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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Parallel Computing

    Background:

    • Evolutionary computation (EC) algorithms are population-based and iterative, making them suitable for parallelization.
    • Existing parallel EC techniques focus on population-level or individual-level parallelism.
    • Generation-level parallelism in EC algorithms remains largely unexplored.

    Purpose of the Study:

    • To propose and investigate a novel paradigm for parallel EC algorithms using generation-level parallelism.
    • To implement a pipeline-based parallel Particle Swarm Optimization (PSO) algorithm, termed P³SO.
    • To evaluate the effectiveness of generation-level parallelism in accelerating EC algorithms.

    Main Methods:

    • Inspired by industrial pipeline techniques, a new generation-level parallelism approach was developed.
    • The local version of Particle Swarm Optimization (PSO) was adapted to implement the pipeline-based parallel PSO (P³SO).
    • P³SO allows different particles to execute evolutionary operations in subsequent generations simultaneously.

    Main Results:

    • Experimental results demonstrate substantial acceleration in evolutionary speed.
    • The problem-solving ability of the P³SO algorithm was not adversely affected.
    • Significant speedup was achieved without compromising the algorithm's performance.

    Conclusions:

    • Generation-level parallelism is a feasible and effective strategy for EC algorithms.
    • The proposed P³SO algorithm offers significant potential for accelerating time-consuming optimization problems.
    • This research opens new avenues for parallelizing EC algorithms at the generation level.