.png){kind=icon}
.png){kind=icon}
Return
Piranha predation optimization algorithm (PPOA) for global optimization and engineering design problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2024.112085.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A new nature-inspired optimization algorithm, Piranha predation optimization algorithm (PPOA), is proposed based on the unique foraging and predation behaviors of piranhas. Briefly, PPOA consists of three optimization operations, i.e., narrowing down to tear prey, swimming in a straight line, and swimming in a spiral. In this paper, various mathematical models for simulating the behavioral operators are presented in detail to solve different optimization challenges effectively. In this paper, the performance of PPOA is rigorously tested on 23 benchmark optimization functions, CEC2017 competition test set, CEC2020 real-world engineering optimization problems and four engineering design applications to show the applicability of the algorithm in different applications. Comparison experiments with other good and advanced competitive algorithms are conducted to reveal the advantages and performance of PPOA by using performance metrics such as Wilcoxon rank sum test and Friedman mean rank. The comparative results of this paper demonstrate the effectiveness of the proposed algorithmic strategy and its potential in applying it to solving optimization real-world engineering optimization problems.
Keywords:
Piranha predation optimization algorithm
Meta-heuristic algorithm
Global optimization
Piranha predation
Engineering applications