Hierarchy Influenced Differential Evolution: A Motor Operation Inspired Approach

TL;DR

This paper introduces a hierarchical variant of Differential Evolution inspired by the human motor system, improving optimization performance through a distributed control structure tested on standard benchmarks.

Contribution

The paper presents a novel hierarchical crossover mechanism for Differential Evolution, inspired by biological control systems, enhancing optimization efficiency.

Findings

Outperforms standard algorithms on benchmark tests

Effective hierarchical control improves convergence

Demonstrates robustness across diverse functions

Abstract

Operational maturity of biological control systems have fuelled the inspiration for a large number of mathematical and logical models for control, automation and optimisation. The human brain represents the most sophisticated control architecture known to us and is a central motivation for several research attempts across various domains. In the present work, we introduce an algorithm for mathematical optimisation that derives its intuition from the hierarchical and distributed operations of the human motor system. The system comprises global leaders, local leaders and an effector population that adapt dynamically to attain global optimisation via a feedback mechanism coupled with the structural hierarchy. The hierarchical system operation is distributed into local control for movement and global controllers that facilitate gross motion and decision making. We present our algorithm as a variant of the classical Differential Evolution algorithm, introducing a hierarchical crossover operation. The discussed approach is tested exhaustively on standard test functions as well as the CEC 2017 benchmark. Our algorithm significantly outperforms various standard algorithms as well as their popular variants as discussed in the results.

Full text

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