Transfer Optimization, understood as the exchange of information among solvers to improve their performance, has gained a remarkable attention from the Swarm and Evolutionary Computation community in the last years. This research area is young but grows at a fast pace, being at the core of a corpus of literature that expands day after day. It is undeniable that the concepts underlying Transfer Optimization are formulated on solid grounds. However, evidences observed in recent contributions and our own experience in this field confirm that there are critical aspects that are not properly addressed to date. This short communication aims to engage the readership around a reflection on these issues, to provide rationale why they remain unsolved, and to call for an urgent action to overcome them fully. Specifically, we emphasize on three critical points of Evolutionary Multitasking Optimization, which is arguably the paradigm in Transfer Optimization that has been most actively investigated in the literature: i) the plausibility of the multitask optimization concept; ii) the acclaimed novelty of some proposed multitasking methods relying on Evolutionary Computation and Swarm Intelligence; and iii) methodologies used for evaluating newly proposed multitasking algorithms. Our ultimate purpose with this critique is to unveil weaknesses observed in these three problematic aspects, so that prospective works can avoid stumbling on the same stones and eventually achieve valuable advances in the right directions.
翻译:优化转让被理解为是解决问题者之间交流信息以改善其业绩,过去几年来,优化转让得到了Swarm和进化计算界的显著关注。这一研究领域虽然年轻,但增长速度很快,是日复一日扩展的一整套文献的核心。不可否认,优化转让所依据的概念是建立在坚实基础上的。然而,从最近的贡献和我们自身在这一领域的经验中观察到的证据证实,到目前为止有一些关键方面没有得到适当处理。这一简短的沟通旨在吸引读者围绕这些问题的思考进行思考,以提供它们仍未解决的理由,并呼吁采取紧急行动以完全克服这些问题。具体地说,我们强调“进化多功能优化”的三个关键点,这可以说是文献中最积极调查的“优化”模式:i)多任务优化概念的可信任性;ii)一些拟议的多任务优化方法被称作新颖的新颖性,这些方法仍然没有解决,并且呼吁采取紧急行动,以彻底克服这些问题。具体地说,我们采用的“进化多任务”模式最终可以避免“进化”的进化,在“进化”中最终评估“进化”的进化方法。