scholarly journals Covering Pareto-optimal fronts by subswarms in multi-objective particle swarm optimization

2005 ◽  
Author(s):  
S. Mostaghim ◽  
J. Teich
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pareto Optimal ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Pareto Optimal Fronts

scholarly journals A novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm

2020 ◽  
Author(s):  
Ahlem Aboud ◽  
Raja Fdhila ◽  
Amir Hussain ◽  
Adel Alimi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Response Strategy ◽  
Pareto Optimal ◽  
Distributed Architecture ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set

Distributed architecture-based Particle Swarm Optimization is very useful for static optimization and not yet explored to solve complex dynamic multi-objective optimization problems. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm with two optimization levels. In the first level, all solutions are optimized in the same search space and the second level is based on a distributed architecture using the Pareto ranking operator for dynamic multi-swarm subdivision. The proposed approach adopts a dynamic handling strategy using a set of detectors to keep track of change in the objective function that is impacted by the problem’s time-varying parameters at each level. To ensure timely adaptation during the optimization process, a dynamic response strategy is considered for the reevaluation of all non-improved solutions, while the worst particles are replaced with a newly generated one. The convergence and<br>diversity performance of the DPb-MOPSO algorithm are proven through Friedman Analysis of Variance, and the Lyapunov theorem is used to prove stability analysis over the Inverted Generational Distance (IGD) and Hypervolume Difference (HVD) metrics. Compared to other evolutionary algorithms, the novel DPb-MOPSO is shown to be most robust for solving complex problems over a range of changes in both the Pareto Optimal Set and Pareto Optimal Front. <br>

Multi-Objective Particle Swarm Optimization with Comparison Scheme and New Pareto-Optimal Search Strategy

2014 ◽  
Vol 496-500 ◽  
pp. 1895-1900
Author(s):  
Wen Wang ◽  
Wei Shen ◽  
Chao Long Ying ◽  
Xin Yi Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Search Strategy ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Experimental Results ◽  
Pareto Optimal ◽  
Optimal Search ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Convergence Performance

In the presented article, a novel multi-objective PSO algorithm, RP-MOPSO has been proposed. The algorithm adopts a new comparison scheme for position upgrading. The scheme is simple but effective in improve algorithms convergence speed. A sigma-density strategy of selecting the global best particle for each particle in swarm based on a new solutions density definition is designed. Experimental results on seven functions show that proposed algorithm show better convergence performance than other classical MOP algorithms. Meanwhile the proposed algorithm is more effective in maintaining the diversity of the solutions.

scholarly journals DPb-MOPSO: A Novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization Algorithm

2021 ◽  
Author(s):  
Ahlem Aboud ◽  
Nizar Rokbani ◽  
Raja Fdhila ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Environmental Changes ◽  
Dynamic Change ◽  
Particle Swarm ◽  
Search Space ◽  
Response Strategy ◽  
Parallel Optimization ◽  
Pareto Optimal ◽  
Swarm Optimization ◽  
Multi Objective

Particle swarm optimization system based on the distributed architecture has shown its efficiency for static optimization and has not been studied to solve dynamic multiobjective problems (DMOPs). When solving DMOP, tracking the best solutions over time and ensuring good exploitation and exploration are the main challenging tasks. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected, the Pareto ranking operator is used to enable a multiswarm subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-evaluate all unimproved solutions and replacing them with newly generated ones is also implemented. Inverted generational distance, mean inverted generational distance, and hypervolume difference metrics are used to assess the DPb-MOPSO performances. All quantitative results are analyzed using Friedman's analysis while the Lyapunov theorem is used for stability analysis. Compared with several multi-objective evolutionary algorithms, the DPb-MOPSO is robust in solving 21 complex problems over a range of changes in both the Pareto optimal set and Pareto optimal front. For 13 UDF and ZJZ functions, DPb-MOPSO can solve 8/13 and 7/13 on IGD and HVD with moderate changes. For the 8 FDA and dMOP benchmarks, DPb-MOPSO was able to resolve 4/8 with severe change on MIGD, and 5/8 for moderate and slight changes. However, for the 3 kind of environmental changes, DPb-MOPSO assumes 4/8 of the solving function on IGD and HVD. <br>

scholarly journals DPb-MOPSO: A Novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization Algorithm

2022 ◽  
Author(s):  
Ahlem Aboud ◽  
Nizar Rokbani ◽  
Raja Fdhila ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Environmental Changes ◽  
Dynamic Change ◽  
Particle Swarm ◽  
Search Space ◽  
Response Strategy ◽  
Parallel Optimization ◽  
Pareto Optimal ◽  
Swarm Optimization ◽  
Multi Objective

Particle swarm optimization system based on the distributed architecture over multiple sub-swarms has shown its efficiency for static optimization and has not been studied to solve dynamic multi-objective problems (DMOPs). When solving DMOP, tracking the best solutions over time and ensuring good exploitation and exploration are the main challenging tasks. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected in the objective values, the Pareto ranking operator is used to enable a multiple sub-swarm’ subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-evaluate all unimproved solutions and replacing them with newly generated ones is also implemented. Inverted generational distance, mean inverted generational distance, and hypervolume difference metrics are used to assess the DPb-MOPSO performances. All quantitative results are analyzed using Friedman's analysis of variance while the Lyapunov theorem is used for stability analysis. Compared with several multi-objective evolutionary algorithms, the DPb-MOPSO is robust in solving 21 complex problems over a range of changes in both the Pareto optimal set and Pareto optimal front. For 13 UDF and ZJZ functions, DPb-MOPSO can solve 8/13 and 7/13 on IGD and HVD with moderate changes. For the 8 FDA and dMOP benchmarks, DPb-MOPSO was able to resolve 4/8 with severe change on MIGD, and 5/8 for moderate and slight changes. However, for the 3 kind of environmental changes, DPb-MOPSO assumes 4/8 of the solving function on IGD and HVD metrics.<br>

scholarly journals DPb-MOPSO: A Novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization Algorithm

