Harmony search algorithm with differential evolution based control parameter co-evolution and its application in chemical process dynamic optimization

Abstract

Harmony search algorithm with differential evolution based control parameter co-evolution and its application in chemical process dynamic optimization FAN Qin-qin(范勤勤), WANG Xun-hua(王循华), YAN Xue-feng(颜学峰)Abstract: A modified harmony search algorithm with co-evolutional control parameters (DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual (i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.

An Overview of the Application of Harmony Search for Chemical Engineering Optimization

“…Dynamic optimization problems were solved by Fan et al [308] using HS hybridized with differential evolution, which has co-evolutional control parameters. Dynamic optimization implies the resolution of problems described by differential equations that represent the change of design (target) variables with respect to time [309].…”

Improved Seagull Optimization Algorithm Combined with an Unequal Division Method to Solve Dynamic Optimization Problems

“…The indirect method mainly solves the optimality condition (necessary condition) of the original problem, thereby indirectly obtaining the optimal solution of the original problem. So far, the optimization methods for chemical engineering problems mainly include dynamic programming (DP) [3], control variable parameterization (CVP) [4], and also swarm intelligence algorithm [5,6].…”