A new multiobjective evolutionary algorithm

Sarker, Ruhul A.; Liang, Ko-Hsin; Newton, Charles S.

doi:10.1016/s0377-2217(01)00190-4

Cited by 97 publications

(23 citation statements)

References 15 publications

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“…Being a populationbased method, GAs are well suited to solve multi-objective optimization problems. A number of GA-based multi-objective optimization methods as been developed as reported in [69] among which the Vector evaluated GA (VEGA) [70], Multi-objective Genetic Algorithm (MOGA) [71], Niched Pareto Genetic Algorithm (NPGA) [72], Weight-based Genetic Algorithm (WBGA) [73], Non-dominated Sorting Genetic Algorithm (NSGA) [74], Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) [75], Multi-objective Evolutionary Algorithm (MEA) [76] are frequently used in building research. Sometimes, strategies other than the GA was used such as Multi-objective Particle Swarm Optimization (MOPSO) in optimizing thermal comfort and building energy consumption [77], Multiobjective Ant Colony Optimization (MACO) in optimizing building life cycle energy consumption [78].…”

Section: 3mentioning

confidence: 99%

A review on simulation-based optimization methods applied to building performance analysis

2014

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Recent progress in computer science and stringent requirements of the design of "greener" buildings put forwards the research and applications of simulation-based optimization methods in the building sector. This paper provides an overview on this subject, aiming at clarifying recent advances and outlining potential challenges and obstacles in building design optimization. Key discussions are focused on handling discontinuous multi-modal building optimization problems, the performance and selection of optimization algorithms, multi-objective optimization, the application of surrogate models, optimization under uncertainty and the propagation of optimization techniques into real-world design challenges. This paper also gives bibliographic information on the issues of simulation programs, optimization tools, efficiency of optimization methods, and trends in optimization studies. The review indicates that future researches should be oriented towards improving the efficiency of search techniques and approximation methods (surrogate models) for largescale building optimization problems; and reducing time and effort for such activities. Further effort is also required to quantify the robustness in optimal solutions so as to improve building performance stability.

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Section: 3mentioning

confidence: 99%

A review on simulation-based optimization methods applied to building performance analysis

2014

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“…According to Storn and Price (1997), compared to other rival approaches, the main advantages of DE include its fast convergence, the use of a small set of tuning parameters, its reduced sensitivity to the initial solution conditions and its robustness. Overall, comparisons on various benchmark problems show that DE is superior when compared to other evolutionary algorithms (Sarker et al, 2002;Sarker and Abbass, 2004). x Cw P  .…”

Section: Evolutionary Solution Techniquesmentioning

confidence: 97%

Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms

Andriosopoulos

Doumpos

Papapostolou

et al. 2013

Transportation Research Part E: Logistics and Transportation Re

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“…The distance is a measure of the well distributed Pareto front. In this study, a GA based on the Multi-Objective Evolutionary Algorithm (MOEA) [26] was used to obtain the Pareto sets for the optimized monitoring network.…”

Section: Genetic Algorithm Overviewmentioning

confidence: 99%

Optimization of a Water Quality Monitoring Network Using a Spatially Referenced Water Quality Model and a Genetic Algorithm

Puri

Borel

Vance

et al. 2017

Water

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Abstract:The monitoring network for a river system is designed to provide information about water quantity and quality. The development of Watershed Protection Plans and Total Maximum Daily Loads require systematic monitoring of waterbodies. In this study, optimum water quality monitoring networks were selected to assess E. coli loads in the Guadalupe River and San Antonio River basins. A Genetic Algorithm (GA) was applied to select monitoring stations using the mean annual E. coli flux from the Spatially Referenced Regression Model on Watershed Attributes (SPARROW). The objectives of the GA were to minimize the number of monitoring stations, include large values of the mean annual E. coli flux, and minimize uncertainty of the flux estimations. Constraints related to the monitoring of critical locations were included in a multi-objective optimization problem. The SPARROW model was applied to the optimized GA solution sets, which were compared using the objective values and statistical indices. The best GA-generated alternative set adequately represented the San Antonio River basin, in good agreement with a previous study conducted using only SPARROW. The application of the GA ensured the inclusion of the monitoring stations with large values of E. coli flux, which reflected high-risk areas within the watershed.

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A new multiobjective evolutionary algorithm

Cited by 97 publications

References 15 publications

A review on simulation-based optimization methods applied to building performance analysis

A review on simulation-based optimization methods applied to building performance analysis

Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms

Optimization of a Water Quality Monitoring Network Using a Spatially Referenced Water Quality Model and a Genetic Algorithm

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