# Advancements in Semi-Active Automotive Suspension Systems with Magnetroheological Dampers: A Review
## 4 Semi-Active Control of Suspension Systems
### Intelligent Control Strategies
#### 4.3.3 Bio-Inspired Optimization Algorithms  
In recent years, bio-inspired optimization algorithms rooted in simulated biological systems, such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Ant Colony Optimization (ACO), have undergone significant advancements and found successful applications across various domains of control.  
In 2017, [PERSON] et al. implemented an Adaptive Neural Network (ANN) control strategy in a Continuous Damping Control (CDC) suspension system and utilized experimental data from CDC dampers to establish a boundary model for analyzing its dynamic characteristics [20]. Addressing uncertainties and nonlinearities in suspension system parameters, with the optimization objective focused on spring displacement, they employed Particle Swarm Optimization (PSO) to tune and optimize controller parameters. In 2017, [PERSON] and [PERSON] proposed an Intelligent Fuzzy Logic (IFL) control strategy utilizing a quarter-vehicle model [98]. They aimed to minimize the Mean Square Error (MSE) of body displacement, acceleration, and suspension deflection angle. They utilized the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) to optimize the weighting factors of the Intelligent Fuzzy Logic (IFL). In 2019, [PERSON] et al. employed the Particle Swarm Optimization (PSO) algorithm to optimize the Linear Quadratic Regulator (LQR) and Sliding Mode Control (SMC) strategies [99]. They employed weighted parameters and integral absolute error as the respective objective functions. In 2020, [PERSON] et al. employed various global optimization algorithms, including Differential Evolution (DE), Controlled Random Search (CRS), Modified Controlled Random Search (MCRS), Particle Swarm Optimization (PSO), and Modified Particle Swarm Optimization (MPSO), to achieve optimal trade-offs between indicators such as sprung mass displacement and tire grip performance in a nonlinear electro-hydraulic active suspension system [100]. In 2021, [PERSON] et al. tackled the limitations of traditional PID control strategies for air suspension, such as extended tuning durations and fixed control parameters [101]. They introduced a lateral interconnected electronic control strategy for air suspension, leveraging the Seeker Optimization Algorithm (SOA). In 2022, [PERSON] et al. proposed a Linear Quadratic Regulator (LQR) strategy to enhance the real-time adjustment control performance of Active Suspension Systems [2]. Additionally, they employed a novel Ant Colony Optimization (ACO) algorithm to conduct multi-objective optimization of the weights in LQR.  
Despite the achievements of intelligent control strategies, several issues persist. For instance, fuzzy rules exhibit considerable variability among individuals, posing challenges in their generalization and evaluation. The foundational principles of neural network control lack clarity and struggle to achieve effective control in data-scarc scenarios. Additionally, genetic algorithms are prone to getting trapped in local minima. As a result, the research focus on intelligent control is progressively pivoting towards composite control, which integrates modern control with intelligent control.