A Many-Objective Particle Swarm Optimizer With Leaders Selected From Historical Solutions by Using Scalar Projections

Xiang, Yi; Chen, Zefeng; Zhang, Mengjie

doi:10.1109/tcyb.2018.2884083

Cited by 25 publications

(29 citation statements)

References 71 publications

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“…Multiple swarms coevolved in a distributed fashion to maintain diversity for approximating the entire PFs, while a novel bottleneck objective learning strategy was used to accelerate convergence for all objectives. In MaPSO [46], a novel MOPSO based on the acute angle was proposed, in which the leader of particles was selected from its historical particles by using the scalar projections and each particle owned K historical particle information (K was set to 3 in its experiments). Moreover, the environmental selection in MaPSO was run based on the acute angle of each pair of particles.…”

Section: Some Current Mopsos and Moeas For Maopsmentioning

confidence: 99%

“…(1) NMPSO [24]: this algorithm uses a balanceable fitness estimation to offer sufficient selection pressure in the search process, which considers both of the convergence and diversity with weight vectors. (2) MaPSO [46]: this algorithm uses an angle-based MOPSO called MaPSO. MaPSO chooses the leader positional information of particles from its historical particles by using scalar projections to guide particles.…”

Section: The Experimental Studiesmentioning

confidence: 99%

“…In this paper, four PSO algorithms (dMOPSO [17], SMPSO [14], NMPSO [24], and MaPSO [46]), and four competitive MOEAs (VaEA, θ − DEA, MOEA/DD, and SPEA2 + SDE), were used for performance comparison.…”

Section: General Experimental Parameter Settingsmentioning

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: Some Current Mopsos and Moeas For Maopsmentioning

confidence: 99%

Section: The Experimental Studiesmentioning

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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“…The third sub-section compares our proposed HGLSS with respect to other leader selection strategies under the framework of the MOPSO algorithm. The last sub-section compares the performance of MOPSO-HGLSS with respect to nine popular population-based metaheuristics (SMPSO [25], dMOPSO [29], MOPSOhv [30], MaPSO [31], MOEA/D [32] NSGA-III [33], DBEA [34], RVEA [35] and ARMOEA [36]), in terms of IGD + with different scalable MOPs (using 3, 5, 8 and 10 objectives). Section V presents our conclusions and some possible paths for future research.…”

Section: Some Moeas Have Recently Been Proposed To Handlementioning

confidence: 99%

“…The compared algorithms can be classified into two groups: MOPSOs and MOEAs. The group of MOPSOs consists of SMPSO [25], dMOPSO [29], MOPSOhv [30], MaPSO [31] and the proposed algorithm, while the other group consists of MOEA/D [32], NSGA-III [33], DBEA [34], RVEA [35] and ARMOEA [36]. In [25], the authors found that the speed of the particles in MOPSO was sometimes too high, making the particles move directly towards the boundaries.…”

Section: Performance Comparisons Of Hglss-mopso With Other Algorithmsmentioning

confidence: 99%

A Hybrid Leader Selection Strategy for Many-Objective Particle Swarm Optimization

et al. 2020

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Many existing Multi-objective Particle Swarm Optimizers (MOPSOs) may encounter difficulties for a set of good approximated solutions when solving problems with more than three objectives. One possible reason is that the diluted selection pressure causes MOPSOs to fail to generate a set of good approximated Pareto solutions. In this paper, a new approach called the Hybrid Global Leader Selection Strategy (HGLSS) is proposed to deal with many-objective problems more effectively. HGLSS provides two global leader selection mechanisms: one for exploration and one for exploitation. Each particle (solution) can choose one of these two leader selection schemes to identify its global best leader. An external archive is adopted for maintaining the diversity of the found solutions and it contains the final solution reported at the end of the run. The update of the external archive is based on both Pareto dominance and density estimation. The performance of the proposed approach is compared with respect to nine state-of-the-art multi-objective metaheuristics in solving several benchmark problems. Our results indicate that the proposed algorithm generally outperforms the others in terms of Modified Inverted Generational Distance (IGD +) indicator.

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Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions

Satō

Ishibuchi

2023

IEEJ Transactions Elec Engng

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Population‐based evolutionary algorithms are suitable for solving multi‐objective optimization problems involving multiple conflicting objectives. This is because a set of well‐distributed solutions can be obtained by a single run, which approximate the optimal tradeoff among the objectives. Over the past three decades, evolutionary multi‐objective optimization has been intensively studied and used in various real‐world applications. However, evolutionary multi‐objective optimization faces various difficulties as the number of objectives increases. The simultaneous optimization of more than three objectives, which is called many‐objective optimization, has attracted considerable research attention. This paper explains various difficulties in evolutionary many‐objective optimization, reviews representative approaches, and discusses their effects and limitations. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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A Many-Objective Particle Swarm Optimizer With Leaders Selected From Historical Solutions by Using Scalar Projections

Cited by 25 publications

References 71 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 Hybrid Leader Selection Strategy for Many-Objective Particle Swarm Optimization

Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions

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