Multi-Objective Firefly Algorithm (MOFA) [18], Multi-Objective Atomic Orbital Search (MOAOS) [19], Artificial Vultures Optimization Algorithm (MOAVOA) [20], Multi-Objective Bonobo Optimizer (MOBO) [21], Multi-Objective Stochastic Paint Optimizer (MOSPO) [22], Multi-Objective Moth...
Multi-objective optimization Heat transfer search Design optimization Pareto front 1. Introduction The objective of this paper is to solve optimization problems which have more than one objective functions. These optimization problems are recognized as multi-objective optimization problems (MOOPs). Solving ...
Azizi M, Talatahari S, Khodadadi N, Sareh P (2022) Multiobjective atomic orbital search (MOAOS) for global and engineering design optimization. IEEE Access 10:67727–67746 Article MATH Google Scholar Khodadadi N, Abualigah L, Mirjalili S (2022) Multi-objective stochastic paint optimizer (MO...
We showed that the use of the restriction principle for the interatomic density (following from the Paulie's principle) allows us to describe correctly angular dependences of the interatomic bonding in polyatomic systems in the framework of the orbital-free version of the density functional theory. ...
The ever-increasing capability of computational methods has resulted in their general acceptance as a key part of the materials design process. Traditionally this has been achieved using a so-called computational funnel, where increasingly accurate - and
[25], Young’s Double-Slit Experiment Optimizer (YDSE) [27], physical experiments from double-slit interference have shown the wave of light, Atomic Orbit Search (AOS) [28] Inspired by concepts such as quantum mechanics and quantum atom models in physics, Atom Search Optimization (ASO) [29...
sampling in-progress molecules and minimizing a multi-objective loss function. The loss function contains both the difference between the predicted value score and the final rollout reward and the difference between predicted prior probabilities and the actual search probabilities for each of the molecule...
Efficient multi-objective simulation-driven antenna design using co-kriging IEEE Trans. Antenn. Propag., 62 (2014), p. 5900 View in ScopusGoogle Scholar [35] T. Xu, A.J. Valocchi, M. Ye, F. Liang Quantifying model structural error: efficient bayesian calibration of a regional groundwater...
Combining the three strategies based on quasi-opposition-based learning, adaptive and spiral predation strategy, and Nelder–Mead simplex search method with BWO, an enhanced belugas optimization, which is marked as HBWO, is proposed. For HBWO, the three strategies introduced are important ways to ...
This paper introduces a multi-objective variant of the Greylag Goose Optimizer (MOGGO) to tackle complex structural optimization problems. Inspired by the