An Interactive Framework to Compare Multi-criteria Optimization Algorithms: Preliminary Results on NSGA-II and MOPSO

Dorado-Sevilla, David F.; Peluffo-Ordóñez, Diego H.; Lorente-Leyva, Leandro L.; Herrera-Granda, Erick P.; Herrera-Granda, Israel David

doi:10.1007/978-981-33-4909-4_5

Cited by 2 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the running time is [2.924EÀ03s, 2.509EÀ02s] for solving ZDT benchmark functions and the running time is [2.765EÀ02s, 9.062EÀ02s] for solving DTLZ benchmark. In order to prove the superiority of the ICDMOPSO algorithm, NSGA-III (Miriam et al, 2021), MOEA/D (Li et al, 2019b), cdMOPSO (Raquel and Naval, 2005), MOMPSO (Dorado-Sevilla et al, 2021) and MODE/DMSM (Deb and Jain, 2014) are selected for comparative analysis. The parameter settings of three algorithms are consistent with the original literature.…”

Section: Experimental Results and Comparative Analysismentioning

confidence: 99%

“…In order to prove the superiority of the ICDMOPSO algorithm, NSGA-III (Miriam et al , 2021), MOEA/D (Li et al , 2019b), cdMOPSO (Raquel and Naval, 2005), MOMPSO (Dorado-Sevilla et al , 2021) and MODE/DMSM (Deb and Jain, 2014) are selected for comparative analysis. The parameter settings of three algorithms are consistent with the original literature.…”

Section: Simulation Experiments and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Multi-objective particle swarm optimization algorithm using Cauchy mutation and improved crowding distance

Zeng

Wei

2022

IJICC

View full text Add to dashboard Cite

PurposeMulti-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.Design/methodology/approachIn this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.FindingsIn order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.Originality/valueIn order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

show abstract

Section: Experimental Results and Comparative Analysismentioning

confidence: 99%

Section: Simulation Experiments and Analysismentioning

confidence: 99%

Multi-objective particle swarm optimization algorithm using Cauchy mutation and improved crowding distance

Zeng

Wei

2022

IJICC

View full text Add to dashboard Cite

show abstract

An improved indirect adaptive neural control performance based on MOPSO approach: An experimental validation via a transesterification reactor

Hamza,

Zribi,

Farhat

2024

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

This paper proposes an indirect adaptive control method based on recurrent neural networks. To achieve satisfactory closed-loop performances, a neural emulator (NE) and a neural controller (NC) adapting rates are established using the multiobjective particle swarm optimization (MOPSO) algorithm. The proposed MOPSO algorithm has been designed to minimize, simultaneously, two separated objective functions: the emulation and the tracking errors. The proposed approach guarantees that the NE tracks the system dynamics within a short time window. Consequently, it provides for the suggested control structure useful information to synthesize optimal adaptive rates of the NE and NC. To validate the effectiveness of the proposed MOPSO algorithm, a numerical example and an experimental validation on a chemical reactor are proposed.

show abstract

An Interactive Framework to Compare Multi-criteria Optimization Algorithms: Preliminary Results on NSGA-II and MOPSO

Cited by 2 publications

References 15 publications

Multi-objective particle swarm optimization algorithm using Cauchy mutation and improved crowding distance

Multi-objective particle swarm optimization algorithm using Cauchy mutation and improved crowding distance

An improved indirect adaptive neural control performance based on MOPSO approach: An experimental validation via a transesterification reactor

Contact Info

Product

Resources

About