A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends
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Atlantis Press; Taylor & Francis
MetaheuristicsOptimization methodsTrajectory-based optimizationPopulation-based optimizationMultimodal optimizationMulti-objective optimizationParallel metaheuristics
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]
SponsorshipSwedish Knowledge Foundation 16317; ABB FACTS; Prevas; VG Power; Spanish Government TIN2011-28488 TIN 2012-37930-C02-01; Andalusian Government 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.
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