Indicator-based set evolution particle swarm optimization for many-objective problems

Sun, Xiaoyan; Chen, Yang; Liu, Yiping; Gong, Dunwei

doi:10.1007/s00500-015-1637-1

Cited by 18 publications

(7 citation statements)

References 34 publications

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“…Also, most codes of the compared algorithm can be found in our source code, such as VaEA [27], θ-DEA [48], and MOEA/DD [50]. Test problems are the WFG [23] and MaF [24]. WFG [23] can be found at http:// jmetal.sourceforge.net/problems.html and MaF [24] can be found at https://github.com/BIMK/PlatEMO.…”

Section: Data Availabilitymentioning

confidence: 99%

“…Test problems are the WFG [23] and MaF [24]. WFG [23] can be found at http:// jmetal.sourceforge.net/problems.html and MaF [24] can be found at https://github.com/BIMK/PlatEMO.…”

Section: Data Availabilitymentioning

confidence: 99%

“…e third category employs the performance indicators (e.g., HV [20] and R2 [21]) to guide the search process in MOPSOs (called indicator-based MOPSOs), such as IBPSO-LS [22], S-MOPSO [23], NMPSO [24], R2HMOPSO [25], and R2-MOPSO [26].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems

Chen

Liu

et al. 2020

Complexity

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Most multiobjective particle swarm optimizers (MOPSOs) often face the challenges of keeping diversity and achieving convergence on tackling many-objective optimization problems (MaOPs), as they usually use the nondominated sorting method or decomposition-based method to select the local or best particles, which is not so effective in high-dimensional objective space. To better solve MaOPs, this paper presents a novel angular-guided particle swarm optimizer (called AGPSO). A novel velocity update strategy is designed in AGPSO, which aims to enhance the search intensity around the particles selected based on their angular distances. Using an external archive, the local best particles are selected from the surrounding particles with the best convergence, while the global best particles are chosen from the top 20% particles with the better convergence among the entire particle swarm. Moreover, an angular-guided archive update strategy is proposed in AGPSO, which maintains a consistent population with balanceable convergence and diversity. To evaluate the performance of AGPSO, the WFG and MaF test suites with 5 to 10 objectives are adopted. The experimental results indicate that AGPSO shows the superior performance over four current MOPSOs (SMPSO, dMOPSO, NMPSO, and MaPSO) and four competitive evolutionary algorithms (VaEA, θ-DEA, MOEA\D-DD, and SPEA2-SDE), when solving most of the test problems used.

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Section: Data Availabilitymentioning

confidence: 99%

“…Test problems are the WFG [23] and MaF [24]. WFG [23] can be found at http:// jmetal.sourceforge.net/problems.html and MaF [24] can be found at https://github.com/BIMK/PlatEMO.…”

Section: Data Availabilitymentioning

confidence: 99%

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A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems

Chen

Liu

et al. 2020

Complexity

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“…MOPSOhv [30] uses HV as the guiding indicator, and R2-MOPSO [31] uses R2, which balances convergence and distribution, as the guiding indicator. In [32], a dynamic mixed performance indicator-based multi-objective particle swarm optimizer is proposed. The performance of the algorithm varies greatly according to the index.…”

Section: Introductionmentioning

confidence: 99%

A Competitive Particle Swarm Algorithm Based on Vector Angles for Multi-Objective Optimization

2021

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Recently, the particle swarm algorithm (PSO) has demonstrated its effectiveness in solving multi-objective optimization problems. However, the performance of most existing multi-objective particle swarm algorithms depends largely on the global or individual best particles. Moreover, due to the rapid convergence of PSO in single objective optimization problems, PSO is prone to poorly distributed indicators when dealing with multi-objective optimization problems. To solve the above problems, we propose a multi-objective competitive particle swarm algorithm based on vector angles (VaCSO). Firstly, in order to remove the influence of global best particles or individual best particles on the algorithm, the competition mechanism is used. Secondly, in order to increase the diversity of solutions while maintaining the convergence of the algorithm, the population is clustered into two populations. Population 1 mainly considers the convergence of the solution in the offspring generation strategy. As a supplement, population 2 adds a new offspring generation strategy to maintain the distribution of the solution, and we innovatively proposed a three-particle competition to improve the distribution and diversity of particle swarms. Finally, based on vector angle information, we consider auxiliary learning to optimize the population gap, so as to improve the distribution of the algorithm. We have established two sets of comparative experiments to test the performance of VaCSO. We compared VaCSO with the currently popular multi-objective particle swarm optimizers and multi-objective evolutionary algorithms. Experimental results show that VaCSO has an excellent performance in convergence and distribution, and has a significant effect in optimizing quality.INDEX TERMS multi-objective optimization, competition particle swarm, competition mechanism, threeparticle competition, vector angle information, auxiliary learning.

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“…In [34], Xiang et al introduced decomposition into ABC to solve MaOPs. Moreover, indicatorbased set and reference-point are combined with PSO for many-objective optimization [35]- [37]. The above improved SIOAs were extended to handle MaOPs, but there still exist some issues.…”

Section: Introductionmentioning

confidence: 99%

A Coupling Approach With GSO-BFOA for Many-Objective Optimization

et al. 2019

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Glowworm swarm optimization (GSO) and bacterial foraging optimization algorithm (BFOA) are two popular swarm intelligence optimization algorithms (SIOAs). However, both GSO and BFOA show some difficulties when solving many-objective optimization problems (MaOPs). To challenge MaOPs, a coupling approach based on GSO and BFOA is proposed in this paper. To implement the coupling method, an external archive is established to save the best solutions found so far. The internal populations in GSO and BFOA can exchange the search information with the external archive in the evolutionary process. Simulation experiments are verified on two benchmark sets (DTLZ and WFG) with 3 to 15 objectives. The performance of our approach is compared with five other famous algorithms including NSGA-III, KnEA, MOEA/D-DE, GrEA and HypE. Results prove the effectiveness of our approach.

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Indicator-based set evolution particle swarm optimization for many-objective problems

Cited by 18 publications

References 34 publications

A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems

A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems

A Competitive Particle Swarm Algorithm Based on Vector Angles for Multi-Objective Optimization

A Coupling Approach With GSO-BFOA for Many-Objective Optimization

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