Control Vector Parameterization‐Based Adaptive Invasive Weed Optimization for Dynamic Processes

Abstract

A novel optimal approach named invasive weed optimization‐control vector parameterization (IWO‐CVP) for chemical dynamic optimization problems is proposed where CVP is used to transform the problem into a nonlinear programming (NLP) problem and an IWO algorithm is then applied to tackle the NLP problem. To improve efficiency, a new adaptive dispersion IWO‐based approach (ADIWO‐CVP) is further suggested to maintain the exploration ability of the algorithm throughout the entire searching procedure. Several classic chemical dynamic optimization problems are tested and detailed comparisons are carried out among ADIWO‐CVP, IWO‐CVP, and other methods. The research results demonstrate that ADIWO‐CVP not only is efficient, but also outperforms IWO‐CVP in terms of both accuracy and convergence speed.

Dynamic Optimization of Chemical Processes Based on Modified Sailfish Optimizer Combined with an Equal Division Method

“…The global optimum obtained by the MSFO after 20 independent runs was 0.76165319 (N = 20) and 0.761594199 (N = 50). Tian et al [34] reached a value of 0.76165319 by control vector parameterization based adaptive invasive weed optimization (ADIWO-CVP), which is the best literature result close to the analytical result. The results of MSFO were close to the ADIWO-CVP and superior to those of other methods, which shows the validity of the proposed algorithm.…”

“…Figure 13 is the optimal temperature control variable curve with N = 20 and N = 50, Figure 14 is the optimal state variable curve, and Figure 15 is the iteration curve for the optimal result. PSO-CVP -0.6105359 [34] IWO-CVP -0.61079180 [35] IACA 10 0.6100 [35] IACA 20 0.6104 [36] GA -0.61072 [36] IKEA 10 0.6101 [36] IKEA 20 0.610426 [36] IKEA 100 0.610781-0.610789 [37] CP-PSO -0.6107847 [37] CP-APSO -0.6107850 [ In the Table 6, Jiang et al [9] used the an efficient multi-objective artificial raindrop algorithm (MOARA) to obtain the value of 5.54 × 10 −2 . Shi et al [10] reached a value of 0.6105359 using optimal control strategies combined with PSO and control vector parameterization (PSO-CVP).…”

“…[24] OCT -0.761594156 [25] ACO-CP -0.76238 [26] IACO-CVP -0.76160 [33] IGA-CVP -0.761595 [34] IWO-CVP 50 0.76159793 [34] ADIWO [26] IACO-CVP -0.76160 [33] IGA-CVP -0.761595 [34] IWO-CVP 50 0.76159793 [34] ADIWO Table 6 shows the optimal target product concentration results of MSFO and 10 different methods for optimizing batch reactor continuous reaction problem. The "~" in Ta- Table 6 shows the optimal target product concentration results of MSFO and 10 different methods for optimizing batch reactor continuous reaction problem.…”

“…Qian et al [33] got a value of 0.761595 using a control vector parameterization method with an iterative genetic algorithm (IGA-CVP). Tian et al [34] obtained values of 0.76159793 (N = 50) and 0.76159417 (N = 50) using control vector parameterization based invasive weed optimization (IWO-CVP) and control vector parameterization based adaptive invasive weed optimization (ADIWO-CVP).…”

Dynamic Optimization of Autocatalytic Esterification in a Semi‐batch Reactor

“…The discretization techniques are characterized by different components. The components involved in the transformation for CVP and OC are the control variables and both the state and control variable, respectively 13. Meanwhile, in the MS strategy, the state trajectories and the control vector are discretized.…”