The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
A new meta-heuristic optimizer: Pathfinder algorithm
Abstract
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.
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Cites
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The Whale Optimization Algorithm
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Cited by
Rajwar, K., Deep, K., & Das, S. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10470-y · Cited by 687
Kaveh, M., & Mesgari, M. S. (2023) · Neural Processing Letters · doi:10.1007/s11063-022-11055-6 · Cited by 292
Metadata
- Authors
- Yapici, H., & Cetinkaya, N.
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 78
- Pages
- 545-568
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W2921893399
- Cited by
- 339
- References
- 82
- FWCI
- 19.57
- Type
- journal article
- Language
- en
- Open access
- closed
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