ABSTRACT

PSO is a remarkable tool for solving several optimisation problems, like global optimisation and many real-life problems. It generally explores global optimal solution via exploiting the particle – swarm’s memory. Its limited properties on objective function’s continuity along with the search space and its potentiality in adapting dynamic environment make the PSO an important meta-heuristic method. PSO has an inherent tendency of trapping at local optimum which affects the convergence prematurely, when trying to solve difficult problems. This work proposed a modified version of PSO called as FPSO, where frequency-wave-sound is employed to exit from any encountered local optimum; if it is not the optimal solution. This FPSO mimics the characteristics of the waves by using three parameters, namely amplitude, frequency and wavelength. FPSO is then compared and analysed with other renowned algorithms like conventional PSO, Grey Wolf Optimisation (GOW), Multi-Verse Optimiser (MVO), Moth-Flame Optimisation (SL-PSO), Sine Cosine Algorithm (PPSO) and Butterfly Optimisation Algorithm (BOA) on 23 bench marking test bed functions. The performance is evaluated using various measures including trajectory, search history, average fitness solution and best optimisation-solution. The obtained results show that the FPSO algorithm beats other metaheuristic algorithms and confirmed its better performance on 2-dimensional test functions.

摘要

PSO 是解决多个优化问题的出色工具，例如全局优化和许多现实生活中的问题。它通常通过利用粒子群的记忆来探索全局最优解。粒子群算法在目标函数与搜索空间的连续性方面的有限性质及其适应动态环境的潜力使粒子群算法成为一种重要的元启发式方法。PSO 在尝试解决困难问题时，具有陷入局部最优的固有趋势，这会过早地影响收敛。这项工作提出了一种改进的 PSO 版本，称为 FPSO，其中频率-波-声音用于退出任何遇到的局部最优；如果不是最优解。该FPSO通过使用振幅、频率三个参数来模拟海浪的特性和波长。然后将 FPSO 与其他著名算法进行比较和分析，例如传统 PSO、灰狼优化 (GOW)、多节优化器 (MVO)、蛾火焰优化 (SL-PSO)、正弦余弦算法 (PPSO) 和蝴蝶优化算法 ( BOA) 上 23 个基准测试台功能。使用各种度量来评估性能，包括轨迹、搜索历史、平均适应度解决方案和最佳优化解决方案。所得结果表明，FPSO 算法优于其他元启发式算法，并证实了其在二维测试函数上的更好性能。