Genetic Algorithm and Particle Swarm Optimization: Analysis and Remedial Suggestions

Pandey, Hari Mohan

doi:10.1007/978-981-10-3226-4_44

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Furthermore, the FOPID controller has been designed to control the concentration by tuning the controller parameters, that is, proportional‐integral‐derivative ( K p , K i , and K d ) gains and fractional powers ( υ and ψ ), through WCA. Moreover, the strength of WCA is endorsed by comparing its performance with other optimization approaches including GA, 37 SA, 38 PSO, 37 and KH 39 for tuning the FOPID controller. An analytical comparison of these optimization approaches in terms of the optimal FOPID controller parameters and number of function evaluations has been demonstrated in Table 3.…”

Section: Resultsmentioning

confidence: 99%

Water cycle algorithm tuned robust fractional‐order Proportional–Integral–Derivative controller for energy optimization and control of nonlinear Multiple Stage Evaporator: A case study of paper mill

Verma

Yadav

Pati

et al. 2021

Asia-Pacific J Chem Eng

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In paper industries, Multiple Stage Evaporator (MSE) is used to concentrate black liquor. Due to the energy intensive nature of MSE, the global energy scenario stresses on optimizing its energy efficiency. Moreover, the necessity of a tight control of product quality demands superior understanding of system dynamics for controller design. This paper presents a robust fractional‐order Proportional–Integral–Derivative (FOPID) controller for concentration control of black liquor in Heptad's effect MSE. First, the steady‐state unknown process parameters are estimated by solving the nonlinear steady‐state model. Thereafter, these optimal process parameters are utilized to simulate the nonlinear dynamic model to obtain the transfer functions. By using these transfer functions, the FOPID controller is designed whose parameters are tuned via Water Cycle Algorithm (WCA). The competence of WCA toward controller tuning is validated by comparing with other optimization techniques (Genetic Algorithm [GA], Simulated Annealing [SA], Particle Swarm Optimization [PSO], and Krill Herd [KH]). Moreover, the performance of the proposed FOPID controller in concentration control, set‐point tracking, and noise suppression is validated by comparing with conventional PID, two‐degree of freedom‐PID (2‐DOF‐PID), and Internal Model Control (IMC) controllers. The results demonstrate that FOPID controller reduces the Integral Square Error (ISE) by 91.05%, 89.81%, and 78.84%, respectively, with respect to PID, 2‐DOF‐PID, and IMC. Also, the proposed FOPID controller improves the system transience with high robustness.

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