The development of a multi-objective Tabu Search algorithm for continuous optimisation problems

Jaeggi, Daniel; Parks, GT; Kipouros, Timoleon; Clarkson, P. John

doi:10.1016/j.ejor.2006.06.048

Cited by 128 publications

(58 citation statements)

References 12 publications

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“…For nsga-ii, the crossover and mutation were chosen as blend and Gaussian [5]. List size, neighbourhood size, and movement method were set to 15, 20 and swap, respectively, as recommended by Jaeggi et al [6]. All random values were chosen from a uniform random distribution in the interval [0, 1] .…”

Section: Resultsmentioning

confidence: 99%

“…In this article the 2D airfoil design problem is used to investigate the capabilities of these different variants of the multi-objective pso (mopso) algorithm. A comparative study with the non-dominated sorting genetic algorithm (nsga-ii) [5] and multi-objective tabu search (mots) [6] is also conducted in order to verify the results. Section 2 presents a brief introduction to mopso.…”

Section: C347mentioning

confidence: 99%

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A comparison of multi-objective optimisation metaheuristics on the 2D airfoil design problem

Mirjalili

Rawlins²,

Hettenhausen³

et al. 2013

ANZIAMJ

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Variants of the multi-objective particle swarm optimisation (mopso) algorithm are investigated, mainly focusing on swarm topology, to optimise the well-known 2D airfoil design problem. The topologies used are global best, local best, wheel, and von Neumann. The results are compared to the non-dominated sorting genetic algorithm (nsga-ii) and multi-objective tabu search (mots) algorithm, and it is found that the attainment surfaces achieved by some of the mopso variants completely dominate those of nsga-ii. In general, the mopso algorithms also significantly improve diversity of solutions compared to mots. The

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Section: Resultsmentioning

confidence: 99%

Section: C347mentioning

confidence: 99%

A comparison of multi-objective optimisation metaheuristics on the 2D airfoil design problem

Mirjalili

Rawlins²,

Hettenhausen³

et al. 2013

ANZIAMJ

View full text Add to dashboard Cite

show abstract

“…The numerical optimiser used in this study is a variant of the multi-objective Tabu Search (TS) algorithm developed by Jaeggi et al [3].…”

Section: Tabu Search Optimisermentioning

confidence: 99%

“…Full details of this algorithm can be found in [3], and the optimiser parameters used in this research are summarised in table 1. …”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…In the second half of the investigation, Pareto-optimal sets of designs for the HAT and STIG cycles were produced, and a Tabu Search (TS) algorithm, developed by Jaeggi et al [3] and implemented on humid cycles by Kavanagh [4], was used to carry out a parametric optimisation of the cycles. The humidified cycle output parameters were calculated using FORTRAN code developed by Aramayo-Prudencio [5] and Kavanagh in the University of Cambridge Department of Engineering, based on code developed for standard industrial gas turbine cycles by Young and Wilcock [6], in the same department.…”

Section: Introductionmentioning

confidence: 99%

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Blade cooling optimisation in humid-air and steam-injected gas turbines

Cleeton

Kavanagh

Parks

2009

Applied Thermal Engineering

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Humidified gas turbine cycles such as the Humidified Air Turbine (HAT) and the Steam-Injected Gas Turbine (STIG) present exciting new prospects for industrial gas turbine technology, potentially offering greatly increased work outputs and cycle efficiencies at moderate costs. The availability of humidified air or steam in such cycles also presents new opportunities in blade and disk cooling architecture. Here, the blade cooling optimisation of a HAT cycle and a STIG cycle is considered, first by optimising the choice of coolant bleeds for a reference cycle, then by a full parametric optimisation of the cycle to consider a range of optimised designs. It was found that the coolant demand reductions which can be achieved in the HAT cycle using humidified or post-aftercooled coolant are compromised by the increase in the required compression work. Furthermore, full parametric optimisation showed that higher water flow-rates were required to prevent boiling within the system. This Preprint submitted to Elsevier 1 April 2009 ACCEPTED MANUSCRIPTcorresponded to higher work outputs, but lower cycle efficiencies. When optimising the choice of coolant bleeds in the STIG cycle, it was found that bleeding steam for cooling purposes reduced the steam available for power augmentation and thus compromised work output, but that this could largely be overcome by reducing the steam superheat to give useful cycle efficiency gains.

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Single‐Search‐Based Heuristics for Multiobjective Optimization

Ehrgott

2011

Wiley Encyclopedia of Operations Research and Management Science

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This article explains the single‐search‐based heuristics for multiobjective optimization. The most prominent heuristics of multiobjective simulated annealing, multiobjective tabu search, and Pareto local search are each explained with a detailed pseudocode and further summaries of algorithmic ideas. This is followed by a brief review of hybrid heuristics combining features of single search and population‐based heuristics. The article ends with brief conclusions and suggestions for further reading.

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The development of a multi-objective Tabu Search algorithm for continuous optimisation problems

Cited by 128 publications

References 12 publications

A comparison of multi-objective optimisation metaheuristics on the 2D airfoil design problem

A comparison of multi-objective optimisation metaheuristics on the 2D airfoil design problem

Blade cooling optimisation in humid-air and steam-injected gas turbines

Single‐Search‐Based Heuristics for Multiobjective Optimization

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