Approximating a multi-dimensional Pareto front for a land use management problem: A modified MOEA with an epigenetic silencing metaphor

Chikumbo, Oliver; Goodman, Erik D.; Deb, Kalyanmoy

doi:10.1109/cec.2012.6256170

Cited by 46 publications

(38 citation statements)

References 21 publications

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“…Taking into account the high number of criteria, the authors proposed a modified version of the Reference-point-based Nondominated Sorting Genetic Algorithm, R-NSGA-II, to deal with this many-objective optimization problem, i.e., with more than three objectives. For constraint handling, the proposed algorithm used an improved version of fitness sharing Pareto-ranking, which consists of subdividing the population into several subpopulations based on the similarity among individuals and emulating gene silencing which is an epigenetic regulation of gene expression (Chikumbo et al 2012). The authors analyzed their results in the form of time-series and grouped criteria into three categories (productivity, profitability, environmental impact) to visually interpret these results (Chikumbo et al 2012).…”

Section: Studies Using Evolutionary Algorithmsmentioning

confidence: 99%

“…We can only point to Chikumbo et al (2012) and Groot et al (2010). The weighted sum approach remains the most used method despite its well-known drawbacks.…”

Section: An Increasing Number Of Objectives and Constraints: Scalabilmentioning

confidence: 99%

“…Similarly, an evolutionary algorithm was used in (Chikumbo et al 2012) for a more complex strategic landuse problem, i.e., the management of a farming system involving 14 criteria and covering an area of 1500 ha composed of 315 paddocks having each one between 1 and 111 management option(s). The studied farm is located east of the Rotorua city, New Zealand, and the studied farming system was composed of dairy cattle, beef cattle, sheep, planted Monterey pine forests, protected natural forests, roads, and buildings.…”

Section: Studies Using Evolutionary Algorithmsmentioning

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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Section: Studies Using Evolutionary Algorithmsmentioning

confidence: 99%

“…We can only point to Chikumbo et al (2012) and Groot et al (2010). The weighted sum approach remains the most used method despite its well-known drawbacks.…”

Section: An Increasing Number Of Objectives and Constraints: Scalabilmentioning

confidence: 99%

Section: Studies Using Evolutionary Algorithmsmentioning

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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“…Many real-world optimization problems, such as water resource management (Reed et al, 2013), design optimization Yang and Deb, 2013), and land use management (Chikumbo, 2012;Masoomi et al, 2013), often have multiple objectives that conflict with each other. Without lose of generality, these kinds of multiobjective optimization problems (MOPs) can be described as follows: min F(x) = (f 1 (x), ..., f M (x)) T s.t.…”

Section: Introductionmentioning

confidence: 99%

Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes

Yang

Jiang

2016

Soft Comput

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It has been increasingly reported that the multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) is promising for handling multiobjective optimization problems (MOPs). MOEA/D employs scalarizing functions to convert an MOP into a number of single-objective subproblems. Among them, penalty boundary intersection (PBI) is one of the most popular decomposition approaches and has been widely adopted for dealing with MOPs. However, the original PBI uses a constant penalty value for all subproblems and has difficulties in achieving a good distribution and coverage of the Pareto front for some problems. In this paper, we investigate the influence of the penalty factor on PBI, and suggest two new penalty schemes, i.e., adaptive penalty scheme (APS) and subproblem-based penalty scheme (SPS), to enhance the spread of Pareto optimal solutions. The new penalty schemes are examined on several complex MOPs, showing that PBI with the use of them is able to provide a better approximation of the Pareto front than the original one. The SPS is further integrated into two recently developed MOEA/D variants to help balance the population diversity and convergence. Experimental results show that it can significantly enhance the algorithm's performance.

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“…In daily life and work, multi-objective optimization problems (MOPs) have a wide range of applications, such as water distribution systems [1], land use management problems [2], automotive engine calibration problems [3], and so on, which are all real-world applications of MOPs. All of these problems are needed to optimize two or more than two objectives simultaneously.…”

Section: Introductionmentioning

confidence: 99%

An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems

et al. 2017

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MOEA/D is a promising multi-objective evolutionary algorithm based on decomposition, and it has been used to solve many multi-objective optimization problems very well. However, there is a class of multi-objective problems, called many-objective optimization problems, but the original MOEA/D cannot solve them well. In this paper, an improved MOEA/D with optimal differential evolution (oDE) schemes is proposed, called MOEA/D-oDE, aiming to solve many-objective optimization problems. Compared with MOEA/D, MOEA/D-oDE has two distinguishing points. On the one hand, MOEA/D-oDE adopts a newly-introduced decomposition approach to decompose the many-objective optimization problems, which combines the advantages of the weighted sum approach and the Tchebycheff approach. On the other hand, a kind of combination mechanism for DE operators is designed for finding the best child solution so as to do the a posteriori computing. In our experimental study, six continuous test instances with 4-6 objectives comparing NSGA-II (nondominated sorting genetic algorithm II) and MOEA/D as accompanying experiments are applied. Additionally, the final results indicate that MOEA/D-oDE outperforms NSGA-II and MOEA/D in almost all cases, particularly in those problems that have complicated Pareto shapes and higher dimensional objectives, where its advantages are more obvious.

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Approximating a multi-dimensional Pareto front for a land use management problem: A modified MOEA with an epigenetic silencing metaphor

Cited by 46 publications

References 21 publications

Metaheuristics for agricultural land use optimization. A review

Metaheuristics for agricultural land use optimization. A review

Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes

An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems

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