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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Adaptive Multiobjective Particle Swarm Optimization Based on Evolutionary State Estimation.

Bolin Wu, Wang Hu, Junjie Hu

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    This study introduces an adaptive multiobjective particle swarm optimization (MOPSO) algorithm. It enhances swarm intelligence by selecting leaders based on evolutionary states to improve convergence and diversity.

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    Area of Science:

    • Computational Intelligence
    • Swarm Intelligence
    • Optimization Algorithms

    Background:

    • Effective leader selection is crucial for managing convergence and diversity in swarm optimization.
    • Existing multiobjective particle swarm optimization (MOPSO) algorithms face challenges in adapting to different evolutionary states.
    • Balancing exploitation and exploration is a key challenge in multiobjective optimization problems.

    Purpose of the Study:

    • To propose a novel adaptive MOPSO algorithm that dynamically adjusts leader selection based on the evolutionary environment.
    • To enhance the performance of MOPSO by improving both convergence speed and diversity of solutions.
    • To introduce an evolutionary state estimation mechanism for adaptive leader selection.

    Main Methods:

    • Developed an adaptive MOPSO algorithm incorporating an evolutionary state estimation mechanism.
    • Implemented a leader selection strategy that chooses convergence global best (c-gBest) and diversity global best (d-gBest) solutions based on exploitation or exploration states.
    • Employed a modified archive maintenance strategy using reference points to maximize Pareto solution diversity.

    Main Results:

    • The proposed adaptive MOPSO algorithm demonstrated superior performance compared to state-of-the-art algorithms on 31 benchmark functions.
    • Significant improvements in both convergence and diversity of the obtained Pareto fronts were observed.
    • The adaptive leader selection strategy effectively managed the trade-off between convergence and diversity.

    Conclusions:

    • The novel adaptive MOPSO algorithm provides a robust approach for solving multiobjective optimization problems.
    • The evolutionary state estimation and adaptive leader selection mechanism are effective in enhancing swarm intelligence.
    • The proposed method offers a promising direction for future research in swarm optimization and evolutionary computation.