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Title: A new meta-heuristic optimizer: Pathfinder algorithm
Authors: Yapıcı, Hamza
Çetinkaya, Nurettin
Keywords: Optimization
Optimization Techniques
Multi-Objective Optimization
Pathfinder Algorithm
Particle Swarm Optimization
Self-Propelled Particles
Power Loss Minimization
Optimal Placement
Distributed Generation
Engineering Optimization
Shunt Capacitors
Optimal Location
Multiobjective Optimization
Differential Evolution
Issue Date: 2019
Publisher: ELSEVIER
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. (C) 2019 Elsevier B.V. All rights reserved.
ISSN: 1568-4946
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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