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A cost-effective algorithm for the solution of engineering problems with particle swarm optimization

机译：用粒子群算法解决工程问题的经济高效算法

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摘要

A hybrid particle swarm optimization algorithm is proposed for the cost-effective solution of single objective constrained engineering problems. The algorithm implements original strategies aimed to reduce computational effort of optimizations when dealing with real-world problems. Taking inspiration from evolutionary algorithms, a selection mechanism among particles is proposed allowing significant reductions in the solution cost. To enlarge the exploration space, a multi-start approach is frequently adopted, randomly reinitializing the swarm; the injection of optimized particles-obtained in previous runs-into the successive randomly generated starting swarms has been investigated as an expedient to accelerate convergence to the optimal solution. In order to avoid the algorithm to remain trapped into local minima, an innovative scheme has been proposed to update the inertia factor multiplying the previous velocity of the swarm. The proposed algorithm has been validated using standard engineering and purely mathematical problems commonly recognized as valid benchmark functions in specialized literature.
机译：针对单目标约束工程问题的高性价比解决方案，提出了一种混合粒子群算法。该算法实现了原始策略，旨在减少处理实际问题时的优化计算量。借鉴进化算法的启发，提出了一种粒子间的选择机制，可以显着降低解决方案的成本。为了扩大勘探空间，经常采用多起点方法，随机重新初始化群体。已经研究了将先前运行中获得的优化粒子注入到连续随机生成的初始粒子群中的方法，以此作为加速收敛到最佳解的权宜之计。为了避免算法陷入局部极小值，提出了一种创新方案来更新惯性因子，使其乘以先前的群速度。所提出的算法已使用标准工程和纯数学问题进行了验证，这些问题通常在专业文献中被认为是有效的基准函数。

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