Evolutionary many-objective optimization: A short review

Ishibuchi, Hisao; Tsukamoto, Noritaka; Nojima, Yusuke

doi:10.1109/cec.2008.4631121

Cited by 592 publications

(395 citation statements)

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“…Examples of such an approach were cited by Ishibuchi et al (2008). Two recent algorithms using this approach were described in (Rui et al 2013) and (Sinha et al 2010).…”

Section: An Increasing Number Of Objectives and Constraints: Scalabilmentioning

confidence: 99%

“…Ishibuchi et al (2008) distinguished two types of scalarization approaches. The first approach assigns to each solution a number of ranks according to each performance criterion against a set of scalarization functions.…”

Section: An Increasing Number Of Objectives and Constraints: Scalabilmentioning

confidence: 99%

“…The scalability of metaheuristics, especially multi-objective evolutionary algorithms, to many objectives has been treated through different approaches. The main approaches are the modification of the Pareto dominance relation, the introduction of different ranks, the use of indicator functions, the use of scalarization functions, the use of preference information, and the reduction of number of objectives (Ishibuchi et al 2008).…”

Section: An Increasing Number Of Objectives and Constraints: Scalabilmentioning

confidence: 99%

“…In this approach, every solution receives many ranks, e.g., according to each objective, and then the final rank of this solution is determined by an appropriate method, e.g., min operator. The second approach uses a single scalarization function, e.g., weighted sum, to calculate the rank of each solution (Ishibuchi et al 2008).…”

Section: An Increasing Number Of Objectives and Constraints: Scalabilmentioning

confidence: 99%

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Metaheuristics for agricultural land use optimization. A review

Memmah

Lescourret

Yao

et al. 2015

Agron. Sustain. Dev.

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Agricultural landscapes presently cover about 46 % of earth terrestrial surface. This cultivated area is decreasing, whereas the global food demand is projected to increase up to 70 % in 2050. The intensification of agriculture is not a solution to this food issue because intensive agriculture has often resulted in pollution and loss of biodiversity. On the other hand, mechanistic models with optimization algorithms can be used to design alternative land uses for sustainable agriculture. Here, we present a review of metaheuristics for land use optimization reported in 50 articles including 38 case studies carried out in 16 countries. Our main conclusions are: 1) the success of metaheuristics is problem-dependent. In general, metaheuristics enable search to escape from local optima and find a good global approximation solution.2) The choice of a given metaheuristic for solving a given problem seems to be driven by its historical use in a research team and by its popularity outside the metaheuristics research community, rather than by the characteristics of the problems to be solved and by the latest results from the metaheuristics research community. 3) Stakeholders of land use are increasingly involved at different levels of the land use optimization procedure and multi-actors decision-making methods are necessary to find trade-offs between their competing interests. 4) A future challenge is the use of parallelization techniques along with the hybridization of different metaheuristics or of metaheuristics with other optimization methods.

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“…Examples of such an approach were cited by Ishibuchi et al (2008). Two recent algorithms using this approach were described in (Rui et al 2013) and (Sinha et al 2010).…”

Section: An Increasing Number Of Objectives and Constraints: Scalabilmentioning

confidence: 99%

Section: An Increasing Number Of Objectives and Constraints: Scalabilmentioning

confidence: 99%

Section: An Increasing Number Of Objectives and Constraints: Scalabilmentioning

confidence: 99%

Section: An Increasing Number Of Objectives and Constraints: Scalabilmentioning

confidence: 99%

See 2 more Smart Citations

Metaheuristics for agricultural land use optimization. A review

Memmah

Lescourret

Yao

et al. 2015

Agron. Sustain. Dev.

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“…Nevertheless, when a large number of objectives need to be considered, the performance of these classical MOEAs tends to drop off as the complexity of the resulting optimisation problem increases. This factor has 65 led to the appearance of new specific approaches, like many-objective evolutionary algorithms, which have emerged as an effective alternative to efficiently explore highly dimensional objective spaces (Ishibuchi et al, 2008). Similarly, many-objective evolutionary algorithms operate in accordance to the precepts of the multi-objective approach.…”

mentioning

confidence: 99%

Evolutionary composition of QoS-aware web services: A many-objective perspective

Ramrez

Parejo

Romero

et al. 2017

Expert Systems with Applications

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Web service based applications often invoke services provided by thirdparties in their workflow. The Quality of Service (QoS) provided by the invoked supplier can be expressed in terms of the Service Level Agreement specifying the values contracted for particular aspects like cost or throughput, among others.In this scenario, intelligent systems can support the engineer to scrutinise the service market in order to select those candidates that best fit with the expected composition focusing on different QoS aspects. This search problem, a.k.a.QoS-aware web service composition, is characterised by the presence of many diverse QoS properties to be simultaneously optimised from a multi-objective perspective. Nevertheless, as the number of QoS properties considered during the design phase increases and a larger number of decision factors come into play, it becomes more difficult to find the most suitable candidate solutions, so more sophisticated techniques are required to explore and return diverse, competitive alternatives. With this aim, this paper explores the suitability of many-objective evolutionary algorithms for addressing the binding problem of web services on the basis of a real-world benchmark with 9 QoS properties. A complete comparative study demonstrates that these techniques, never before * Corresponding author. Tel.: +34 957 21 26 60Email addresses: aramirez@uco.es (Aurora Ramírez), japarejo@us.es (José Antonio Parejo), jrromero@uco.es (José Raúl Romero), sergiosegura@us.es (Sergio Segura), aruiz@us.es (Antonio Ruiz-Cortés) Preprint submitted to Expert Systems with ApplicationsOctober 31, 2016 applied to this problem, can achieve a better trade-off between all the QoS properties, or even promote specific QoS properties while keeping high values for the rest. In addition, this search process can be performed within a reasonable computational cost, enabling its adoption by intelligent and decision-support systems in the field of service oriented computation.

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