Current heating control systems relying on traditional PID control exhibit suboptimal performance, leading to poor heating effectiveness and significant energy wastage. This paper addresses these limitations by employing the Seagull Optimization Algorithm (SOA) to optimize PID controller parameters. To mitigate the inherent shortcomings of SOA, such as limited convergence accuracy and slow convergence speed, an Improved Seagull Optimization Algorithm (IGSOA) is proposed. The IGSOA incorporates a cosine function convergence factor during the seagull migration phase to sustain exploratory capabilities while accelerating convergence in later stages. In the predatory phase, a combination of a greedy strategy and a weighted-average updating mechanism is introduced to guide the population toward promising regions of the search space. Furthermore, a golden-section-based sine strategy is integrated to guide population position updates, thereby enhancing local search capabilities. To evaluate the proposed approach, a mathematical model of a heating control system is established. Experimental data obtained from the system is used to derive its transfer function. The IGSOA-PID controller is then implemented and simulated using Simulink in MATLAB. Experimental results demonstrate that the proposed controller exhibits a faster response, lower overshoot, and improved stability, ultimately enhancing the control effectiveness of the heating control system.
Key words
heating control system /
PID control /
improved seagull optimization algorithm /
Matlab
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