A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends
- https://doi.org/10.1080/18756891.2015.1046324How to use a DOI?
- metaheuristics, optimization methods, trajectory-based optimization, population-based optimization, multimodal optimization, multi-objective optimization, parallel metaheuristics
Metaheuristics has attained increasing interest for solving complex real-world problems. This paper studies the principles and the state-of-the-art of metaheuristic methods for engineering optimization. Both the classic and emerging approaches to optimization using metaheuristics are reviewed and analyzed. All the methods are discussed in three basic types: trajectory-based, in which in each step a new solution is created from the previous one; multi-trajectory-based, in which a multi-start mechanism is used; and population-based, where multiple new solutions are created considering a population of approximate solutions. We further discuss algorithms and strategies to handle multi-modal and multi-objective optimization tasks as well as methods for parallel implementation of metaheuristic algorithms. Then, different software frameworks for metaheuristics are introduced. Finally, several interesting directions are pointed out as future research trends.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Ning Xiong AU - Daniel Molina AU - Miguel Leon Ortiz AU - Francisco Herrera PY - 2015 DA - 2015/08/01 TI - A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends JO - International Journal of Computational Intelligence Systems SP - 606 EP - 636 VL - 8 IS - 4 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2015.1046324 DO - https://doi.org/10.1080/18756891.2015.1046324 ID - Xiong2015 ER -