Real-Coded Chemical Reaction Optimization with Different Perturbation Functions
This work provides incremental guidelines for designing CRO for various optimization problems, primarily benefiting researchers in metaheuristic optimization.
The paper tackled the problem of selecting perturbation functions in Chemical Reaction Optimization (CRO) for continuous optimization by testing four probability distributions (Gaussian, Cauchy, exponential, and modified Rayleigh) on benchmark functions, finding that different problem characteristics prefer different distributions.
Chemical Reaction Optimization (CRO) is a powerful metaheuristic which mimics the interactions of molecules in chemical reactions to search for the global optimum. The perturbation function greatly influences the performance of CRO on solving different continuous problems. In this paper, we study four different probability distributions, namely, the Gaussian distribution, the Cauchy distribution, the exponential distribution, and a modified Rayleigh distribution, for the perturbation function of CRO. Different distributions have different impacts on the solutions. The distributions are tested by a set of well-known benchmark functions and simulation results show that problems with different characteristics have different preference on the distribution function. Our study gives guidelines to design CRO for different types of optimization problems.