Objective Reduction Using Objective Sampling and Affinity Propagation for Many-Objective Optimization Problems
نویسندگان
چکیده
منابع مشابه
Online Objective Reduction to Deal with Many-Objective Problems
In this paper, we propose and analyze two schemes to integrate an objective reduction technique into a multi-objective evolutionary algorithm (MOEA) in order to cope with many-objective problems. One scheme reduces periodically the number objectives during the search until the required objective subset size has been reached and, towards the end of the search, the original objective set is used ...
متن کاملUsing Different Many-Objective Techniques in Particle Swarm Optimization for Many Objective Problems: An Empirical Study
Pareto based Multi-Objective Evolutionary Algorithms face several problems when dealing with a large number of objectives. In this situation, almost all solutions become nondominated and there is no pressure towards the Pareto Front. The use of Particle Swarm Optimization algorithm (PSO) in multi-objective problems grew in recent years. The PSO has been found very efficient in solve Multi-Objec...
متن کاملA New Evolutionary Decision Theory for Many-Objective Optimization Problems
In this paper the authors point out that the Pareto Optimality is unfair, unreasonable and imperfect for Many-objective Optimization Problems (MOPs) underlying the hypothesis that all objectives have equal importance. The key contribution of this paper is the discovery of the new definition of optimality called ε-optimality for MOP that is based on a new conception, so called ε-dominance, which...
متن کاملAlternative Fitness Assignment Methods for Many-Objective Optimization Problems
Pareto dominance (PD) has been the most commonly adopted relation to compare solutions in the multiobjective optimization context. Multiobjective evolutionary algorithms (MOEAs) based on PD have been successfully used in order to optimize bi-objective and three-objective problems. However, it has been shown that Pareto dominance loses its effectiveness as the number of objectives increases and ...
متن کاملBi-goal evolution for many-objective optimization problems
This paper presents a meta-objective optimization approach, called Bi-Goal Evolution (BiGE), to deal with multi-objective optimization problems with many objectives. In multi-objective optimization, it is generally observed that 1) the conflict between proximity and diversity requirements is aggravated with the increase of the number of objectives and 2) the Pareto dominance loses its effective...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2914069