Gradual Innovative Transitional Solutions Improving Current to a Desired Target: Innovation Path

IF 11.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Evolutionary Computation Pub Date : 2025-03-18 DOI:10.1109/TEVC.2025.3552685
Ahmer Khan;Kalyanmoy Deb
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Abstract

In practice, there is often a need to update the currently implemented (CI) solution to achieve better performance goals catering to new demands or adoption of new technologies. However, the new optimal solution, found by reoptimizing the problem, may be quite different from the CI solution implicating large costs, major changes, and laborious efforts, causing an apathy for its adoption. For such scenarios, we propose a concept of an “innovation path” (IP), containing a sequence of transitional solutions from the existing to the new target solution with gradual and controlled change from one to the next. To discover such intermediate solutions of the IP, we propose a bi-objective formulation with dynamic step-constraints as an IP problem (IPP), such that a finite set of Pareto-optimal solutions of the resulting IPP become the desired intermediate IP solutions. Due to required gradual discovery of IP solutions, the IP-seeking task happens to be a nontrivial task. We demonstrate the working of the proposed approach on a number of single-, two-objective, and many-objective test and engineering problems. This article concludes with a number of extensions of this study, but the results of this study clearly indicate the usefulness of the proposed approach to other practical problems.
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渐进式创新过渡解决方案:创新路径
在实践中,经常需要更新当前实现的(CI)解决方案,以实现更好的性能目标,以满足新的需求或采用新技术。然而,通过重新优化问题找到的新的最优解决方案可能与CI解决方案有很大的不同,这意味着大的成本、重大的更改和费力的工作,从而导致对其采用的冷漠。对于这样的场景,我们提出了一个“创新路径”(IP)的概念,其中包含从现有到新的目标解决方案的一系列过渡解决方案,并且从一个到下一个的变化是渐进和可控的。为了发现IP的中间解,我们提出了一个带动态步进约束的双目标公式作为IP问题(IPP),使得得到的IPP的有限pareto最优解集成为期望的中间IP解。由于需要逐步发现IP解决方案,因此IP寻找任务恰好是一项不平凡的任务。我们在一些单目标、双目标和多目标的测试和工程问题上展示了所提出的方法的工作。本文总结了本研究的一些扩展,但本研究的结果清楚地表明了所提出的方法对其他实际问题的有用性。
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来源期刊
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
21.90
自引率
9.80%
发文量
196
审稿时长
3.6 months
期刊介绍: The IEEE Transactions on Evolutionary Computation is published by the IEEE Computational Intelligence Society on behalf of 13 societies: Circuits and Systems; Computer; Control Systems; Engineering in Medicine and Biology; Industrial Electronics; Industry Applications; Lasers and Electro-Optics; Oceanic Engineering; Power Engineering; Robotics and Automation; Signal Processing; Social Implications of Technology; and Systems, Man, and Cybernetics. The journal publishes original papers in evolutionary computation and related areas such as nature-inspired algorithms, population-based methods, optimization, and hybrid systems. It welcomes both purely theoretical papers and application papers that provide general insights into these areas of computation.
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