Multi-objective Optimization

Coello, Carlos A. Coello

doi:10.1007/978-3-319-07153-4_17-1

Cited by 11 publications

(5 citation statements)

References 165 publications

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“…Multi-objective optimisation techniques are useful in optimising problems with two or more often conflicting objective functions simultaneously. The application areas of multi-objective optimisation have triggered continuous research in meta-heuristic and multi-evolutionary optimisation techniques (Ehrgott, 2008;Jaimes et al, 2009;Coello, 2018).…”

Section: Multi-objective Optimisationmentioning

confidence: 99%

Modelling of a Continuous Veneer Drying Unit of Industrial Scale and Model-Based Anova of the Energy Efficiency

Gradov

Oluwatoki

Ohjainen³

et al. 2021

SSRN Journal

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Section: Multi-objective Optimisationmentioning

confidence: 99%

Modelling of a Continuous Veneer Drying Unit of Industrial Scale and Model-Based Anova of the Energy Efficiency

Gradov

Oluwatoki

Ohjainen³

et al. 2021

SSRN Journal

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“…The Pareto method of non-dominated solutions is one of the methods to determine the optimal solution for a set of competing objective functions [2]. Pareto optimality enables us to determine the 'trade-offs' rather than single solutions for multi-objective problems [3]. Some of the classical methods of multi-objective optimization include scalarization (weighting), hierarchical, trade-off, global criterion and goal programming methods [3].…”

Section: Theory Of Multi-optimization Techniquesmentioning

confidence: 99%

“…Pareto optimality enables us to determine the 'trade-offs' rather than single solutions for multi-objective problems [3]. Some of the classical methods of multi-objective optimization include scalarization (weighting), hierarchical, trade-off, global criterion and goal programming methods [3]. Multi-objective evolutionary algorithms (MOEAs) have been developed to advance these classical methods.…”

Section: Theory Of Multi-optimization Techniquesmentioning

confidence: 99%

Multi-objective Optimization Strategies

Mwema

Akinlabi

2020

Fused Deposition Modeling

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In this chapter, multi-objective optimization as a strategy for quality production of parts through fused deposition modelling is presented. Various techniques used in undertaking the multi-objective optimization process are described based on case studies from the literature and the authors' data. The general algorithms for multi-objective optimization of the FDM process are described. The most significant objectives of the various optimization cases are identified and described in relation to the quality of the fused deposition modelling of parts. The main objectives for optimizing fused deposition process are (i) to increase the rate of production, (ii) to reduce material wastage and utilize as minimum material as possible, (iii) save on the cost of power consumption during printing and (iv) achieve the highest quality of FDM parts. Keywords 3D printing • Fused deposition modelling • Genetic algorithms • Grey relational degree • Multi-objective optimization • Pareto • Printing parameters

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“…Pareto based methods look to iteratively improve a set of non-dominated solutions towards an optimal front. Here, evolutionary algorithms (EAs) have been widely applied for the solution of multi-objective problems [4]. In general, EAs iteratively change a set of candidate solutions towards an optimum set of values.…”

Section: Introductionmentioning

confidence: 99%

MVMOO: Mixed variable multi-objective optimisation

2021

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In many real-world problems there is often the requirement to optimise multiple conflicting objectives in an efficient manner. In such problems there can be the requirement to optimise a mixture of continuous and discrete variables. Herein, we propose a new multi-objective algorithm capable of optimising both continuous and discrete bounded variables in an efficient manner. The algorithm utilises Gaussian processes as surrogates in combination with a novel distance metric based upon Gower similarity. The MVMOO algorithm was compared to an existing mixed variable implementation of NSGA-II and random sampling for three test problems. MVMOO shows competitive performance on all proposed problems with efficient data acquisition and approximation of the Pareto fronts for the selected test problems.

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Multi-objective Optimization

Cited by 11 publications

References 165 publications

Modelling of a Continuous Veneer Drying Unit of Industrial Scale and Model-Based Anova of the Energy Efficiency

Modelling of a Continuous Veneer Drying Unit of Industrial Scale and Model-Based Anova of the Energy Efficiency

Multi-objective Optimization Strategies

MVMOO: Mixed variable multi-objective optimisation

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