A new meta-heuristic optimizer: Pathfinder algorithm

By Hamza Yapici, Nurettin Cetinkaya. Applied Soft Computing, 2019

317 citations recorded by the source corpus.

Abstract This paper proposes a new meta-heuristic algorithm called Pathfinder Algorithm (PFA) to solve optimization problems with different structure. This method is inspired by collective movement of animal group and mimics the leadership hierarchy of swarms to find best food area or prey. The proposed method is tested on some optimization problems to show and confirm the performance on test beds. It can be observed on benchmark test functions that PFA is able to converge global optimum and avoid the local optima effectively. Also, PFA is designed for multi-objective problems (MOPFA). The results obtained show that it can approximate to true Pareto optimal solutions. The proposed PFA and MPFA are implemented to some design problems and a multi-objective engineering problem which is time consuming and computationally expensive. The results of final case study verify the superiority of the algorithms proposed in solving challenging real-world problems with unknown search spaces. Furthermore, the method provides very competitive solutions compared to well-known meta-heuristics in literature, such as particle swarm optimization, artificial bee colony, firefly and grey wolf optimizer.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/e37a1c6af6cdf71907ed62af90b02115d008f832

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Citations in the corpus

Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning (2024, unknown): “Another algorithm, the Pathﬁnder Algorithm (PFA) [21], has been proposed to address optimization problems with a different structure.”
Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm (2024, background): “Drawing inspiration from the collective movement observed in animal groups and the hierarchical leadership within swarms, PFA seeks to discover optimal solutions akin to identifying food areas or prey.”