Many-objective optimizationMulti-objective optimization2022 Elsevier B.V.For evolutionary computation, how to balance the convergence and diversity of populations is a challenging problem for solving many-objec
Inspired by these two observations, BiGE converts a given multi-objective optimization problem into a bi-goal (objective) optimization problem regarding proximity and diversity, and then handles it using the Pareto dominance relation in this bi-goal domain. Implemented with estimation methods of ...
many‐objective optimization problemThe problem of convergence and diversity in the course of population evolution is difficult to be balanced for solving the many﹐bjective optimization problem (MaOP). To track with the problem, a many﹐bjective optimization algorithm is designed. In the algorithm,...
In this paper, the MRTA problem is built as a many-objective optimization model with four objectives, which takes the load capacity of single robot, single picking station, all robots and all picking stations into account. To solve the model, a hybrid many-objective competitive swarm ...
A many-objective evolutionary algorithm based on three states for solving many-objective optimization problem Article Open access 19 August 2024 A new optimization algorithm to solve multi-objective problems Article Open access 13 October 2021 External archive guided radial-grid multi objective diffe...
A key question when tackling such a set problem is how to define the optimization criterion. Many multiobjective evo- lutionary algorithms (MOEAs) implement a combination of Pareto dominance on sets and a diversity measure based on Euclidean distance in the objective space, e.g., NSGA-II ...
In this paper, an evolutionary algorithm based on bi-goal is proposed for many-objective optimization. We first provide a new proximity estimation, ensuring the convergence of algorithm. Afterwards, a new sharing function with a novel discriminator is employed to improve the diversity. The dominance...
Finding such as subspace involves solving a three‐objective optimization problem. A subspace should cover many of the decision relevant model runs, while containing as few as possible nondecision relevant model runs, and being easy to interpret. Existing techniques for scenario discovery, however, ...
Liu. Bi-goal evolution for many-objective opti- mization problems. Artificial Intelligence, 228:45-65, 2015.Li M Q, Yang S X, Liu X H. Bi-goal evolution for many-objective optimization problems. Artificial Intelli- gence, 2015, 228: 45-65...
MaOP Many-Objective Optimization Problem MOEA Multi-objective Evolutionary Algorithm MOP Multi-objective Optimization Problem RoI Region of Interest RVEA-iGNG Reference-Vector-Guided Evolutionary Algorithm with Improved Growing Neural Gas TS-NSGA-II Two-Stage Non-dominated CRediT authorship contribution stat...