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 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 A Multiobjective Optimization Algorithm Based on Discrete Bacterial Colony Chemotaxis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhigang Lu ◽  
Tao Feng ◽  
Zhaozheng Liu
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Population Diversity ◽  
Transfer Mechanism ◽  
Bacterial Colony ◽  
Continuous Space ◽  
Pareto Optimal Set ◽  
Crowding Distance ◽  
Multiobjective Optimization Problems ◽  
Definition Of

Bacterial colony chemotaxis algorithm was originally developed for optimal problem with continuous space. In this paper the discrete bacterial colony chemotaxis (DBCC) algorithm is developed to solve multiobjective optimization problems. The basic DBCC algorithm has the disadvantage of being trapped into the local minimum. Therefore, some improvements are adopted in the new algorithm, such as adding chaos transfer mechanism when the bacterium choose their next locations and the crowding distance operation to maintain the population diversity in the Pareto Front. The definition of chaos transfer mechanism is used to retain the elite solution produced during the operation, and the definition of crowding distance is used to guide the bacteria for determinate variation, thus enabling the algorithm obtain well-distributed solution in the Pareto optimal set. The convergence properties of the DBCC strategy are tested on some test functions. At last, some numerical results are given to demonstrate the effectiveness of the results obtained by the new algorithm.

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 An MOEA/D-ACO with PBI for Many-Objective Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Tianbai Ling ◽  
Chen Wang
Keyword(s):  
Evolutionary Algorithms ◽  
Multiobjective Optimization ◽  
Selection Pressure ◽  
Optimization Problems ◽  
State Of The Art ◽  
Experimental Results ◽  
Significant Problem ◽  
Adjustment Method ◽  
Multiobjective Optimization Problems ◽  
Single Objective

Evolutionary algorithms (EAs) are an important instrument for solving the multiobjective optimization problems (MOPs). It has been observed that the combined ant colony (MOEA/D-ACO) based on decomposition is very promising for MOPs. However, as the number of optimization objectives increases, the selection pressure will be released, leading to a significant reduction in the performance of the algorithm. It is a significant problem and challenge in the MOEA/D-ACO to maintain the balance between convergence and diversity in many-objective optimization problems (MaOPs). In the proposed algorithm, an MOEA/D-ACO with the penalty based boundary intersection distance (PBI) method (MOEA/D-ACO-PBI) is intended to solve the MaOPs. PBI decomposes the problems with many single-objective problems, a weighted vector adjustment method based on clustering, and uses different pheromone matrices to solve different single objectives proposed. Then the solutions are constructed and pheromone was updated. Experimental results on both CF1-CF4 and suits of C-DTLZ benchmarks problems demonstrate the superiority of the proposed algorithm in comparison with three state-of-the-art algorithms in terms of both convergence and diversity.

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