An overview of population-based algorithms for multi-objective optimisation

Giagkiozis, Ioannis; Purshouse, Robin C.; Fleming, Peter J.

doi:10.1080/00207721.2013.823526

Cited by 92 publications

(25 citation statements)

References 144 publications

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“…Multi-objective evolutionary algorithms (MOEAs) have come to be used widely throughout both the scienti c and engineering communities, and are typically classi ed in terms of the primary selection method used in the algorithm: Pareto-based, decomposition-based and indicator-based [3]. Most of the methods that have been developed within each class typically assume that a large budget will exist for evaluating candidate solutions as the optimization process progresses.…”

Section: Introductionmentioning

confidence: 99%

Component-level study of a decomposition-based multi-objective optimizer on a limited evaluation budget

Jones

Oakley

Purshouse

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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Component-level study of a decomposition-based multi-objective optimizer on a limited evaluation budget. In: Aquirre, H., (ed.) ABSTRACT Decomposition-based algorithms have emerged as one of the most popular classes of solvers for multi-objective optimization. Despite their popularity, a lack of guidance exists for how to con gure such algorithms for real-world problems, based on the features or contexts of those problems. One context that is important for many real-world problems is that function evaluations are expensive, and so algorithms need to be able to provide adequate convergence on a limited budget (e.g. 500 evaluations). is study contributes to emerging guidance on algorithm con guration by investigating how the convergence of the popular decomposition-based optimizer MOEA/D, over a limited budget, is a ected by choice of componentlevel con guration. Two main aspects are considered: (1) impact of sharing information; (2) impact of normalisation scheme. e empirical test framework includes detailed trajectory analysis, as well as more conventional performance indicator analysis, to help identify and explain the behaviour of the optimizer. Use of neighbours in generating new solutions is found to be highly disruptive for searching on a small budget, leading to be er convergence in some areas but far worse convergence in others. e ndings also emphasise the challenge and importance of using an appropriate normalisation scheme. CCS CONCEPTS•Computing methodologies → Optimization algorithms; • eory of computation → Evolutionary algorithms; •Applied computing → Multi-criterion optimization and decision-making; KEYWORDS MOEA/D, decomposition-based multi-objective optimization, component study, trajectory analysis ACM Reference format: Oliver P. H. Jones, Jeremy E. Oakley, and Robin C. Purshouse. 2018. Componentlevel study of a decomposition-based multi-objective optimizer on a limited evaluation budget.

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Section: Introductionmentioning

confidence: 99%

Component-level study of a decomposition-based multi-objective optimizer on a limited evaluation budget

Jones

Oakley

Purshouse

2018

Proceedings of the Genetic and Evolutionary Computation Conference

Self Cite

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“…If only the second condition is met the solution is considered weakly Pareto optimal. The multi-objective optimisation solver used aims to find a subset of Pareto optimal solutions which is referred to as the Pareto front (Giagkiozis et al 2015).…”

Section: Multi-objective Optimisation To Optimise Prior Parametersmentioning

confidence: 99%

“…In order to perform this multi-objective optimisation a Pareto-based method was chosen since the relative importance of the objectives is unclear (Giagkiozis, Purshouse & Fleming 2015) and a controlled elitist genetic algorithm (Deb 2001) (a variant of NSGA-II (Deb, Pratap, Agarwal & Meyarivan 2002)) was used.…”

Section: Multi-objective Optimisation To Optimise Prior Parametersmentioning

confidence: 99%

Constructing informative Bayesian map priors: A multi-objective optimisation approach applied to indoor occupancy grid mapping

Georgiou

Anderson

Dodd

2017

The International Journal of Robotics Research

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The problem of Simultaneous Localisation And Mapping (SLAM) has been addressed in numerous ways with different approaches aiming to produce faster, more robust solutions that yield consistent maps. This focus, however, has resulted in a number of solutions that perform poorly in challenging real life scenarios. In order to achieve improved performance and map quality this paper proposes a novel method to construct informative Bayesian mapping priors through a multi-objective optimisation of prior map design variables defined using a source of prior information. This concept is explored for 2D occupancy grid SLAM, constructing such priors by extracting structural information from architectural drawings and identifying optimised prior values to assign to detected walls and empty space. Using the proposed method a contextual optimised prior can be constructed. This prior is found to yield better quantitative and qualitative performance than the commonly used non-informative prior, yielding an increase of over 20% in the F2 metric. This is achieved without adding to the computational complexity of the SLAM algorithm, making it a good fit for time critical real life applications such as search and rescue missions.

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“…In the past two decades, population-based optimization has attracted great attention from both academia and industry in many fields not limited in system science [54][55][56][57][58][59][60][61][62]. Recently, a new population-based metaheuristics, labeled as the Teaching-learning-based optimization (TLBO), has been proposed [63][64][65][66][67] as an alternative to genetic algorithm (GA) [68], particle swarm optimization (PSO) [69,70] and Differential Evolution (DE) [71] for continuous optimization problems.…”

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confidence: 99%

Optimal targeting of nonlinear chaotic systems using a novel evolutionary computing strategy

Wang¹,

Lyu

et al. 2016

Knowledge-Based Systems

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Control of chaotic systems to given targets is a subject of substantial and well-developed research issue in nonlinear science, which can be formulated as a class of multi-modal constrained numerical optimization problem with multi-dimensional decision variables. This investigation elucidates the feasibility of applying a novel population-based metaheuristics labelled here as Teaching-learning-based optimization to direct the orbits of discrete chaotic dynamical systems towards the desired target region. Several consecutive control steps of small bounded perturbations are made in the Teaching-learning-based optimization strategy to direct the chaotic series towards the optimal neighborhood of the desired target rapidly, where a conventional controller is effective for chaos control. Working with the dynamics of the well-known Hénon as well as Ushio discrete chaotic systems, we assess the effectiveness and efficiency of the Teaching-learning-based optimization based optimal control technique, meanwhile the impacts of the core parameters on performances are also discussed.Furthermore, possible engineering applications of directing chaotic orbits are discussed. Keywords:Chaos control; chaotic dynamics; optimization; computational intelligence;Teaching-learning-based optimization Highlights► Control of chaotic systems is formulated as constrained optimization problem.► Teaching-learning-based optimization is to direct the chaotic series.► The efficacy of TLBO based optimal control technique have been demonstrated.

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An overview of population-based algorithms for multi-objective optimisation

Cited by 92 publications

References 144 publications

Component-level study of a decomposition-based multi-objective optimizer on a limited evaluation budget

Component-level study of a decomposition-based multi-objective optimizer on a limited evaluation budget

Constructing informative Bayesian map priors: A multi-objective optimisation approach applied to indoor occupancy grid mapping

Optimal targeting of nonlinear chaotic systems using a novel evolutionary computing strategy

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