Adaptive Chemical Reaction Optimization for Global Numerical Optimization

TL;DR

This paper introduces an adaptive version of Chemical Reaction Optimization (ACRO) that automatically tunes parameters, improving performance across various optimization problems, especially continuous ones, compared to the original CRO.

Contribution

The paper develops an adaptive scheme for CRO that reduces parameter tuning effort and enhances its adaptability and effectiveness in solving diverse optimization problems.

Findings

ACRO outperforms canonical CRO in benchmark tests.

Adaptive parameter tuning improves optimization efficiency.

ACRO demonstrates robustness across different problem types.

Abstract

A newly proposed chemical-reaction-inspired metaheurisic, Chemical Reaction Optimization (CRO), has been applied to many optimization problems in both discrete and continuous domains. To alleviate the effort in tuning parameters, this paper reduces the number of optimization parameters in canonical CRO and develops an adaptive scheme to evolve them. Our proposed Adaptive CRO (ACRO) adapts better to different optimization problems. We perform simulations with ACRO on a widely-used benchmark of continuous problems. The simulation results show that ACRO has superior performance over canonical CRO.