Abstract
The success of local search techniques in the solution of combinatorial optimization problems has motivated their incorporation into multi-objective evolutionary algorithms, giving rise to the so-called multi-objective memetic algorithms (MOMAs). The main advantage for adopting this sort of hybridization is to speed up convergence to the Pareto front. However, the use of MOMAs introduces new issues, such as how to select the solutions to which the local search will be applied and for how long to run the local search engine (the use of such a local search engine has an extra computational cost). Here, we propose a new MOMA which switches between a hypervolume-based global optimizer and an IGD+-based local search engine. Our proposed local search engine adopts a novel clustering technique based on the IGD+ indicator for splitting the objective space into sub-regions. Since both computing the hypervolume and applying a local search engine are very costly procedures, we propose a GPU-based parallelization of our algorithm. Our preliminary results indicate that our MOMA is able to converge faster than SMS-EMOA to the true Pareto front of multi-objective problems having different degrees of difficulty.
E. Manoatl Lopez—Author acknowledges support from CONACyT and CINVESTAV-IPN to pursue graduate studies in Computer Science.
C.A. Coello Coello—Author gratefully acknowledges support from CONACyT project no. 221551 and from a Cátedra Marcos Moshinsky 2014 in Mathematics.
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Notes
- 1.
SIMD (Single Instruction Multiple Data) is a computer architecture which can handle only one instruction but applies it to many data streams simultaneously [9].
- 2.
The GPU-based approach computes in a faster way the hypervolume contribution of a point.
- 3.
The GPU platform and API developed by Nvidia called CUDA [15] (Computer Unified Device Architecture), which is the one adopted in this work, is based on the CUDA-C language, which is an extension of C that allows the development of GPU routines called kernels. Each kernel defines instructions that are executed on the GPU by many threads at the same time.
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Manoatl Lopez, E., Coello Coello, C.A. (2016). A Parallel Multi-objective Memetic Algorithm Based on the IGD+ Indicator. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_44
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