Dynamic multiobjective optimization problems: test cases, approximations, and applications. Farina M,Deb K,Amato P. IEEE Transactions on Evolutionary Computation . 2004M. Farina, K. Deb, and P. Amato: Dynamic multiobjective optimization problems: Test cases, approximation, and applications. In: ...
Dynamic multiobjective optimization problems (DMOPs) require the evolutionary algorithms that can track the moving Pareto-optimal fronts efficiently. This
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...
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...
Application of Particle Swarm to Multiobjective Optimization Evolutionary algorithms (EAs), search procedures based on natural selection (Back 1996), have been used to solve a wide variety of single and multiple objective optimization problems (Goldberg 1989). Particle Swarm Optimization (PSO) (Ke.....
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 ...
multiobjective (MO) problems. There have been significant contributions made to the field of evolutionary multiobjective optimization (EMOO) in the past two decades, as a result of intense research examining topics such as fitness assignment ...
Dynamic multi-objective optimization problems (DMOPs), in which the environments change over time, have attracted many researchers' attention in recent years. Since the Pareto set (PS) or the Pareto front (PF) can change over time, how to track the movement of the PS or PF is a challengin...
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...
In this article, a bi-level optimization problem covering upper (design) and lower (operation) levels is defined and a solution procedure for bi-level optimization problems is presented. This is devised as a dynamic multiobjective optimization problem, i.e. the values of the control and state ...