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Multiple-Population Jaya Algorithm for Continuous Function Optimization

In order to improve population diversity, multiple-population jaya algorithm (MPJA) is introduced. The population is divided into several sub-populations and each sub-population is evolved independently by using its own best solution and the worst solution. Local search is applied to intensify the local search ability of MPJA. Extensive experiments are conducted on 10 test functions. The experimental result show that MPJA has advantages on computational accuracy and global search ability.