Real-Coded Chemical Reaction Optimization with Different Perturbation Functions

TL;DR

This paper investigates how different probability distributions used as perturbation functions in Chemical Reaction Optimization affect its performance on various continuous optimization problems, providing guidelines for selecting suitable distributions.

Contribution

It compares four different distributions for CRO's perturbation function and analyzes their impact on solving diverse benchmark functions, offering practical design guidelines.

Findings

Different distributions perform better on different problem types.

Gaussian and Cauchy distributions show distinct advantages depending on problem characteristics.

The study guides the choice of perturbation functions for improved CRO performance.

Abstract

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.