MOPSOhv: A new hypervolume-based multi-objective particle swarm optimizer

I, Garcia; Coello, Carlos A. Coello; Arias-Montaño, Alfredo

doi:10.1109/cec.2014.6900540

Cited by 33 publications

(16 citation statements)

References 21 publications

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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%

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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Section: Some Moeas Have Recently Been Proposed To Handlementioning

confidence: 99%

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

et al. 2020

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“…At present, a lot of MOEAs adopted the global optimum selection strategy based on the nondominated sorting [10, 11] or Pareto dominance [32] or hypervolume [33, 34] or niche [35, 36] and so on. But they all have some problems of high selection pressure or low selection pressure.…”

Section: Related Work and Motivationmentioning

confidence: 99%

AMOBH: Adaptive Multiobjective Black Hole Algorithm

et al. 2017

Computational Intelligence and Neuroscience

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This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called “adaptive multiobjective black hole algorithm” (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.

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“…2) The MOPSOhv algorithm: MOPSOhv is a hypervolumebased multi-objective particle swarm algorithm. MOPSOhv proved to be effective in many-objective optimization problems [14]. From an operation point of view, MOPSOhv randomly initializes the swarm with N particles.…”

Section: B Multi-and Many-objective Optimizationmentioning

confidence: 99%

“…The optimization problem is solved in two steps. First, a hypervolume-based particle swarm multiobjective algorithm (i.e., MOPSOhv [14]) generates a set of Pareto optimal recruitment plans. Then, the best recruitment plan is selected by means of the TOPSIS algorithm.…”

Section: Introductionmentioning

confidence: 99%

Human factors-based many-objective personnel recruitment for safety-critical work environments

Lazzerini

Pistolesi

2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

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In spite of many improvements in industrial safety of the last decades, nowadays four people per minute die in the world for occupational illnesses and accidents at work. Besides equipping machines with the most advanced technologies, industrial safety has become more and more interested in human factors in recent years, since many accidents at work are proven to be blamed on dangerous behaviours of workers. Recruiting workers with proper risk perception and caution can make safer their interaction with the task assigned, thus reducing devastating events. This paper presents a many-objective optimization framework for personnel recruitment in safety-critical work environments. Four objectives are considered: cost and learning time (which are minimized), and risk perception and caution (which are maximized). A neural network-based module computes each candidate's risk perception and caution for every single task he/she applies for. Pareto optimal solutions are generated using the Multi-Objective Particle Swarm Optimizer based on hypervolume (MOPSOhv). The best personnel recruitment is selected by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The effectiveness of the proposed framework was validated on two real-world recruitment processes involving 100 and 300 candidates, respectively.

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MOPSOhv: A new hypervolume-based multi-objective particle swarm optimizer

Cited by 33 publications

References 21 publications

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

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

AMOBH: Adaptive Multiobjective Black Hole Algorithm

Human factors-based many-objective personnel recruitment for safety-critical work environments

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