A multi-objective particle swarm optimizer based on decomposition

Zapotecas-Martínez, Saúl; Coello, Carlos A. Coello

doi:10.1145/2001576.2001587

Cited by 108 publications

(19 citation statements)

References 19 publications

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“…Many papers have shown particle swarm to be an improvement over the GA in terms of efficiency and optimal values found [32][33][34][35]. On top of this, PSO algorithms have been updated to include characteristics found in evolutionary methods, such as mutation and ageing [36,37]. For these reasons, it was decided that the particle swarm optimizer was the superior option, with the ability to incorporate most of the techniques found in genetic algorithms.…”

Section: A Methodologymentioning

confidence: 99%

Creation of Design and Analysis Tools for Large Design Space Reusable Launch Vehicle Shape Optimization

Cusick

Kontis

2019

AIAA Aviation 2019 Forum

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A design optimization suite currently under development for arbitrary super-hypersonic vehicles is introduced. A brief review of literature on the subject is presented. Parameterization techniques utilized to obtain realistic geometries of various flying components are discussed. Well known engineering methods for the prediction of inviscid high speed aerodynamic coefficients are outlined, alongside implementation of streamline tracing and mesh interpolation for the calculation of viscous and local condition dependent approaches. Global optimization methods are employed, with detailed discussion on the specific characteristics chosen for this work. Validation cases are presented for the aerodynamic module, comparing computed results to wind tunnel tests gathered in previously published research. An overview of the optimization loop is provided, followed by an aerodynamic design optimization, demonstrating the potential of the combined methodologies.

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Section: A Methodologymentioning

confidence: 99%

Creation of Design and Analysis Tools for Large Design Space Reusable Launch Vehicle Shape Optimization

Cusick

Kontis

2019

AIAA Aviation 2019 Forum

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“…In this section, the proposed algorithms will be extensively compared with three additional algorithms: modified Indicator based Evolutionary Algorithm (MIBEA) [83], SMSEMOA: Multiobjective selection based on dominated hypervolume [84], and multiobjective PSO with decomposition (DMOPSO) [85], in term of IGD metric on GLT test problems to show clearly the effectiveness of our proposed algorithm. Those three algorithms are compared with the proposed according to their implementation in JMetal frameworks under the cited parameters values.…”

Section: Computational Cost and Other Comparisonsmentioning

confidence: 99%

An Efficient Marine Predators Algorithm for Solving Multi-Objective Optimization Problems: Analysis and Validations

et al. 2021

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Recently, a new strong optimization algorithm called marine predators algorithm (MPA) has been proposed for tackling the single-objective optimization problems and could dramatically fulfill good outcomes in comparison to the other compared algorithms. Those dramatic outcomes, in addition to our recently-proposed strategies for helping meta-heuristic algorithms in fulfilling better outcomes for the multi-objective optimization problems motivate us to make a comprehensive study to see the performance of MPA alone and with those strategies for those optimization problems. Specifically, This paper proposes four variants of the marine predators' algorithm (MPA) for solving multi-objective optimization problems. The first version, called the multi-objective marine predators' algorithm (MMPA) is based on the behavior of marine predators in finding their prey. In the second version, a novel strategy called dominance strategybased exploration-exploitation (DSEE) recently-proposed is effectively incorporated with MMPA to relate the exploration and exploitation phase of MPA to the dominance of the solutions-this version is called M-MMPA. DSEE counts the number of dominated solutions for each solution-the solutions with high dominance undergo an exploitation phase; the others with small dominance undergo the exploration phase. The third version integrates M-MMPA with a novel strategy called Gaussian-based mutation, which uses the Gaussian distribution-based exploration and exploitation strategy to search for the optimal solution. The fourth version uses the Nelder-Mead simplex method with M-MMPA (M-MMPA-NMM) at the start of the optimization process to construct a front of the non-dominated solutions that will help M-MMPA to find more good solutions. The effectiveness of the four versions is validated on a large set of theoretical and practical problems. For all the cases, the proposed algorithm and its variants are shown to be superior to a number of well-known multi-objective optimization algorithms.

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“…Then, Pareto solutions could be achieved by minimizing such subproblems. There exists several methods to construct the aggregation function [54] for each subproblem with a weight vector, such as weight sum approach [44], Tchebycheff approach [55], penalty-based boundary intersection (PBI) [44], etc. Here, AIR 2.0 uses the PBI approach to construct the aggregation function for each subproblem.…”

Section: Decomposition Approach In Multi-objective Optimizationmentioning

confidence: 99%

Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy

Zhou

Wang

Pan

et al. 2021

IJMS

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Protein structure refinement is a crucial step for more accurate protein structure predictions. Most existing approaches treat it as an energy minimization problem to intuitively improve the quality of initial models by searching for structures with lower energy. Considering that a single energy function could not reflect the accurate energy landscape of all the proteins, our previous AIR 1.0 pipeline uses multiple energy functions to realize a multi-objectives particle swarm optimization-based model refinement. It is expected to provide a general balanced conformation search protocol guided from different energy evaluations. However, AIR 1.0 solves the multi-objective optimization problem as a whole, which could not result in good solution diversity and convergence on some targets. In this study, we report a decomposition-based method AIR 2.0, which is an updated version of AIR, for protein structure refinement. AIR 2.0 decomposes a multi-objective optimization problem into a number of subproblems and optimizes them simultaneously using particle swarm optimization algorithm. The solutions yielded by AIR 2.0 show better convergence and diversity compared to its previous version, which increases the possibilities of digging out better structure conformations. The experimental results on CASP13 refinement benchmark targets and blind tests in CASP 14 demonstrate the efficacy of AIR 2.0.

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A multi-objective particle swarm optimizer based on decomposition

Cited by 108 publications

References 19 publications

Creation of Design and Analysis Tools for Large Design Space Reusable Launch Vehicle Shape Optimization

Creation of Design and Analysis Tools for Large Design Space Reusable Launch Vehicle Shape Optimization

An Efficient Marine Predators Algorithm for Solving Multi-Objective Optimization Problems: Analysis and Validations

Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy

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