Abstract
This paper proposes a self-adaptive semi-asynchronous evolutionary algorithm, SA2EA for short, and verifies its effectiveness on multi-objective optimization problems. SA2EA is an extension of an asynchronous EA that continuously evolves solutions whenever one solution completes its evaluation in a parallel computation environment, unlike a conventional generation-based synchronous EA needs to wait for evaluations of all solutions in a population, which causes to waste much idle time of parallel computation nodes. In contrast to such asynchronous EA, SA2EA adequately controls its asynchrony, which means the number of waited solutions, depends on the variance of evaluation time of solutions. To investigate the effectiveness of the proposed SA2EA, this paper conducts the experiment on benchmark problems of multi-objective optimization where several variations of the variance of evaluation time are tested in pseudo-parallel computation environment. The experimental result reveals that the proposed SA2EA outperforms the synchronous and the asynchronous EA with constant asynchrony not depends on the variance of evaluation time of solutions.
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