Multi-objective optimization methods and application in energy saving
Y Cui, Z Geng, Q Zhu, Y Han - Energy, 2017 - Elsevier
Multi-objective optimization problems are difficult to solve in that the optimized objectives are
usually conflicting with each other. It is usually hard to find an optimal solution that satisfies …
usually conflicting with each other. It is usually hard to find an optimal solution that satisfies …
Many-objective evolutionary algorithms: A survey
Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world
applications. However, most MOEAs based on Pareto-dominance handle many-objective …
applications. However, most MOEAs based on Pareto-dominance handle many-objective …
Unexpected improvements to expected improvement for bayesian optimization
Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian
optimization and has found countless successful applications, but its performance is often …
optimization and has found countless successful applications, but its performance is often …
Bio-inspired computation: Where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …
with the mimicking of behavioral patterns and social phenomena observed in nature towards …
A multicloud-model-based many-objective intelligent algorithm for efficient task scheduling in internet of things
X Cai, S Geng, D Wu, J Cai… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
Internet of Things (IoT) is a huge network and establishes ubiquitous connections between
smart devices and objects. The flourishing of IoT leads to an unprecedented data explosion …
smart devices and objects. The flourishing of IoT leads to an unprecedented data explosion …
Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement
Optimizing multiple competing black-box objectives is a challenging problem in many fields,
including science, engineering, and machine learning. Multi-objective Bayesian optimization …
including science, engineering, and machine learning. Multi-objective Bayesian optimization …
Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization
In many real-world scenarios, decision makers seek to efficiently optimize multiple
competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization …
competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization …
Grasshopper optimization algorithm for multi-objective optimization problems
This work proposes a new multi-objective algorithm inspired from the navigation of grass
hopper swarms in nature. A mathematical model is first employed to model the interaction of …
hopper swarms in nature. A mathematical model is first employed to model the interaction of …
A new arithmetic optimization algorithm for solving real-world multiobjective CEC-2021 constrained optimization problems: diversity analysis and validations
In this paper, a new Multi-Objective Arithmetic Optimization Algorithm (MOAOA) is proposed
for solving Real-World constrained Multi-objective Optimization Problems (RWMOPs). Such …
for solving Real-World constrained Multi-objective Optimization Problems (RWMOPs). Such …
A survey of multiobjective evolutionary algorithms based on decomposition
Decomposition is a well-known strategy in traditional multiobjective optimization. However,
the decomposition strategy was not widely employed in evolutionary multiobjective …
the decomposition strategy was not widely employed in evolutionary multiobjective …