Improving Performance via Population Growth and Local Search: The Case of the Artificial Bee Colony Algorithm
نویسندگان
چکیده
We modify an artificial bee colony algorithm to (i) make the population size grow over time, and to (ii) apply local search on strategically selected solutions. The modified algorithm obtains very good results on a set of large-scale continuous optimization problems. This is not the first time we see that these algorithmic components make an initially non-competitive algorithm produce state-of-the-art results. In previous work, we have shown that the same modifications substantially improve the performance of particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms. Altogether, these results suggest that population growth and local search are key components for obtaining high-quality results.
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