2021 ◽  
Author(s):  
Ahlem Aboud ◽  
Nizar Rokbani ◽  
Raja Fdhila ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Environmental Changes ◽  
Dynamic Change ◽  
Particle Swarm ◽  
Search Space ◽  
Response Strategy ◽  
Parallel Optimization ◽  
Pareto Optimal ◽  
Swarm Optimization ◽  
Multi Objective

Particle swarm optimization system based on the distributed architecture has shown its efficiency for static optimization and has not been studied to solve dynamic multiobjective problems (DMOPs). When solving DMOP, tracking the best solutions over time and ensuring good exploitation and exploration are the main challenging tasks. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected, the Pareto ranking operator is used to enable a multiswarm subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-evaluate all unimproved solutions and replacing them with newly generated ones is also implemented. Inverted generational distance, mean inverted generational distance, and hypervolume difference metrics are used to assess the DPb-MOPSO performances. All quantitative results are analyzed using Friedman's analysis while the Lyapunov theorem is used for stability analysis. Compared with several multi-objective evolutionary algorithms, the DPb-MOPSO is robust in solving 21 complex problems over a range of changes in both the Pareto optimal set and Pareto optimal front. For 13 UDF and ZJZ functions, DPb-MOPSO can solve 8/13 and 7/13 on IGD and HVD with moderate changes. For the 8 FDA and dMOP benchmarks, DPb-MOPSO was able to resolve 4/8 with severe change on MIGD, and 5/8 for moderate and slight changes. However, for the 3 kind of environmental changes, DPb-MOPSO assumes 4/8 of the solving function on IGD and HVD. <br>

Comprehensive Optimization Management of Engineering Project Based on Multi-Objective Particle Swarm Optimization

2020 ◽  
Vol 13 ◽  
Author(s):  
Yong Xiang ◽  
Huidan Zheng ◽  
Wuwen Cao ◽  
Dong Gong ◽  
Jiazhen Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Traditional Management ◽  
Multi Objective ◽  
Management Objectives ◽  
Optimization Management

: As the construction industry becomes more sustainable in the future, such as green, ecology, and safety, the higher the requirements for the ultimate objectives of the project.The traditional management objectives of investment, duration, and quality can no longer meet the requirements of comprehensive optimization management. Therefore, from the perspective of the project owners, the work introduced the safety and environmental objectives based on traditional management objectives. The thesis analyzes the relationship between the objectives, and builds the equilibrium optimization model. Moreover, this thesis uses multi-objective particle swarm optimization (MOPSO) to solve the problem, and obtains a series of Pareto optimal solutions. Then, according to the specific requirements of project management and the use of the efficacy coefficient method, the best solution is selected from the Pareto optimal solutions. Finally, a Sichuan wind power project is taken as an example. The work used the MOPSO to run 1,000 trails, and calculate the mean and standard deviation. It verified the rationality of model and the practicability of MOPSO.

Multi-Reservoir Ecological Operation Using Multi-Objective Particle Swarm Optimization

2014 ◽  
Vol 641-642 ◽  
pp. 65-69 ◽  
Author(s):  
Wei Lin Liu ◽  
Li Na Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Water Resources ◽  
Particle Swarm ◽  
Main Stream ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
River Ecosystem ◽  
Swarm Optimization ◽  
Multi Objective

Traditional reservoir operation ignores ecological demands of rivers. This would probably lead to degradation of river ecosystem. In order to alleviate the influence of reservoirs on river ecosystem, multi-objective reservoir ecological operation was proposed from perspective of maintaining the river ecosystem health. Multi-objective mathematical model of multi-reservoir ecological operation was established. A multi-objective particle swarm optimization (MOPSO) algorithm was introduced to generate a set of Pareto-optimal solutions. In addition, to facilitate easy implementation for the reservoir operator, a simple but effective decision-making method was presented to choose the desired alternative from a set of Pareto-optimal solutions. Finally, the proposed approach was applied to the ecological operation of the reservoirs at the main stream of Xiuhe river in Poyang Lake basin in China. The results show that the proposed approach is able to offer many alternative policies for the water resources managers, and it is a viable alternative to solve multi-objective water resources and hydrology problems.

Reactive Power Optimization Using Multi-Objective Particle Swarm Optimization Algorithm Based on Pareto

2014 ◽  
Vol 945-949 ◽  
pp. 2409-2412
Author(s):  
Ying Ai ◽  
Yi Xin Su ◽  
Dan Hong Zhang ◽  
Yao Peng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Pareto Optimal ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Multi Objective

In order to avoid the defect that particle swarm optimization algorithm is easy to trap into local optimal solution, an improved multi-objective particle swarm algorithm based on the Pareto optimal set is proposed to deal with reactive power optimization of power system. Taking the minimum active network loss and voltage offset as objective, index functions of multi-objective reactive power optimization are established. The algorithm uses a group fitness variance judging mechanism to update each particle’s inertia weight so as to enhance their global searching ability, and adopts the elite archiving technology to get a set of Pareto optimal solutions so as to improve the diversity of the solution. Simulation of IEEE 30 bus system demonstrates that the proposed method has fast convergence speed and high optimization accuracy.

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