Purpose

This paper aims to present a new physically inspired meta-heuristic algorithm, which is called Plasma Generation Optimization (PGO). To evaluate the performance and capability of the proposed method in comparison to other optimization methods, two sets of test problems consisting of 13 constrained benchmark functions and 6 benchmark trusses are investigated numerically. The results indicate that the performance of the proposed method is competitive with other considered state-of-the-art optimization methods.

Design/methodology/approach

In this paper, a new physically-based metaheuristic algorithm called plasma generation optimization (PGO) algorithm is developed for solving constrained optimization problems. PGO is a population-based optimizer inspired by the process of plasma generation. In the proposed algorithm, each agent is considered as an electron. Movement of electrons and changing their energy levels are based on simulating excitation, de-excitation and ionization processes occurring through the plasma generation. In the proposed PGO, the global optimum is obtained when plasma is generated with the highest degree of ionization.

Findings

A new physically-based metaheuristic algorithm called the PGO algorithm is developed that is inspired from the process of plasma generation.

Originality/value

The results indicate that the performance of the proposed method is competitive with other state-of-the-art methods.

中文翻译：

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等离子体生成优化：一种新的基于物理的元启发式算法，用于解决约束优化问题

目的

本文旨在提出一种新的受物理启发的元启发式算法，称为等离子生成优化 (PGO)。为了评估所提出的方法与其他优化方法相比的性能和能力，对由 13 个受约束的基准函数和 6 个基准桁架组成的两组测试问题进行了数值研究。结果表明，所提出的方法的性能与其他考虑的最先进的优化方法相比具有竞争力。

设计/方法/方法

在本文中，开发了一种新的基于物理的元启发式算法，称为等离子体生成优化 (PGO) 算法，用于解决约束优化问题。PGO 是一种受等离子体生成过程启发的基于群体的优化器。在所提出的算法中，每个代理都被视为一个电子。电子的运动和改变它们的能级是基于模拟通过等离子体产生发生的激发、去激发和电离过程。在所提出的 PGO 中，当以最高电离度产生等离子体时，将获得全局最优解。

发现

受等离子体生成过程的启发，开发了一种新的基于物理的元启发式算法，称为 PGO 算法。

原创性/价值

结果表明，所提出方法的性能与其他最先进的方法相比具有竞争力。