Grey modelDynamic multiobjective optimization problems (DMOPs) usually involve multiple conflicting objectives that change over time. A good evolutionary algorithm should be able to quickly track the moving Pareto optimal front (POF) and Pareto optimal set (POS) over time. To solve DMOPs, a ...
Multiobjective optimization problems occur in many situations and aspects of the engineering optimization field. In reality, many of the multiobjective optimization problems are dynamic in nature, i.e. their Pareto fronts change with the time or environment parameter; these optimization problems most ...
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 ...
Dynamic multiobjective optimization problems (DMOPs) require the evolutionary algorithms that can track the moving Pareto-optimal fronts efficiently. This
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 ...
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...
Multi-Objective Evolution Strategy for Dynamic Multi-objective Optimization This paper presents a novel evolution strategy based evolutionary algorithm, named DMOES, which can efficiently and effectively solve multi-objective optimization problems in dynamic environments. DMOES can track the new approximate ...
1.In this paper,noninferior nature of the solution of multiobjective dynamic programming is discussed.针对多目标动态规划问题,指出其一般只存在非劣解的性质,提出了多目标阶段收益非劣矩阵、多目标阶段收益非劣合成矩阵和多目标逆向递推矩阵等概念。 4)Dynamic multi-objective optimization动态多目标优化 ...
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...
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 ...