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A Novel Particle Swarm Optimization Algorithm with Intelligent Weighting Mechanism

机译：具有智能加权机制的粒子群优化算法

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This paper presents a novel particle swarm optimization algorithm with an intelligent weighting mechanism, which is termed as weighted particle swarm optimization (WPSO) for short. The intelligent weighting mechanism is developed based on an effectiveness index to improve performance on a diverse set of problems and enhance the ability of local search infeasible region. Three search techniques, a non-uniform mutation operator, a differential mutation operator, and a local random search procedure are used to mutate the global best position and combined to get a further improved solution by using the weighted average. The performance of WPSO is tested on a set of well-known optimization benchmark functions and the optimization results are compared with four reported optimization methods in terms of solution quality and convergence speed. The experimental results demonstrate superior performance of the WPSO in solving optimization problems compared with other optimization methods.
机译：本文提出了一种具有智能加权机制的新型粒子群优化算法，简称为加权粒子群优化（WPSO）。基于有效性指标开发了智能加权机制，以提高在各种问题上的性能并增强本地搜索不可行区域的能力。三种搜索技术，非均匀变异算子，差分变异算子和局部随机搜索过程被用来变异全局最佳位置，并通过使用加权平均相结合以获得进一步改进的解决方案。在一组著名的优化基准功能上测试了WPSO的性能，并根据解决方案质量和收敛速度，将优化结果与四种报告的优化方法进行了比较。实验结果表明，与其他优化方法相比，WPSO在解决优化问题方面具有出色的性能。

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