The Pareto-Following Variation Operator as an alternative approximation model

Talukder, A.K.M. Khaled Ahsan; Kirley, Michael; Buyya, Rajkumar

doi:10.1109/cec.2009.4982924

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…o Genetic Algorithms (GA) [14,15,16,17,18] o Differential Evolution (DE) [19,20,21] o Memetic Algorithm (MA) [22,23,24,25] o Cultural Algorithm (CA) [26,27,28] o Particle Swarm Optimization (PSO) [29,30,31,32] o Artificial Bee Colony (ABC) [33,34,35,36,37] o Tabu Search (TS) [38,39,40,41,42,43] o Local Search (LS) [44,45] o Beam Search (BS) [46,47,48] o Simulated Annealing (SA) [49,50,51,52] o Artificial Neural Network (ANN) [14,53,54,55,56,57] o Radial Basis Function Neural Networks (RBFNN) [55] o Augmented Neural Networks (AugNN)…”

Section: A Algorithms Reviewmentioning

confidence: 99%

“…Papers [4,6,14,15,24,32,35,38,39,41,44,48,52] concern problems defined in terms of work shop scheduling, in different variants. Research papers [5,20,25,54,58] deal with flow shop problems. Papes [ 10,11,18,61,62,45] concern problems related to human resource management and scheduling of work teams.…”

Section: A Algorithms Reviewmentioning

confidence: 99%

“…4. A subtype of evolutionary strategies is the differential evolution (DE) algorithm [19,20,21]. The characteristic feature of this algorithm is the creation of new individuals (solutions) by combining several individuals from the population.…”

Section: Figure 3 Scheme Of the Genetic Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Task Scheduling–Review of Algorithms and Analysis of Potential Use in a Biological Wastewater Treatment Plant

Ujazdowski

Piotrowski

2022

IEEE Access

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The idea of task scheduling is to increase the efficiency of a system by minimising wasted time, evenly loading machines, or maximising the throughput of machines. Moreover, the use of appropriate scheduling algorithms often leads to a reduction in the energy costs of the process. Task scheduling problems are found in a variety of industrial areas, and their scale changes significantly depending on the problem. This review shows the extent to which task scheduling methods are used in industry. This paper presents methods and algorithms for solving task scheduling problems. In addition, an analysis of the possibility of using task scheduling methods to improve the efficiency of wastewater treatment plants was also conducted. This approach is based on the assumption of a balanced workload for multiple SBR reactors. Analysing the case of a wastewater treatment plant in Swarzewo City in northern Poland. The problem of scheduling tasks in a wastewater treatment plant has not been so far subject of research work.

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Section: A Algorithms Reviewmentioning

confidence: 99%

Section: A Algorithms Reviewmentioning

confidence: 99%

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Task Scheduling–Review of Algorithms and Analysis of Potential Use in a Biological Wastewater Treatment Plant

Ujazdowski

Piotrowski

2022

IEEE Access

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“…Both of them have been used with MOEAs [139], but their use in real-world applications is still scarce [128], mainly because practitioners are either not aware of them or do not trust their reliability in highly nonlinear search spaces [35]. It is also worth remarking that several other approaches exist for improving the efficiency of a MOEA, but most of them remain unused in real-world applications [1], [166]. 4) Efficient Constraint-Handling Techniques: Most of the applications reviewed in this paper dealt with problems subject to constraints.…”

Section: Future Research Pathsmentioning

confidence: 99%

Multiobjective Evolutionary Algorithms in Aeronautical and Aerospace Engineering

Arias-Montaño

Coello

Mezura-Montes

2012

IEEE Trans. Evol. Computat.

133

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Nowadays, the solution of multiobjective optimization problems in aeronautical and aerospace engineering has become a standard practice. These two fields offer highly complex search spaces with different sources of difficulty, which are amenable to the use of alternative search techniques such as metaheuristics, since they require little domain information to operate. From the several metaheuristics available, multiobjective evolutionary algorithms (MOEAs) have become particularly popular, mainly because of their availability, ease of use, and flexibility. This paper presents a taxonomy and a comprehensive review of applications of MOEAs in aeronautical and aerospace design problems. The review includes both the characteristics of the specific MOEA adopted in each case, as well as the features of the problems being solved with them. The advantages and disadvantages of each type of approach are also briefly addressed. We also provide a set of general guidelines for using and designing MOEAs for aeronautical and aerospace engineering problems. In the final part of the paper, we provide some potential paths for future research, which we consider promising within this area.

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“…Therefore, the EA community have proposed several ways to improve the efficiency of their existing approaches. Some of the major developments to achieve this goal are: hybrid algorithms [4]- [9], informed operators [10]- [14] and the use of surrogate models [15]- [19].…”

Section: Introductionmentioning

confidence: 99%

A hybrid local search operator for multiobjective optimization

Diaz-Manríquez¹,

Toscano-Pulido²,

Landa-Becerra³

2013

2013 IEEE Congress on Evolutionary Computation

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In recent years, the development of hybrid approaches to solve multiobjective optimization problems has become an important trend in the evolutionary computation community. Despite hybrid approaches of mathematical programming techniques with multiobjective evolutionary algorithms are not very popular, when both fields are successfully coupled, results are impressive. However, the main objective of this sort of hybridization relays on the needing of several executions of the mathematical approach in order to obtain a sample of the Pareto front, raising with this, the number of fitness function evaluations. However, the use of surrogate models has become a recurrent approach to diminish the number of function evaluations.In this work, a hybrid operator that transforms the original multiobjective problem into a set of modified goal programming models is proposed. Furthermore, a local surrogate model is used instead of the real function in the hybrid operator. The goal programming model with the surrogate is optimized by a direct search method. Additionally, a standalone algorithm that uses the hybrid operator is here proposed. The new algorithm is validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed operator gives rise to an effective algorithm, which produces results that are competitive with respect to those obtained by two well-known multiobjective evolutionary algorithms.

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The Pareto-Following Variation Operator as an alternative approximation model

Cited by 9 publications

References 29 publications

Task Scheduling–Review of Algorithms and Analysis of Potential Use in a Biological Wastewater Treatment Plant

Task Scheduling–Review of Algorithms and Analysis of Potential Use in a Biological Wastewater Treatment Plant

Multiobjective Evolutionary Algorithms in Aeronautical and Aerospace Engineering

A hybrid local search operator for multiobjective optimization

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