Various real-world multiobjective optimization problems are dynamic, requiring evolutionary algorithms (EAs) to be able to rapidly track the moving Pareto front of an optimization problem once an environmental change occurs. To this end, several methods have been developed to predict the new location...
Dynamic Multiobjective Problems cover a set of real-world problems that have many conflicting objectives. These problems are challenging and well known by
The challenge of solving dynamic multi-objective optimization problems is to effectively and efficiently trace the varying Pareto optimal front and/or Pareto optimal set. To this end, this paper proposes a multi-direction search strategy, aimed at finding the dynamic Pareto optimal front and/or Pare...
Most of multi-objective optimization problems in the real-world are dynamic, so optimization algorithms are required to continuously track time-varying Pareto optimal set (POS) or Pareto optimal front (POF) rapidly with high accuracy. To meet this requirement, an improved variant based on particle...
Region partitioning is effective for solving dynamic multi-objective optimization problems (DMOPs). However, most region partitioning approaches use only specific individual information to predict directions within each region. Their efficiency degrades when the distribution of individuals is irregular, and ...
Section 2 describes the related work of the dynamic multi-objective optimization problems and fuzzy inference mechanism. In Section 3, a novel population prediction strategy based on fuzzy inference and one-step prediction (FIOPPS) is described in detail. In Section 4, a new MOTLBO/D is ...
A New Evolutionary Algorithm for Multi-objective Optimization Problems Among the currently successful Evolution- ary Multi-Objective Algorithms (MOEAs), elitism and no sharing factor are two com- mon characteristics and have b... W Zhi - Springer Berlin Heidelberg 被引量: 30发表: 2008年 A New ...
In the mul- tipopulation structure of DMOEF-MS, multiple populations handle different optimization problems, respectively. There- fore, the adopted multipopulation structure can improve the ability of DMOEF-MS to maintain population diversity and obtain well-distributed PFs, since the multiple ...
4) Dynamic multi-objective optimization 动态多目标优化 1. A dynamic multi-objective immune optimization algorithm suitable for dynamic multi-objective optimization problems is proposed based on the functions of adaptive learning,immune memory,antibody diversity and dynamic balance maintenance,etc. ...
基于生物免疫系统的自适应学习、免疫记忆、抗体多样性及动态平衡维持等功能,提出一种动态多目标免疫优化算法处理动态多目标优化问题。 2. A method for dynamic multi-objective optimization problems(DMOPs)is given. 给出了动态多目标优化问题的一种新解法。