FindingsThe achieved numerical results empirically prove the superiority of the proposed method to state-of-the-art counterparts in the most test problems of a known artificial benchmark.Originality/valueThis paper provides a new interpretation and important insights into the many-objective optimization ...
This paper presents a meta-objective optimization approach, called Bi-Goal Evolution (BiGE), to deal with many-objective optimization problems. Inspired by two observations: 1) the conflict between proximity and diversity requirements is aggravated with the increase of the number of objectives and 2...
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 calledε-optimality for MOP that is based on a new conception, so ca...
When we solve many-objective optimization problems (MaOPs) by using multi-objective evolutionary algorithms (MOEAs), genetic diversity of solutions in the population significantly increases in order to explore the true Pareto optimal solutions widely distributed in variable space. In MOEAs, if solutions...
Liu, "Bi-goal evolution for many-objective optimization problems," Artificial Intelligence, vol. 228, no. Sup- plement C, pp. 45 - 65, 2015.M. Li, S. Yang, and X. Liu, "Bi-goal evolution for many-objective optimization problems," Artif. Intell., vol. 228, pp. 45-65, Nov. ...
Many-objective optimizationMOON2MOON2RPairwise comparisonAHPNeural networkA multi-objective optimization (MUOP) method that supports agile and flexible decision making to be able to handle complex and diverse decision environments has been in high demand. This study......
When solving many-objective optimization problems, evolutionary algorithms do not differentiate between solutions due to the large number of objectives inv... D Zouache,F Ben Abdelaziz - 《Annals of Operations Research》 被引量: 0发表: 0年 An exact -constraint method for bi-objective combinatorial...
problems, and the presented concepts may also be helpful for other types of quality indicators to be integrated in the optimization process. II. A BRIEF REVIEW OF HYPERVOLUME-RELATED RESEARCH The hypervolume indicator was originally proposed and ...
Recently it has been pointed out in many studies that evolutionary multi-objective optimization (EMO) algorithms with Pareto dominance-based fitness evaluation do not work well on many-objective problems with four or more objectives. In this paper, we examine the behavior of well-known and frequent...
Many-objective optimization problems(MaOPs) are the most challenging problems among multi-objective optimization problems (MOPs). Objective reduction method has become one of the most important technique for MaOPs which can alleviate the difficulties of selection pressure, computational cost and the human...