Abstract
The Multiobjective Electromagnetism-like Mechanism (MOEM) is a relatively new technique for solving continuous multiobjective optimization problems. In this work, an enhanced MOEM algorithm (EMOEM) with a modified local search phase is presented. This algorithm derives from the modification of some key components of MOEM including a novel local search strategy, which are relevant for improving its performance. To assess the new EMOEM algorithm, a comparison with an original MOEM algorithm and other three multiobjective optimization state-of-the-art approaches, OMOPSO (a multiobjective particle swarm optimization algorithm), MOSADE (a multiobjective differential evolution algorithm) and NSGA-II (a multiobjective evolutionary algorithm), is presented. Our aim is to assess the ability of these algorithms to solve continuous problems including benchmark problems and an inventory control problem. Experiments show that EMOEM performs better in terms of convergence and diversity when compared with the original MOEM algorithm. EMOEM is also competitive in comparison with the other state-of-art algorithms.
Keywords
- Local Search
- Pareto Front
- Multiobjective Optimization
- Multiobjective Optimization Problem
- Local Search Procedure
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgements
This R&D work has been partially supported by the Portuguese Foundation for Science and Technology (FCT) under project grant UID/MULTI/00308/2013 and QREN Mais Centro Program Projects EMSURE (CEN- TRO 07 0224 FEDER 002004) and iCIS (CENTRO-07-ST24-FEDER-002003).
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Carrasqueira, P., Alves, M.J., Antunes, C.H. (2015). A Multiobjective Electromagnetism-Like Algorithm with Improved Local Search. In: Almeida, J., Oliveira, J., Pinto, A. (eds) Operational Research. CIM Series in Mathematical Sciences, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-20328-7_8
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DOI: https://doi.org/10.1007/978-3-319-20328-7_8
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