Dynamic Multiobjective Optimization Problems: Test Cases, Approximations, and Applications

2004 ◽  
Vol 8 (5) ◽  
pp. 425-442 ◽  
Author(s):  
M. Farina ◽  
K. Deb ◽  
P. Amato
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Test Cases ◽  
Multiobjective Optimization Problems ◽  
Dynamic Multiobjective Optimization

scholarly journals A Hybrid Predictive Strategy Carried through Simultaneously from Decision Space and Objective Space for Evolutionary Dynamic Multiobjective Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Peng Xu ◽  
Xiaoming Wu ◽  
Man Guo ◽  
Shuai Wang ◽  
Qingya Li ◽  
...  
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Ideal Point ◽  
Dynamic Environment ◽  
Decision Space ◽  
Center Point ◽  
Multiobjective Optimization Problems ◽  
Objective Space ◽  
Adaptive Diversity ◽  
Dynamic Multiobjective Optimization

There are many issues to consider when integrating 5G networks and the Internet of things to build a future smart city, such as how to schedule resources and how to reduce costs. This has a lot to do with dynamic multiobjective optimization. In order to deal with this kind of problem, it is necessary to design a good processing strategy. Evolutionary algorithm can handle this problem well. The prediction in the dynamic environment has been the very challenging work. In the previous literature, the location and distribution of PF or PS are mostly predicted by the center point. The center point generally refers to the center point of the population in the decision space. However, the center point of the decision space cannot meet the needs of various problems. In fact, there are many points with special meanings in objective space, such as ideal point and CTI. In this paper, a hybrid prediction strategy carried through from both decision space and objective space (DOPS) is proposed to handle all kinds of optimization problems. The prediction in decision space is based on the center point. And the prediction in objective space is based on CTI. In addition, for handling the problems with periodic changes, a kind of memory method is added. Finally, to compensate for the inaccuracy of the prediction in particularly complex problems, a self-adaptive diversity maintenance method is adopted. The proposed strategy was compared with other four state-of-the-art strategies on 13 classic dynamic multiobjective optimization problems (DMOPs). The experimental results show that DOPS is effective in dynamic multiobjective optimization.

scholarly journals Dynamic Multiobjective Optimization with Multiple Response Strategies Based on Linear Environment Detection

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-26
Author(s):  
Qiyuan Yu ◽  
Shen Zhong ◽  
Zun Liu ◽  
Qiuzhen Lin ◽  
Peizhi Huang
Keyword(s):  
Multiobjective Optimization ◽  
Prediction Models ◽  
Environmental Changes ◽  
Optimization Problems ◽  
Initial Population ◽  
Multiple Response ◽  
Response Strategies ◽  
Multiobjective Optimization Problems ◽  
Dynamic Multiobjective Optimization ◽  
Environment Detection

Dynamic multiobjective optimization problems (DMOPs) bring more challenges for multiobjective evolutionary algorithm (MOEA) due to its time-varying characteristic. To handle this kind of DMOPs, this paper presents a dynamic MOEA with multiple response strategies based on linear environment detection, called DMOEA-LEM. In this approach, different types of environmental changes are estimated and then the corresponding response strategies are activated to generate an efficient initial population for the new environment. DMOEA-LEM not only detects whether the environmental changes but also estimates the types of linear changes so that different prediction models can be selected to initialize the population when the environmental changes. To study the performance of DMOEA-LEM, a large number of test DMOPs are adopted and the experiments validate the advantages of our algorithm when compared to three state-of-the-art dynamic MOEAs.

scholarly journals Dynamic Multiobjective Optimization Algorithm Based on Average Distance Linear Prediction Model

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Zhiyong Li ◽  
Hengyong Chen ◽  
Zhaoxin Xie ◽  
Chao Chen ◽  
Ahmed Sallam
Keyword(s):  
Multiobjective Optimization ◽  
Prediction Model ◽  
Linear Prediction ◽  
Prediction Models ◽  
Optimization Problems ◽  
Average Distance ◽  
Test Problems ◽  
Multiobjective Optimization Problems ◽  
Dynamic Multiobjective Optimization ◽  
Linear Prediction Model

Many real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. Optimization in a changing environment is a challenging task, especially when multiple objectives are required to be optimized simultaneously. Nowadays the common way to solve dynamic multiobjective optimization problems (DMOPs) is to utilize history information to guide future search, but there is no common successful method to solve different DMOPs. In this paper, we define a kind of dynamic multiobjectives problem with translational Paretooptimal set (DMOP-TPS) and propose a new prediction model named ADLM for solving DMOP-TPS. We have tested and compared the proposed prediction model (ADLM) with three traditional prediction models on several classic DMOP-TPS test problems. The simulation results show that our proposed prediction model outperforms other prediction models for DMOP-TPS.

scholarly journals A multipopulation evolutionary framework with Steffensen’s method for dynamic multiobjective optimization problems

2021 ◽  
Author(s):  
Tianyu Liu ◽  
Lei Cao ◽  
Zhu Wang
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Population Diversity ◽  
Environment Changes ◽  
Steffensen’S Method ◽  
Diversity Maintenance ◽  
Multiobjective Optimization Problems ◽  
Comprehensive Comparison ◽  
Dynamic Multiobjective Optimization ◽  
Single Objective

AbstractDynamic multiobjective optimization problems (DMOPs) require the evolutionary algorithms that can track the moving Pareto-optimal fronts efficiently. This paper presents a dynamic multiobjective evolutionary framework (DMOEF-MS), which adopts a novel multipopulation structure and Steffensen’s method to solve DMOPs. In DMOEF-MS, only one population deals with the original DMOP, while the others focus on single-objective problems that are generated by the weighted summation of the original DMOP. Then, Steffensen’s method is used to control the evolving process in two ways: prediction and diversity-maintenance. Particularly, the prediction strategy is devised to predict the next promising positions for the individuals that handle single-objective problems, and the diversity-maintenance strategy is used to increase population diversity before the environment changes and reinitialize the multiple populations after the environment changes. This paper gives a comprehensive comparison of DMOEF-MS with some state-of-the-art DMOEAs on 14 DMOPs and the experimental results demonstrate the effectiveness of the proposed algorithm.

scholarly journals The Evolutionary Algorithm to Find Robust Pareto-Optimal Solutions over Time

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Meirong Chen ◽  
Yinan Guo ◽  
Haiyuan Liu ◽  
Chun Wang
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multiobjective Optimization Problems ◽  
Dynamic Multiobjective Optimization ◽  
Pareto Fronts ◽  
Over Time

In dynamic multiobjective optimization problems, the environmental parameters change over time, which makes the true pareto fronts shifted. So far, most works of research on dynamic multiobjective optimization methods have concentrated on detecting the changed environment and triggering the population based optimization methods so as to track the moving pareto fronts over time. Yet, in many real-world applications, it is not necessary to find the optimal nondominant solutions in each dynamic environment. To solve this weakness, a novel method called robust pareto-optimal solution over time is proposed. It is in fact to replace the optimal pareto front at each time-varying moment with the series of robust pareto-optimal solutions. This means that each robust solution can fit for more than one time-varying moment. Two metrics, including the average survival time and average robust generational distance, are present to measure the robustness of the robust pareto solution set. Another contribution is to construct the algorithm framework searching for robust pareto-optimal solutions over time based on the survival time. Experimental results indicate that this definition is a more practical and time-saving method of addressing dynamic multiobjective optimization problems changing over time.

Sign in / Sign up

Export Citation Format

Share Document