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 dynamic nature of their objective functions, constraint functions, and problem parameters which often change over time. In fact, dealing with ...
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 ...
based on the optimization requirements of that particular time instant, enabling the coevolutionary algorithm to handle both the static and dynamic multiobjective problems. The effective- ness of the competitive-cooperation coevolutionary algorithm (COEA) in static environments is validated against various...
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...
Dynamic multiobjective optimization problems (DMOPs) require the evolutionary algorithms that can track the moving Pareto-optimal fronts efficiently. This
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。 3. A new method solving a special class of dynamic multi-objective optimization problem(DDMOP) is given. 给出了一类定义在离散时间(环境)空间上、自变量的维数随环境可发生改变的一类动...
Sendhoff, "Constructing dynamic optimization test prob- lems using the multi-objective optimization concept," in Applications of Evolutionary Computing. Springer, 2004, pp. 525-536.Y. Jin, "Constructing dynamic optimization test problems using the multi- objective optimization concept," in Applications...