Results 41 to 50 of about 65,077 (195)

From a Pareto Front to Pareto Regions: A Novel Standpoint for Multiobjective Optimization

Mathematics, 2021
This work presents a novel approach for multiobjective optimization problems, extending the concept of a Pareto front to a new idea of the Pareto region.
Carine M. Rebello, Márcio A. F. Martins, Daniel D. Santana, Alírio E. Rodrigues, José M. Loureiro, Ana M. Ribeiro, Idelfonso B. R. Nogueira   +6 more
doaj   +1 more source

A benchmark test problem toolkit for multi-objective path optimization [PDF]

, 2019
Due to the complexity of multi-objective optimization problems (MOOPs) in general, it is crucial to test MOOP methods on some benchmark test problems.
Hu, Xiao-Bing, Leeson, Mark S., Li, Hang, Zhang, Chi, Zhang, Hai-Lin, Zhang, Ming-Kong   +5 more
core   +1 more source

Version [2.0] — [pyMCMA: Uniformly distributed Pareto-front representation]

SoftwareX
pyMCMA is the Python implementation of a novel method for autonomous computation of the Pareto-front representation composed of efficient solutions distributed uniformly in terms of the distances between neighbor Pareto solutions.
Marek Makowski, Janusz Granat, Andrii Shekhovtsov, Zbigniew Nahorski, Jinyang Zhao   +4 more
doaj   +1 more source

System optimization by multiobjective genetic algorithms and analysis of the coupling between variables, constraints and objectives [PDF]

, 2005
This paper presents a methodology based on Multiobjective Genetic Algorithms (MOGA’s) for the design of electrical engineering systems. MOGA’s allow to optimize multiple heterogeneous criteria in complex systems, but also simplify couplings and ...
Roboam, Xavier, Régnier, Jérémi, Sareni, Bruno   +2 more
core   +2 more sources

Pareto-optimal front generation for the bi-objective JIT scheduling problems with a piecewise linear trade-off between objectives

Operations Research Perspectives
This paper proposes a novel method of Pareto front generation from a set of piecewise linear trade-off curves typically encountered in bi-objective just-in-time (JIT) scheduling problems. We have considered the simultaneous minimization of total weighted
Sona Babu, B.S. Girish
doaj   +1 more source

Multi-Objective Feature Selection Method Based on Hybrid MI and PSO Algorithm

Jisuanji kexue yu tansuo, 2020
Feature selection is an important pre-processing in data mining, due to a lot of redundant and irrelevant features in datasets. A filter-wrapper multi-objective feature selection method based on hybrid mutual information and particle swarm optimization ...
WANG Jinjie, LI Wei
doaj   +1 more source

Generalized decomposition and cross entropy methods for many-objective optimization [PDF]

, 2012
Decomposition-based algorithms for multi-objective optimization problems have increased in popularity in the past decade. Although their convergence to the Pareto optimal front (PF) is in several instances superior to that of Pareto-based algorithms ...
Fleming, P.J., Giagkiozis, I., Purshouse, R.C.   +2 more
core  

Simple Problems: The Simplicial Gluing Structure of Pareto Sets and Pareto Fronts

, 2017
Quite a few studies on real-world applications of multi-objective optimization reported that their Pareto sets and Pareto fronts form a topological simplex.
Deb K., Hamada N.
core   +1 more source

Pareto or non-Pareto: Bi-criterion evolution in multi-objective optimization [PDF]

, 2015
It is known that Pareto dominance has its own weaknesses as the selection criterion in evolutionary multi-objective optimization. Algorithms based on Pareto dominance can suffer from slow convergence to the optimal front, inferior performance on problems
Li, M, Liu, X, Yang, S
core   +3 more sources

Learning and Selection of Pareto Optimal Policies Matching User Preferences

IEEE Access
Reinforcement learning, which is attracting attention as a method for optimizing sequential decision-making, primarily focuses on scenarios with a single objective.
Akinori Tamura, Sachiyo Arai
doaj   +1 more source