A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends

DOI

10.1080/18756891.2015.1046324How to use a DOI?

Keywords

metaheuristics, optimization methods, trajectory-based optimization, population-based optimization, multimodal optimization, multi-objective optimization, parallel metaheuristics

Abstract

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.

Copyright

© 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/).

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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  - 10.1080/18756891.2015.1046324
ID  - Xiong2015
ER  -