Review of the research landscape of multi-criteria evaluation and benchmarking processes for many-objective optimization methods: coherent taxonomy, challenges …
RT Mohammed, R Yaakob, AA Zaidan… - … Journal of Information …, 2020 - World Scientific
Evaluation and benchmarking of many-objective optimization (MaOO) methods are
complicated. The rapid development of new optimization algorithms for solving problems …
complicated. The rapid development of new optimization algorithms for solving problems …
An adaptive multi-objective algorithm based on decomposition and large neighborhood search for a green machine scheduling problem
Green machine scheduling consists in the allocation of jobs in order to maximize production,
in view of the sustainable use of energy. This work addresses the unrelated parallel …
in view of the sustainable use of energy. This work addresses the unrelated parallel …
Preference-guided evolutionary algorithms for many-objective optimization
F Goulart, F Campelo - Information Sciences, 2016 - Elsevier
This paper presents a technique that incorporates preference information within the
framework of multi-objective evolutionary algorithms for the solution of many-objective …
framework of multi-objective evolutionary algorithms for the solution of many-objective …
A mathematical formulation and an NSGA-II algorithm for minimizing the makespan and energy cost under time-of-use electricity price in an unrelated parallel …
In many countries, there is an energy pricing policy that varies according to the time-of-use.
In this context, it is financially advantageous for the industries to plan their production …
In this context, it is financially advantageous for the industries to plan their production …
Online clustering reduction based on parametric and non-parametric correlation for a many-objective vehicle routing problem with demand responsive transport
In this paper, we address an online dimensionality reduction approach to deal with a many-
objective formulation of a Vehicle Routing Problem with a Demand Responsive Transport …
objective formulation of a Vehicle Routing Problem with a Demand Responsive Transport …
[PDF][PDF] List of references on evolutionary multiobjective optimization
CAC Coello - URL< http://www. lania. mx/~ ccoello/EMOO …, 2010 - delta.cs.cinvestav.mx
List of References on Evolutionary Multiobjective Optimization Page 1 List of References on
Evolutionary Multiobjective Optimization Carlos A. Coello Coello ccoello@cs.cinvestav.mx …
Evolutionary Multiobjective Optimization Carlos A. Coello Coello ccoello@cs.cinvestav.mx …
Evolutionary method for weight vector generation in multi-objective evolutionary algorithms based on decomposition and aggregation
The generation of weight vectors is the primary step in MOEA based on decomposition and
aggregation methods, affecting the diversity of the Pareto approximation and overall …
aggregation methods, affecting the diversity of the Pareto approximation and overall …
Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm
Y Li, M Mohammadi, X Zhang, Y Lan… - arxiv preprint arxiv …, 2024 - arxiv.org
Mixed service mode docks enhance efficiency by flexibly handling both loading and
unloading trucks in warehouses. However, existing research often predetermines the …
unloading trucks in warehouses. However, existing research often predetermines the …
Plane elliptic or toroidal multipole expansions for static fields: Applications within the gap of straight and curved accelerator magnets
P Schnizer, B Schnizer, P Akishin… - … -The international journal …, 2009 - emerald.com
Purpose–The purpose of this paper is to present new basis functions suitable to
parameterize two‐dimensional static potentials or (magnetic) fields and to show their …
parameterize two‐dimensional static potentials or (magnetic) fields and to show their …
A differential mutation operator for the archive population of multi-objective evolutionary algorithms
The Differential Evolution (DE) algorithm is a simple and efficient evolutionary algorithm that
has been applied to solve many optimization problems mainly in continuous search …
has been applied to solve many optimization problems mainly in continuous search …