Adaptive Chemical Reaction Optimization for Global Numerical Optimization
This work addresses parameter tuning for optimization algorithms, but it is incremental as it builds on an existing metaheuristic.
The paper tackled the parameter tuning effort in Chemical Reaction Optimization (CRO) by reducing the number of parameters and developing an adaptive scheme, resulting in ACRO, which showed superior performance over canonical CRO on a benchmark of continuous problems.
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.