A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends Xiong, Ning Molina Cabrera, Daniel León Ortiz, Miguel Herrera Triguero, Francisco Metaheuristics Optimization methods Trajectory-based optimization Population-based optimization Multimodal optimization Multi-objective optimization Parallel metaheuristics The work is within the EMOPAC project (project no 16317) granted by the Swedish Knowledge Foundation. We are also grateful to ABB FACTS, Prevas, and VG Power for co-financing the research. This work was supported in part by the Spanish Ministry of Education and Science under Grant TIN2011-28488 and TIN 2012-37930-C02-01 and the Andalusian Government under Grant P10-TIC-6858. 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. 2020-12-21T13:34:14Z 2020-12-21T13:34:14Z 2015-08-01 info:eu-repo/semantics/article Xiong, N., Molina, D., Ortiz, M. L., & Herrera, F. (2015). A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends. International Journal of Computational Intelligence Systems, 8(4), 606-636. [https://doi.org/10.1080/18756891.2015.1046324] http://hdl.handle.net/10481/65077 10.1080/18756891.2015.1046324 eng http://creativecommons.org/licenses/by-nc/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial 3.0 España Atlantis Press; Taylor & Francis