local search approach combining Mtsls1 and NLopt algorithms, with an adaptive switching mechanism, and provides an extensive performance comparison on standard benchmarks.

Findings

Hybrid Mtsls1-BFGS algorithm performs best on benchmarks.

Adaptive switching improves optimization performance.

NLopt algorithms' performance varies across benchmark problems.

Abstract

This paper presents a comparative analysis of the performance of the Incremental Ant Colony algorithm for continuous optimization ($IACO_\mathbb{R}$), with different algorithms provided in the NLopt library. The key objective is to understand how the various algorithms in the NLopt library perform in combination with the Multi Trajectory Local Search (Mtsls1) technique. A hybrid approach has been introduced in the local search strategy by the use of a parameter which allows for probabilistic selection between Mtsls1 and a NLopt algorithm. In case of stagnation, the algorithm switch is made based on the algorithm being used in the previous iteration. The paper presents an exhaustive comparison on the performance of these approaches on Soft Computing (SOCO) and Congress on Evolutionary Computation (CEC) 2014 benchmarks. For both benchmarks, we conclude that the best performing algorithm is a hybrid variant of Mtsls1 with BFGS for local search.