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An Adaptive Framework to Tune the Coordinate Systems in Nature-Inspired Optimization Algorithms.

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This study introduces ACoS, an adaptive coordinate system framework for nature-inspired optimization algorithms (NIOAs). ACoS enhances search efficiency by dynamically adjusting coordinate systems based on function landscape features.

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Area of Science:

Computational Intelligence
Optimization Algorithms
Evolutionary Computation

Background:

Nature-inspired optimization algorithms (NIOAs) performance is sensitive to coordinate system choice.
Fixed coordinate systems limit NIOA efficiency across diverse function landscapes.

Purpose of the Study:

To propose ACoS, a novel framework for adaptively tuning coordinate systems in NIOAs.
To enhance the efficiency and adaptability of NIOAs in complex optimization problems.

Main Methods:

Developed ACoS framework utilizing an Eigen coordinate system derived from population distribution.
Employed covariance matrix adaptation and archiving for cumulative population information.
Coupled Eigen and original coordinate systems with an adaptive probability vector for individual-based selection.

Main Results:

ACoS demonstrated effectiveness when applied to particle swarm optimization and differential evolution.
Evaluated on 30 test functions across 30D and 50D dimensions.
Experimental results confirmed the framework's capability to identify function landscape modality and improve search efficiency.

Conclusions:

ACoS significantly improves NIOA performance by adaptively optimizing coordinate systems.
The Eigen coordinate system, informed by population distribution, allows for better landscape feature identification.
This adaptive approach offers a promising direction for advancing nature-inspired optimization techniques.