Multi-objective optimization and multi-criteria decision-making methods for optimal design of standalone photovoltaic system: A comprehensive review

HM Ridha, C Gomes, H Hizam, M Ahmadipour… - … and Sustainable Energy …, 2021 - Elsevier
Standalone photovoltaic (SAPV) systems have been considered as promising and fast
development renewable energy sources due to free-noise, easy availability, and low-cost …

Expected improvement for expensive optimization: a review

D Zhan, H Xing - Journal of Global Optimization, 2020 - Springer
The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …

A survey on evolutionary computation for complex continuous optimization

ZH Zhan, L Shi, KC Tan, J Zhang - Artificial Intelligence Review, 2022 - Springer
Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …

A benchmark-suite of real-world constrained multi-objective optimization problems and some baseline results

A Kumar, G Wu, MZ Ali, Q Luo, R Mallipeddi… - Swarm and Evolutionary …, 2021 - Elsevier
Abstract Generally, Synthetic Benchmark Problems (SBPs) are utilized to assess the
performance of metaheuristics. However, these SBPs may include various unrealistic …

Secure and energy efficient-based E-health care framework for green internet of things

M Kaur, D Singh, V Kumar, BB Gupta… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
This paper proposes a secure and energy-efficient Internet of Things (IoT) model for e-
health. The main objective is to secure the transmission and retrieval of biomedical images …

PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum]

Y Tian, R Cheng, X Zhang, Y Jin - IEEE Computational …, 2017 - ieeexplore.ieee.org
Over the last three decades, a large number of evolutionary algorithms have been
developed for solving multi-objective optimization problems. However, there lacks an upto …

Learning to optimize: reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system

L Ma, N Li, Y Guo, X Wang, S Yang… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
The performance of decomposition-based algorithms is sensitive to the Pareto front shapes
since their reference vectors preset in advance are not always adaptable to various problem …

IGD indicator-based evolutionary algorithm for many-objective optimization problems

Y Sun, GG Yen, Z Yi - IEEE Transactions on Evolutionary …, 2018 - ieeexplore.ieee.org
Inverted generational distance (IGD) has been widely considered as a reliable performance
indicator to concurrently quantify the convergence and diversity of multiobjective and many …

A survey of multiobjective evolutionary algorithms based on decomposition

A Trivedi, D Srinivasan, K Sanyal… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Decomposition is a well-known strategy in traditional multiobjective optimization. However,
the decomposition strategy was not widely employed in evolutionary multiobjective …

An easy-to-use real-world multi-objective optimization problem suite

R Tanabe, H Ishibuchi - Applied Soft Computing, 2020 - Elsevier
Although synthetic test problems are widely used for the performance assessment of
evolutionary multi-objective optimization algorithms, they are likely to include unrealistic …